The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements. There is a lack of standardisation in slide microscopy imaging data. Here the authors report Slim, an open-source, web-based slide microscopy viewer implementing the Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a range of existing medical imaging systems. Unfortunately, sharing of imaging data and reporting of imaging findings are lagging with detrimental effects on the reproducibility of microscopy imaging science5 and on the clinical adoption of computational pathology6. Given their immense size, slide microscopy imaging data sets generally exceed the capacity of local storage and are hence stored on remote servers and accessed over a network. A critical requirement for making data findable, accessible, interoperable, and reusable (FAIR) and for enabling AI is the availability of standardized metadata and standard application programming interfaces (APIs)12. Web-based slide microscopy viewers, which access imaging data via web APIs, have become key for human-machine collaboration in biomedical imaging data science and computational pathology because they enable human experts to browse through and visually analyze large image datasets for hypothesis generation and testing, to select and annotate images for the development and validation of ML models, and to review and visually interpret image analysis results upon ML model inference15. However, existing web-based slide microscopy viewers are either monolithic or heavily rely on custom interfaces, which tightly couple client and server components and thereby impede interoperability between image analysis, management, and viewing systems. The dependence on custom data formats and metadata elements further introduces interdependencies between analysis and visualization or annotation, requiring both ML models and viewers to customize data retrieval and decoding or data encoding and storage for each system they consume data from or produce data for, respectively18. It would thus be desirable for analysis tools and viewers to rely on standard web APIs and data formats to become agnostic to internal implementation details of image management systems, to enable the visualization of image analysis results generated by a broad range of ML models or other computational tools, and to allow for the generation of machine-readable image annotations that can be readily consumed and interpreted by ML models and reused across projects without the need for extensive data engineering.Slide microscopy imaging data acquired in biomedical research and clinical pathology practice are increasing in size, dimensionality, and complexity19, catering to the healthcare needs of billions of people on a daily basis. DICOM is often considered a radiology standard, but it has been widely adopted across medical domains including dermatology, ophthalmology, and endoscopy for a variety of imaging modalities and finds broad application in preclinical and clinical research23. In recent years, the standard has been further developed to support slide microscopy25, quantitative imaging22, and machine learning27, and is being adopted internationally for storage, management, and exchange of slide microscopy in diagnostic pathology28 as well as in research and development30. While DICOM has been primarily designed for medical imaging in clinical practice and includes established mechanisms for ensuring high image quality required for diagnostic purposes, the standard also has several advantages for biomedical imaging research26. Notably, DICOM specifies profiles for data de-identification that facilitate the use of clinically acquired imaging data sets for research purposes32 and defines digital objects for encoding and communicating image annotations and analysis results27. Furthermore, there is an abundance of open-source software libraries and tools that support DICOM33.In this paper we argue that the existing Digital Imaging and Communications in Medicine (DICOM) standard is well suited to address the challenges articulated above and we present a software implementation that demonstrates the practical utility of using the DICOM standard for quantitative tissue imaging in biomedical research as well as in digital and computational pathology. Specifically, we present the web-based viewer and annotation tool Slim, which realizes FAIR principles and enables standard-based imaging data science by leveraging the DICOM standard for management and communication of digital images and related information. DICOM is the internationally accepted standard for medical imaging29. As part of the Cancer Research Data Commons (CRDC), the IDC provides researchers a scalable, cloud-hosted imaging data repository and tools for searching, sharing, analyzing, and visualizing cancer imaging data. Slim serves as the slide microscopy viewer of the IDC and has been tasked to support a wide range of slide microscopy imaging use cases from traditional brightfield microscopy of hematoxylin and eosin (H&E) stained tissue to highly multiplexed tissue imaging using sequential immunofluorescence microscopy on a common, modality-agnostic DICOM-centric cloud infrastructure that is shared with the radiology viewer34.The National Cancer Institute’s Imaging Data Commons (IDC) is a DICOM-based research platform for visual exploration and computational analysis of biomedical images in the cloud that aims to integrate heterogeneous imaging data from multiple modalities and diverse projects to facilitate integrative, multi-modal imaging data science35, the Clinical Proteomic Tumor Analysis Consortium (CPTAC)36, and the Human Tumor Atlas Network (HTAN)1 projects. In addition, we demonstrate the ability of Slim to interoperate with commercial digital pathology systems that support DICOM and present a pathway for streamlining the translation of novel image-based methods and tools from research into clinical practice.In this paper, we showcase Slim’s visualization and annotation capabilities for quantitative tissue imaging research and machine learning model development in the context of the IDC using several public IDC collections of The Cancer Genome Atlas (TCGA)37 to query, retrieve, or store data  or Vendor Neutral Archives (VNAs) installed at healthcare enterprises around the world41, and cloud services offered by several major public cloud providers  profiles for color management. An ICC profile specifies a transformation for mapping pixel data from a source color space into a target color space and a rendering intent to handle potential gamut mismatches between source and target color spaces by mapping saturated, out-of-gamut colors in the target color space into the gamut. The ICC color management workflow makes use of two ICC profiles to retain color fidelity: (i) an input device profile for mapping stored pixel values from the RGB color space used by the scanner for image acquisition into a device-independent CIEXYZ or CIELAB color space called the profile connection space (PCS) and (ii) a display device profile for mapping PCS pixel values into the RGB color space used by the viewer for image display 47.Color is of critical importance for microscopy imaging, yet accurate and reproducible display of colors is complicated by the fact that different image acquisition and display devices may represent colors in device-specific RGB color spaces with different gamuts, i.e., ranges of chromaticities of the red, green, and blue primaries. The sRGB color space has evolved as the standard color space for the web and is assumed by most web browsers. However, medical imaging devices generally use a device-specific RGB color space that accommodates a wider gamut and allows for more exact digital representation of colors. Display of color images acquired by medical imaging devices via a web browser thus requires controlled conversion of colors between different color spaceslay Fig. 46. DICOM48. Second, grayscale images are often pseudocolored, which can aid in visual interpretation, but can also sometimes mislead human readers49. To facilitate display of fluorescence microscopy images, Slim implements the DICOM VOI LUT Transformation and the DICOM Advanced Blending Transformation, which parametrize the conversion of grayscale images with various bit depths into 8-bit pseudocolor images for display on ordinary color monitors and the additive blending of two or more pseudocolor images into a false color image, respectively. To this end, the viewer first applies a value of interest (VOI) lookup table (LUT) to clip and rescale stored pixel values of each grayscale image into a normalized range — a process that is referred to as windowing — and then subsequently applies a palette color LUT to map the rescaled grayscale values to RGB values in the range from 0 to 255  and specimen preparation  are generally needed for unambiguous interpretation of the pixel data25. Slim obtains metadata of DICOM image objects via DICOMweb without requiring additional, out-of-band connections and displays pertinent identifying and descriptive metadata via the graphical user interface alongside the pixel data  is that DICOM image objects not only include the pixel data and pixel-related structural metadata , but also identifying and descriptive metadata about the patient, the imaging study, the image acquisition equipment, and the imaged tissue specimens, including information about specimen preparation procedures , which allow human readers to interpret the pixel data in context14. Visualizing channels of a highly multiplexed tissue imaging data set may easily overwhelm a human reader14 and selecting appropriate colors is a challenging subject in itself52. To lower the barrier for scientists and to facilitate the interpretation of complex multi-dimensional imaging data, Slim allows for automatic and persistent configuration of display settings using DICOM presentation states. A DICOM Presentation State (PR) object makes it possible to reference a set of images and define how the referenced images should be presented to human readers in a device-independent manner. DICOM PR objects can be used to persist display settings as data alongside the DICOM image objects and communicate display settings between input and display devices. Slim uses DICOM Advanced Blending Presentation State instances to pre-define different combinations of immunofluorescence imaging channels and to rescale and colorize each channel such that an optimal contrast is achieved for visualization of specific anatomical or morphologically abnormal structures, such as tumor cells and different subtypes of tumor infiltrating lymphocytes , for example to generate a ground-truth for training or validation of ML models. The viewer provides various annotation tools for drawing, labeling, and measuring ROIs using DICOM Structured Reporting (SR)OIs Fig. a. Slim sOIs Fig. b. DICOM nes Fig. b. Each qnes Fig. b. Each mnes Fig. c. Beforenes Fig. c or an ines Fig. d. When a49. Slim automatically applies provided LUTs to transform the grayscale images and displays a colorbar along with the generated pseudocolor images to aid the reader in interpreting the data. If no palette color LUT is included in an image object, Slim choses a default colormap. In the case of binary or fractional segmentation images, pixels encode class probabilities, which by definition map to real world values in the range from 0 to 1, and Slim thus uses a sequential colormap based on the viridis palette to pseudocolor pixel data of segmentation images by default if no palette color LUT is embedded in the image object. In the case of parametric map images, pixels may encode a variety of quantities, and the relationship between stored values and real world values is not implied but needs to be communicated explicitly. For example, pixel values may be encoded and stored as 16-bit unsigned integers to allow for efficient lossless image compression , but the stored values may represent real world values in the range from −1 to 1, which should be displayed using a diverging colormap. DICOM Parametric Map objects include a real world value LUT to map stored values into real world values. When Slim transforms the pixel data using VOI and palette color LUTs to render pixels as 8-bit color values, it also maps the stored values of interest to the corresponding real world values using the real world value LUT  and does not impose any constraints on how data is stored by server applications. Therefore, it would in principle be possible to expose data that is stored server side in OME format (either in the form OME-TIFF files66 or OME-NGFF objects73) as DICOMweb resources, by transcoding them on-the-fly as long as the metadata that is required for DICOMweb resources is available and compatible. We recognize the importance of OME in the research setting and are convinced that DICOM and OME can and should coexist. Therefore, we are closely collaborating with the OME community and are actively contributing to the development of OME and the harmonization of image metadata between DICOM and OME11 to maximize interoperability between different devices and tools.While the digital pathology community has been focusing on the development and adoption of the DICOM standard, the microscopy research community has been investing into the Open Microscopy Environment (OME)25. To address this issue, several open-source tools are available to convert a variety of proprietary formats into standard DICOM format75. Importantly, enterprise medical imaging workflows nowadays evolve around DICOMweb and are increasingly moving from on premises into cloud76. Accordingly, major public cloud providers have started to offer cloud-based DICOMweb services , which offer scalable storage, management, and communication of imaging data in DICOM format as a service and have the potential to fundamentally reshape data governance at healthcare enterprises and research institutes around the world. Slim is able to interoperate with existing DICOMweb services in the cloud and supports OIDC-based authentication and authorization.There are a growing number of commercially available slide scanners from different manufacturers  that can output brightfield as well as fluorescence microscopy image data directly in DICOM format. Despite the increasing adoption of DICOM in digital pathology, several slide scanners unfortunately continue to output data in proprietary formats, and therefore currently DICOM conversion may be required29. It is worth noting that we had no control over the design or implementation of the Google product and developed Slim independently in a local development environment using different open-source DICOMweb servers. The fact that Slim nevertheless achieves interoperability with the Cloud Healthcare API is due to the fact that both systems conform to the standard DICOMweb API specification and serves as a testament to the power of standardization. Through participation in multiple DICOM Connectathons, Slim has further repeatedly demonstrated its ability to interoperate with many other image storage and communication systems from different manufacturers. By relying on the internationally accepted standard for medical imaging, Slim enables scientists to leverage existing medical-grade infrastructure and a wealth of existing software tools33 for quantitative tissue imaging research and allows for visualization of images, image annotations, and image analysis results independent of the device that is used to store and manage the data as well as the device that acquired or generated the data . To the best of our knowledge, Slim is the first and currently only open-source web viewer that enables the use of DICOMweb services for interactive visualization and annotation of brightfield and fluorescence slide microscopy images as well as of derived image analysis results. While other web viewers were described in the literature to have the ability to display slide microscopy images stored in DICOM files77, these viewers still rely on custom network APIs for querying and retrieving image data from a server over the web and therefore remain tightly coupled to specific server implementations. Furthermore, they depend on the browser’s JavaScript API for client-side decoding and rendering of image tiles and therefore require a specialized tile server to transcode the stored pixel data into a simple representation and format  that can be directly decoded and rendered by web browsers. In contrast, Slim can be connected with any server that exposes a DICOMweb API and, by handling the decoding, transformation, and rendering of image tiles client side in a browser-independent manner, enables the server to send the pixel data as it is stored.In the case of the IDC, Slim interacts with the DICOMweb services provided by the Cloud Healthcare API of the Google Cloud PlatformA number of design decisions in Slim reflect the current state of client-server network connections and computing capacities of the systems available to our target users. First, while networks are efficient at bulk data transfers, round-trip latencies for repeated rendering requests tend to be less efficient than local computation when the client machine has the power to perform the calculations. Second, modern client machines are generally equipped with powerful processors and hosting servers with similar compute resources is expensive. Third, performing compute- and memory-intensive operations on the server may work well for a few users but is difficult and costly to scale to a large number of users. In the face of these considerations, Slim performs all image decoding, transformation, and rendering operations client side in the browser, thereby placing low functional requirements on DICOMweb servers and increasing the likelihood of achieving interoperability with different servers. Client-side processing further has the advantages that the total computational load is distributed across clients, leveraging each client’s processing units, and that pixel data need to be retrieved only once per client but can subsequently be repeatedly transformed locally without additional server round trips to enable highly interactive visualization and a smooth viewing experience. To maximize the performance of client-side processing, Slim utilizes state-of-the-art web technologies. Specifically, all image decoding, transformation, and rendering operations are either implemented in C/C++, compiled to WebAssembly, and run in parallel on the central processing unit (CPU) using multiple web workers, or implemented in OpenGL Shading Language (GLSL) and run in parallel on the graphical processing unit (GPU) using WebGL. The use of WebAssembly not only improves performance, but also allows Slim to reuse well established C/C++ libraries for decoding compressed pixel data and color management without having to rely on a particular browser’s implementation. This is important, because some slide scanners make use of advanced image compression algorithms and color space transformations  that can better preserve image quality but are not widely used on the web and are hence not well supported by all browsers. By providing its own browser-independent decoders and transformers, Slim facilitates consistent image quality across different browsers.26. Each institution or even department may use a distinct set of devices for acquisition, storage, or management of imaging data and realization of ML in the real world thus hinges on the ability of ML models to programmatically find, access, interoperate, and (re)use data78. Slim can support ML workflows through collection of image annotations from expert readers that can be readily used as ground truth for model training and validation and through display of computational image analysis results to expert readers upon model inference. Since Slim stores image annotations and retrieves image analysis results in DICOM format over DICOMweb, it facilitates the decoupling of ML models and viewers26 and the integration of outputs from different ML models.Achieving interoperability through standardized metadata and standard APIs is important for image visualization and annotation, but maybe even more so for the development of ML models using heterogenous clinically acquired imaging data from different institutions as well as for performing ML model inference and integrating inference results into clinical workflows at different institutionshttps://github.com/ImagingDataCommons/slim.In conclusion, DICOM is a practical standard for quantitative tissue imaging and Slim is an interoperable web-based slide microscopy viewer and annotation tool that implements the standard and opens new avenues for standard-based biomedical imaging data science and computational pathology. The viewer application and its underlying libraries are freely available under a permissive open-source software license: Slim’s user interface (UI) is designed for slide microscopy workflows in digital and computational pathology and consists of three core components: i. the Worklist component, which lists available imaging studies (cases) and allows users to dynamically filter and select studies for display; ii. the Case Viewer component, which displays metadata about the patient and study, lists the slides that are contained within the study (case), and displays the overview image of each slide; and iii. the Slide Viewer component, which displays volume and label images of the currently selected slide as well as descriptive metadata of the imaged specimen(s) as well as the image acquisition equipment and context. The Case Viewer component searches for slide microscopy images within a selected DICOM Study and groups matched images into digital slides. A digital slide hereby constitutes the set of images that were acquired of a tissue specimen within the same image acquisition context. The images of a given slide may include multiple channels or focal planes and generally correspond to one DICOM Series. Upon selection of a slide, Slim routes the user to the Slide Viewer component, which retrieves the metadata for all image instances of the selected slide and creates the main viewport for the display of the volume images, which contain the tissue on the slide, a separate viewport for the display of the associated label image, and panels for display of relevant specimen, equipment, and optical path metadata that are shared amongst the images. The Slide Viewer component also automatically searches for available image annotations and analysis results that share the same frame of reference as the volume images and makes the data available for display to the user. Alternatively to this user flow, a user or a device can also directly navigate to a particular case or slide using a study- or series-specific unique resource locator (URL) that routes the application to the Case Viewer or Slide Viewer, respectively.25. Metadata obtained as Search Transaction Resources (DICOM Part 18 Section 10.6.3.3) and Retrieve Transaction Metadata Resources (DICOM Part.18 Section 10.4.1.1.2) are retrieved in JavaScript Object Notation (JSON) format using media type “application/dicom+json”. The JSON documents are structured according to the DICOM JSON Model (DICOM Part 18 Chapter F). Individual image frames obtained as Retrieve Transaction Pixel Data Resources (DICOM Part 18 Section 10.4.1.1.6) are retrieved either uncompressed using media type “application/octet-stream” or JPEG, JPEG 2000, or JPEG-LS compressed using media types “image/jpeg”, “image/jp2” and “image/jpx”, or “image/jls”, respectively. Other bulkdata  are retrieved uncompressed using media type “application/octet-stream”.All data communication between Slim and origin servers is performed using standard transactions of the DICOMweb Study Service. The transactions are also known as RESTful services (RS). Specifically, the application queries the server for available DICOM studies, series, or instances using the DICOMweb Search Transaction (QIDO-RS), fetches instance metadata and bulkdata  using the DICOMweb Retrieve Transaction (WADO-RS), and stores created annotation instances using the DICOMweb Store Transaction (STOW-RS)25 to interact with DICOMweb servers over HyperText Transfer Protocol (HTTP), to manage DICOM datasets in computer memory, and to decode, transform, and render images into viewport HyperText Markup Language (HTML) elements, respectively. For rendering raster and vector graphics in the viewport, the dicom-microscopy-viewer library internally uses the OpenLayers JavaScript library, a well-established and well-tested library for building geographic mapping applications.Slim is implemented as a single-page application in TypeScript using the React framework. The application has a modular architecture and viewer components are implemented as independent, reusable React components. Complex low-level operations are abstracted into separate JavaScript libraries, which are maintained and developed by the authors in collaboration with open-source community members. Specifically, the Slim application relies on the dicomweb-client, dcmjs, and dicom-microscopy-viewer JavaScript librariesSlim is aware of the multi-scale pyramid representation of tiled whole slide images, which it determines from the structural metadata of DICOM VL Whole Slide Microscopy Image instances. Based on the image metadata, the viewer precomputes the tile coordinates and then dynamically retrieves the necessary image frames from the server using the Frame Pixel Data resource of the DICOMweb Retrieve Transaction (DICOM Part 18 Section 10) when the user zooms or pans and then subsequently decodes, transforms, and renders each frame in the viewport for display. The viewer supports both the TILED_FULL and TILED_SPARSE dimension organization type and additionally supports concatenations, where an individual large image instance may be split into several smaller instances that are stored and communicated separately. Of note, the viewer expresses the intent to retrieve the frame pixel data in their original transfer syntax by specifying a wide range of acceptable media types via the accept header field of DICOMweb request messages.Image frames may be served by the DICOMweb server in a variety of image media types and Slim decodes the pixel data client-side using WebAssembly decoders. The operations for decoding of JPEG, JPEG-2000, JPEG-LS compressed image frames are implemented in C++ using the established libjpeg-turbo, OpenJPEG, and CharLS C/C++ libraries, respectively. The C++ implementations are compiled into WebAssembly using the Emscripten C++ library and exposed via a JavaScript API. The decoding operations are performed in the browser in parallel using multiple Web Worker threads/processes.Slim implements various DICOM pixel transformation sequences (DICOM Part 4 Chapter N). The operations of the DICOM Profile Connection Space Transformation for true color images are implemented in C++ using the Little-CMS C library, compiled into WebAssembly using the Emscripten C++ library, and run on the CPU immediately after the decoding operations in the same Web Worker thread/process for maximal memory efficiency. The operations of the DICOM VOI LUT Transformation and Advanced Blending Transformation for grayscale and pseudocolor images are implemented as graphic shaders in the OpenGL Shading Language (GLSL) and are run on the GPU using WebGL. All pixel data transformations are performed in memory using 32-bit floating-point value representation.22. Individual user-drawn ROI annotations are included in the report document content using SR template TID 1400 “Planar ROI Measurements and Qualitative Evaluations”. The SR templates are implemented in JavaScript in the dcmjs JavaScript library.The Slim annotation tools enable users to draw, label, and measure regions of interest (ROI) on displayed images using DICOM Structured Reporting (SR). The points of user-drawn vector graphic geometries are recorded as 3D spatial coordinates (SCOORD3D) in the reference coordinate system, which are independent of pixel matrices of specific image instances and apply to all resolution levels of the multi-resolution pyramid. Slim supports the following graphic types defined by the standard for the SCOORD3D graphic type (DICOM Part 3 Chapter C): POINT, POLYLINE, POLYGON, ELLIPSE, and ELLIPSOID. Users can save their annotations as DICOM Comprehensive 3D SR documents, which are structured according to SR template TID 1500 “Measurement Report”When the user draws onto the images using the annotation tools, the viewer internally tracks and renders the graphic data as 32-bit floating-point values in the form of two-dimensional coordinates defined relative to the total pixel matrix of the highest resolution image. When the user stores the data in a Comprehensive 3D SR document, the viewer maps the two-dimensional image coordinates at sub-pixel resolution into three-dimensional slide coordinates at millimeter resolution as required by SCOORD3D graphic types. To this end, the viewer constructs an affine transformation matrix from image metadata using equations defined in the standard (DICOM Part 3 Chapter C) and performs the necessary translation, rotation, and scaling of coordinates using a single matrix multiplication. The metadata of the highest resolution image are used to keep potential inaccuracies due to floating-point arithmetic at a minimum.When Slim routes to the Slide Viewer component, it also searches for existing image annotations and analysis results in the form of i. DICOM Comprehensive 3D SR instances structured according to template TID 1500 “Measurement Report” containing spatial coordinates, qualitative evaluations, and measurements for a small number of ROIs ; ii. DICOM Microscopy Bulk Simple Annotation instances containing spatial coordinates and measurements for annotation groups with a potentially very large number of ROIs ; iii. DICOM Segmentation instances containing binary or probabilistic segmentation masks; and iv. DICOM Parametric Map instances containing saliency, activation, or attention maps or other heatmaps. The viewer then parses the objects and creates a UI component for each discovered annotation , annotation group (item of the Annotation Group Sequence attribute), segment (item of the Segment Description Sequence), and parameter mapping in the side panel.Segmentation and Parametric Map images are grayscale images and their pixel data are transformed analogously to the pixel data of grayscale slide microscopy images (see above).To facilitate overlay of SCOORD3D graphic data onto the image pixel data, the viewer maps the three-dimensional slide coordinates to two-dimensional image coordinates, using the highest resolution image as reference. To this end, the viewer constructs an affine transformation matrix from the metadata of the highest resolution image, which represents the inverse of the transformation described above in “Generation and storage of user-drawn image annotations”. The affine transformation matrix is constructed only once and then cached to avoid repeated matrix inversion and thereby speed up the mapping of coordinates into the total pixel matrix.Slim utilizes the OpenID Connect (OIDC) protocol for authenticating users and authorizing the application to access secured DICOMweb services based on the OAuth 2.0 protocol. By default, Slim uses the OAuth 2.0 Authorization Code grant type with Proof Key for Code Exchange (PKCE) challenge. In case this grant type is not supported by an authorization server, Slim can be configured to use the legacy Implicit grant type instead. To enable authentication and authorization, Slim needs to be configured with the URL of the identity provider, the OIDC client ID, and a set of OIDC scopes. Slim will then authenticate users with the identity provider and obtain personal information about the user such as their name and email address. This information will automatically be incorporated into stored DICOM SR documents along with image annotations generated by the user to enable downstream applications to identify the person that made the observations. Slim will further request a token from the authorization server to access the configured DICOMweb endpoint and will include the obtained access token into the header of DICOMweb request messages.https://imaging.datacommons.cancer.gov. Images obtained by the IDC were originally stored as TIFF files, structured based on either the proprietary SVS format (i) or the Open Microscopy Environment (OME) format (ii), and were converted into standard DICOM format for inclusion into the IDC using the PixelMed Java toolkit70. In brief, pixel data were copied from the original TIFF files into DICOM files, carefully avoiding lossy image re-compression, and structural image metadata  as well as information about the microscope  were copied from TIFF fields into the corresponding DICOM data elements. Additional clinical and biospecimen metadata were obtained from different sources in a variety of formats and were harmonized to populate the appropriate DICOM data elements. Specifically, available information about the specimen preparation  were described via the DICOM Specimen Preparation Sequence attribute using DICOM SR template TID 8001 “Specimen Preparation”. Further details on the conversion process are provided at https://github.com/ImagingDataCommons/idc-wsi-conversion. Additional collections of DICOM slide microscopy images were used, which were directly stored in DICOM format by commercially available slide scanners. The DICOM files of these images are publicly available via the NEMA File Transfer Protocol (FTP) server at ftp://medical.nema.org.To demonstrate the display capabilities of Slim, several public DICOM imaging collections of the IDC were used: i. brightfield microscopy images of hematoxylin and eosin (H&E) stained tissue sections from The Tumor Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects; ii. fluorescence microscopy images of tissue sections stained with DAPI or Hoechst and multiple fluorescently-labeled antibodies via cyclic immunofluorescence imaging (CyCIF) from the Human Tissue Atlas Network (HTAN) project. The DICOM files of these images are publicly available via the IDC portal at To demonstrate the annotation capabilities of the viewer, regions of interest (ROIs) were manually demarcated in brightfield and fluorescence microscopy images and anatomic or morphologically abnormal structures were described using SNOMED CT codes. Since Slim is used in the IDC for cancer research, the focus was placed on the classification of tumors and related tumor findings, such as the associated morphology and topography. To this end, a Slim configuration file was created that included the SNOMED CT codes for tumor morphology and topography pertinent to the tumor types of TCGA and CPTAC collections (79 was obtained from The Cancer Imaging Archive (TCIA) and converted from custom CSV format into standard DICOM format using the highdicom Python library26. Contours of detected nuclei and associated nuclear size measurements were encoded in the form of DICOM Microscopy Bulk Simple Annotations objects. Additionally, semantic segmentation masks were derived from the nuclei contours and encoded in the form of DICOM Segmentation objects. To facilitate display of these DICOM objects together with the source slide microscopy images in Slim, references to the source DICOM VL Whole Slide Microscopy Image objects, which were obtained from the IDC, were included into the derived DICOM objects. In addition, all patient, study, and specimen metadata were copied from the source into the derived image objects to enable unique identification and matching. The DICOM Microscopy Bulk Simple Annotations and Segmentation objects are in the process of being included into the IDC and will soon become available via the IDC portal, too.To demonstrate the features of the viewer for visualization and interpretation of image analysis results, existing, publicly available data sets were used. Specifically, a public collection of pan cancer nuclei segmentation results26. For each slide, three presentation states were created for the following three groups of staining targets (compounds investigated) that were used in the majority of imaging studies: 1. DNA, CD45, Pan-Cytokeratin, Vimentin; 2. CD3, CD68, CD20; 3. CD45, E-cadherin, PD1. For each digital slide, the corresponding grayscale images for the given stain were selected based on the coded description of the specimen staining process, which was included in the image metadata (see Imaging data sets above for details), and an optimal value of interest range was determined using the autominerva tool14. The DICOM Advanced Blending Presentation State objects have been included in the IDC and are available via the IDC portal.DICOM Advanced Blending Presentation State objects, which parametrize the display of highly multiplexed immunofluorescence microscopy images from the HTAN project, were generated using the highdicom Python library40, which is a free and open-source Java application that can be readily installed and deployed on different operating systems and machines.Slim is a single-page application that can be served from a generic static web server and configured to interact with one or more DICOMweb servers. For development and production use of Slim, we have primarily relied on two DICOMweb server implementations: the Google Cloud Healthcare API, which is a public cloud service of the Google Cloud Platform (GCP), and the DCM4CHE ArchiveFor local development, an NGINX web server was used to serve the Slim single-page application as a static website and the DCM4CHE Archive was used as DICOMweb server. Both servers were installed into Linux containers and deployed on localhost using the Docker container runtime. Example Dockerfile and docker-compose.yml configuration files are included in the GitHub repository along with the source code (29. For development and testing, additional Slim instances were hosted on GCP using the Google Identify and Application Management service as an identity provider and authorization server. To enable access to the protected DICOMweb services directly without the use of a reverse proxy, OAuth 2.0 Client ID credentials were created and Slim was configure to use these credentials to authenticate users and to obtain access tokens for access of the secured DICOMweb endpoints of the Google Cloud Healthcare API.For development of Slim within the context of the IDC and for production use of Slim via the IDC portal, Google Firebase was used as a static web server and the Google Cloud Healthcare API was used as DICOMweb server. For IDC production use, a reverse proxy server was used, because the Google Cloud Healthcare API requires authorization to access DICOMweb endpoints. The reverse proxy redirects DICOMweb request messages from the viewer to the Google Cloud Healthcare API while imposing limits on the aggregate and IP-specific traffic in order to place a cap on network egress and DICOM operation chargesFurther information on research design is available in the Supplementary informationReporting SummaryPeer Review FileDescription of Additional Supplementary FilesSupplementary software


Molecular biomarkers in hepatobiliary and pancreatic cancers: implications of non-coding RNAs and its therapeutic opportunities” aims to identify potential novel biomarkers at different levels of diagnosis, prognosis and therapy for hepatobiliary and pancreatic cancers.Hepatobiliary and pancreatic cancers, including hepatocellular carcinoma (HCC), gallbladder cancer, cholangiocarcinoma (CCA) and pancreatic ductal adenocarcinoma (PDAC) are lethal malignancies worldwide . DespiteLi C. et al., identified five independent predictors associated with the prognosis of pancreatic cancer patients namely fibrinogen to pre-albumin ratio (FPR), pre-operative controlling nutritional status (CONUT), carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), and TNM stage and constructed a nomogram using these factors for clinical benefit assessment. It was observed that among all the five independent prognostic factors, FPR had comparable prognostic accuracy as well as significantly associated with overall survival of patients with resectable pancreatic cancer . Using decision curve analysis (DCA) and time dependent area under the curve (AUC) analysis, the authors also identified that the nomogram had a better predictive and discriminative ability than the conventional TNM staging system and could potentially serve as a useful tool for prognosis prediction and clinical management of the disease.In this Research Topic, Zhang et al., showed that PDAC patients with liver metastasis exhibited poor prognosis compared to PDAC patients with lung metastasis. The authors constructed a nomogram model using seven factors including age at diagnosis, location of the tumor, tumor grade, tumor stage, surgical status, presence of metastases in multiple organs as well as treatment with chemotherapy to investigate its ability to predict the prognosis of patients with distant metastases (DM). The authors confirmed that this nomogram can precisely predict the prognosis of PDAC patients with DM in internal and external validation datasets by calculating the ROC curves, C-index, calibration curves and DCA. This will help clinicians to analyze the prognosis of PDAC patients with DM and implement personalized treatment strategies.In pancreatic cancers, most of the patients are diagnosed at advanced stages with local or distant metastases. Therefore, evaluating prognostic outcome of patients based on the site(s) of metastasis may aid in risk assessment and clinical decision-making. Using the data from the Surveillance, Epidemology and End Results (SEER) database as well as an independent cohort, Liu et al., elucidated the characteristics of tumor microenvironment such as infiltration of immune cells, proportion of cancer associated fibroblasts (CAFs) and stemness of tumor cells. The authors performed RNA sequencing from 10 pancreatic cancer patients and identified differentially expressed genes (DEGs) such as BHLHE40, ITGA3, ITGA2, and ADAM9. Furthermore, the authors developed a risk model prediction model based on these four genes and validated their performance to predict the prognosis of pancreatic cancer patients. In addition, the authors identified that the overexpression of BHLHE40 was associated with neoplastic progression by promoting epithelial-mesenchymal transition (EMT) and stemness phenotype. Taken together, this study identified that an increased expression of BHLHE40 is associated with poor prognosis and can be used as a potential therapeutic target in pancreatic cancer. In another study, Chen et al., identified eight necroptosis related genes using publicly available single cell RNA sequencing and transcriptomic datasets and developed a prognostic signature model, namely NCPTS. The authors further classified the patients into high and low risk based on the median risk score value generated based on LASSO regression analysis. It was observed that pancreatic cancer patients with high NCPTS risk score exhibited poor prognosis, low immune infiltration levels and an increased tumor mutational burden (TMB), compared to the NCPTS_low group. Furthermore, using a series of in vitro experiments and pancreatic cancer tissue specimens, the authors validated the functional role of one of the key necroptosis related genes, namely ESP8, and identified its role in tumor progression.In another interesting work, Li J. et al., the authors reviewed the existing literature and summarized the miRNA-associated mechanisms in regulating cell death, mitochondrial function and metabolic reprogramming in HCC. The authors have also explored the clinical usefulness of miRNAs as molecular biomarkers and their prospect for therapeutic applications in HCC.The present Research Topic also entails a review article summarizing the interactions between micro RNAs (miRNAs), a type of non-coding RNA and the tumor microenvironment in hepatocellular carcinoma (HCC). The altered expression profiles of miRNAs have been reported to have diagnostic, prognostic, and therapeutic potentials and can be used as molecular biomarkers . In thisIn summary this Research Topic represents several important and diverse contribution in the field of pancreatic cancer featuring an immune nutritional biomarker, the FPR, the study of prognostic outcomes based on the site of metastasis, BHLHE40 as a molecular biomarker for neoplastic progression and prognosis prediction, and a necroptosis-related gene signature for predicting prognosis. Finally, this Research Topic includes a review article summarizing the regulatory roles of miRNAs in the pathogenesis of HCC and their utility for clinical applications.SR drafted the editorial. MM and CX edited and revised the editorial. All authors reviewed and approved the final version.


This review article aims to systematically identify the relationship between sports drinks and healthy behavior. This systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline criteria, and eligibility criteria were established using the PICOS tool  from about 1000 records of sports drinks articles identified in the various Web of Science Core Collection databases. The literature review stages determined a reduced set of 15 articles relating these drinkable supplements to healthy behavior. This study concludes that water consumption should be emphasized for non-athletes, sports drinks should be labeled to indicate water consumption and carry a warning label, and more randomized clinical trials should be considered to ensure conclusive results for health decision making. Sports drinks are non-carbonated flavored liquids containing added sugars, minerals, and electrolytes to help replenish the body during strenuous exercise . Some reIn this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ,40,41 weAccording to the current checklist of the PRISMA guidelines , the folA set of articles was used as a homogeneous citation base, avoiding the impossibility of comparing indexing databases that use different calculation bases to determine journals’ impact factors and quartiles ,46,47,48The selection of articles was specified based on eligibility criteria: the target population (participants), the interventions , the elements of comparison of these studies, the outcomes of these studies, and the study designs  scale was used for the assessment of the risk of bias among the included studies. The MMAT scale is a valid measure of the methodological quality of the article. Two authors independently conducted the studies, and a third author was recruited in case of any argument.Mixed Methods Appraisal Tool (MMAT), a checklist used in systematic reviews based on synthesis of qualitative and quantitative evidence, includes criteria for the evaluation of mixed studies; it defines the study category, and 7 items are applied according to a score from zero to one, to obtain a final percentage mean. Studies are considered as high quality > 75%, moderate quality 50–74%, and low quality < 49%. Studies with values below 75% were excluded from the category analysis and discussion [The search vector extracted a total of 991 records without repetitions from seven different databases of the Web of Science Core Collection  from 1984 to 2023. Excluding records according to the PICOS tool see , non-artUsing the PRISMA guidelines, fifteen articles were selected . Table 2Then, the studies included in this review have been classified according to their main topics in three categories, as shown in Thus, the articles analyzed mention three specific types of healthy behaviors related to sports drinks consumption: healthy oral–dental behavior, healthy eating behavior (overweight/obesity), and healthy eating behavior . The results are detailed below.Four categorized studies focus on children ,57,60,61Also, it is important to note that the only randomized clinical trial among all those selected belongs to this category . This tyThe categorized studies focus on adolescents, which conclude the following: (a) the existence of racial–ethnic disparities in their consumption, to the detriment of Latino and African American children, is also related to this healthy behavior category ; (b) theThese results present, in addition to the two previous categories, only two studies different from those considered above: (a) one regarding closed campus policies and their effects on reducing consumption of sugar-sweetened beverages , although students who are accustomed to purchasing beverages may switch from off-campus to on-campus alternatives, ensuring healthy school food environments is relevant ; (b) anoThe systematic review presented in this manuscript examines 991 scientific articles from seven databases of the Web of Science Core Collection on healthy behaviors associated with the consumption of energy drinks, performing a screening that results in 15 articles being reviewed in depth, which allows an initial categorization into three groups that are healthy oral–dental behavior, healthy eating behavior (overweight/obesity) and healthy eating behavior , the latter being discarded because it does not allow to see significant differences with the category referred to overweight/obesity.Regarding the first group, healthy oral–dental behavior, this systematic review coincides with other previous reviews regarding the association between oral–dental health and sports drinks ,65,66,67Dental erosion caused by the ingestion of sports drinks and other foods has increased and cannot be ignored by clinical dental practice ,10. AlthDuring intense activities, other reviews recommend ingesting fluids with a carbohydrate concentration of 5–10% and up to 10–12% to reduce the total amount of fluids consumed and maintain a correct hydration level . Other sAs a contribution of our work, in the relationship between the consumption of sports drinks and oral–dental health, we highlight the incorporation of the concept of “healthy oral–dental behavior”, given that previous reviews ,65,66,67On the other hand, regarding our results identifying a relationship between healthy eating behavior in overweight and obese conditions and the use of sports drinks, there is an agreement with previous systematic reviews ,80,81,82In addition, sports drinks have high levels of sodium, which can lead to health problems such as high blood pressure and heart disease ,12. TherPrevious reviews ,80,81,82One limitation of this study is the use of seven databases from the Web of Science Core Collection (WoS), whose data have been added as Finally, to strengthen healthy eating behaviors and provide relevant information on the potential harms of sports drinks in the non-athlete population ,83, moreThe evidence presented in this systematic review suggests emphasizing water consumption in non-athletes to prevent the harmful effects of sports drink consumption on oral–dental health and to prevent dehydration and heat stroke.On the other hand, our findings suggest to business decision makers that their marketing campaigns for this type of food should indicate the risks of its consumption, and we propose that these products improve their composition and nutritional contribution. Furthermore, public policymakers should focus more on the consequences of the consumption behavior of sports drinks to prevent and remedy the consequences on oral–dental health and overweight/obesity.Finally, we believe unique randomized clinical trials would mobilize research leaders and funding decision makers to increase efforts and incentives to expand studies of this type.


Here, we present an expanded utility of the R package qgg for genetic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes Bayesian linear regression modeling procedures, which provide a unified framework for mapping of genetic variants, estimation of heritability and genomic prediction from either individual level data or from genome-wide association study summary data. With this release, the qgg package now provides a wealth of the commonly used methods in analysis of complex traits and diseases, without the need to switch between software and data formats.https://github.com/psoerensen/qgg). Notes on the implemented statistical genetic models, tutorials and example scripts are available at our GitHub page https://psoerensen.github.io/qgg/.The methodologies are implemented in the publicly available R software package, qgg, using fast and memory efficient algorithms in C++ and is available on CRAN or as a developer version at our GitHub page ( With the increasing availability of large genetic biobanks and a simultaneous increase in number of collected samples by biobanks, there is a demand for software tools that allow for robust and efficient genomic analyses. Tools such as PLINK , and direct fast and memory-efficient processing of genetic data without the need to re-format the binary PLINK files  constructing PGS with utilities in genomic medicine, (ii) fine mapping of GWAS results and (iii) accurate estimation of quantitative genetic parameters.btad656_Supplementary_DataClick here for additional data file.


Background: A ‘Health in All Policies’ (HiAP) approach has been widely advocated as a way to involve multiple government sectors in addressing health inequalities, but implementation attempts have not always produced the expected results. Explaining how HiAP-style collaborations have been governed may offer insights into how to improve population health and reduce health inequalities. Methods: Theoretically focused systematic review. Synthesis of evidence from evaluative studies into a causal logic model.Results: Thirty-one publications based on 40 case studies from nine high-income countries were included. Intersectoral collaborations for population health and equity were multi-component and multi-dimensional with collaborative activity spanning policy, strategy, service design and service delivery. Governance of intersectoral collaboration included structural and relational components. Both internal and external legitimacy and credibility delivered collaborative power, which in turn enabled intersectoral collaboration. Internal legitimacy was driven by multiple structural elements and processes. Many of these were instrumental in developing (often-fragile) relational trust. Internal credibility was supported by multi-level collaborations that were adequately resourced and shared power. External legitimacy and credibility was created through meaningful community engagement, leadership that championed collaborations and the identification of ‘win-win’ strategies. External factors such as economic shocks and short political cycles reduced collaborative power.Conclusion: This novel review, using systems thinking and causal loop representations, offers insights into how collaborations can generate internal and external legitimacy and credibility. This offers promise for future collaborative activity for population health and equity; it presents a clearer picture of what structural and relational components and dynamics collaborative partners can focus on when planning and implementing HiAP initiatives. The limits of the literature base, however, does not make it possible to identify if or how this might deliver improved population health or health equity. This involves working to ensure that all policy areas start to recognise their role in health, to value health and wellbeing, and contribute to enhancing population health.19 While challenges persist on tracking HiAP action to health and health equity outcomes, there is a large but disparate evidence base describing, assessing, theorising and evaluating a multitude of HiAP-approach initiatives. This body of research suggests a complex intersection of critical contextual factors can promote or impede the initiation and implementation of healthy public policies and intersectoral working.23 Implementation factors include the political prioritisation (or not) of HiAP,24 formal implementation processes, the availability of resources and capacity building activities.20 In addition, interprofessional trust and meaningful stakeholder and community engagement have been identified as important to the implementation of HiAP.27 This troubling backdrop does not appear to have undermined enthusiasm for ‘whole government’ or collaborative approaches to achieving healthier public policy, with evidence of ongoing commitments across global, national and local policy levels.28 To enable positive collaborative processes and to give them a specific form and function, tools, most notably health impact assessments (HIAs) and health equity impact assessments, have been identified as assisting the implementation of HiAP. HIA is a practical approach to judge the health effects of policy. They are used as a way of informing decision makers and stakeholders about the health harms and benefits of specific policy interventions. They are an aid to decision making and central to some HiAP-style initiatives. Some of the focused literature seeking to understand how HIAs function in practice suggests that the way in which such tools are used and, therefore, their role in supporting meaningful intersectoral collaboration to achieve health outcomes, can also be limited by wider politico-administrative factors.29 The processes of partnership working for health have also been scrutinised. Corbin and colleagues’ scoping review of what makes intersectoral partnerships for health promotion work, identified nine core elements of partnership processes that could inform best practice: (1) develop a shared mission aligned to the partners’ individual or institutional goals; (2) include a broad range of participation from diverse partners and a balance of human and financial resources; (3) incorporate leadership that inspires trust, confidence and inclusiveness; (4) monitor how communication is perceived by partners and adjust accordingly; (5) balance formal and informal roles/structures depending upon mission; (6) build trust between partners from the beginning and for the duration of the partnership; (7) ensure balance between maintenance  and production (activities that deliver on objectives); (8) consider the impact of political, economic, cultural, social and organizational contexts; and, (9) evaluate partnerships for continuous improvement.32 One of the most recent reviews reports that the available literature often finds a disappointing gap between HiAP expectations and outcomes, noting that much of this literature constitutes ‘policy analysis (identifying policy problems and solutions) rather than policy theory (explaining policy-making dynamics).’30 Our scoping research reached a similar conclusion and informed a decision to drop one of our original questions exploring what the existing literature suggests about the consequences of intersectoral collaboration for outcomes in policy, practice and health . In sum, the growing research field of intersectoral collaboration for health in general and of HiAP in particular have resulted in several efforts to review and synthesise the burgeoning literature, with a view to identifying the lessons for policy.i) the components and dynamic interactions involved in intersectoral collaboration to improve population health outcomes in policy settings; (ii) plausible theories of change connecting intersectoral collaboration to population health outcomes (which might inform future research focusing on outcomes); and (iii) insights into sustaining successful intersectoral collaborations for population health over time within policy settings. To aid our analysis, we draw on two key concepts: policy governance and systems thinking. The concept of governance has been variously defined33 but we use it to focus our attention on analysing the bureaucratic processes, rules , structures and relationships that existing evidence suggests are important for understanding intersectoral collaboration for population health. Then, informed by systems thinking, we focus on trying to identify any non-linear, unpredictable dynamics that have been identified as important for the creation and operation of intersectoral (often multi-level) policy systems for population health. To aid this part of our analysis, we draw on our findings to develop a causal loop diagram of intersectoral collaboration for population health. Building on these observations, our review specifically focuses on identifying:  and processes (dynamics). We synthesise this aspect of our analysis visually, in a causal loop diagram. Although this work is not yet sufficient for understanding the consequences of different approaches to intersectoral collaboration for population health, by surfacing some of the pathways through which collaborations function in their distinct settings, and identifying theories of change within existing literature, we believe our review provides an important first step in shifting the focus of research from processes to outcomes. The review protocol is registered on PROSPERO36 (reference number CRD42019138779). We draw on literature on how intersectoral collaboration for health/equity is constructed and operates in practice. Although much of this literature specifically uses the HiAP concept, we wanted to ensure that we did not exclude literature that did not employ this term but nonetheless involved intersectoral collaboration with the intention of achieving population health outcomes. It incorporates a theoretical focus by working to identify and synthesise theories of change that link intersectoral collaboration to population health outcomes. As outlined above, it is also informed by two key concepts: policy governance and systems. The search was conducted across eight databases: Medline, Embase, Social Policy and Practice, Web of Science, IBSS, ASSIA + Sociology Abstracts and the Sociology Database (PROQUEST Sociology Collection), Scopus and PROQUEST dissertations and theses. Specific and controlled vocabulary  were used in the search. Examples of search terms for specific databases are included in  All primary studies that had an evaluative component were included as case studies. Further selection criteria were:National, regional, local intentional collaborations between two or more government policy areas , with or without other sectoral  collaborations; Explicit population health or health equity target outcomes intended to prevent inequities in health before they become clinically identifiable ; Empirical evidence on the component parts and dynamics of intersectoral collaboration. within one area (sector) of government eg, collaborations between different professionals within the same sector only, such as healthcare practitioners and public health professionals; Collaborations Collaborations between a single policy area (sector) and research/academic institutions; Collaborations between policy and community organisations only ; Collaborations to improve access to healthcare only; Commercial/private sector and public sector collaborations  and legal/medical partnerships; Collaborations in humanitarian emergencies; Papers that identified the components of collaboration but did not provide insight into how those component parts interrelated; Studies based within lower- or middle-income countries; Studies of collaboration between global/international agencies ; Secondary studies including evidence syntheses and systematic reviews; Commentaries, book reviews, protocols, opinions, editorials; Papers not in English; Papers prior to 2000.  Application of these criteria revealed three main categories of research and evaluation: (1) empirical evaluative papers; (2) empirical descriptive papers and (3) theoretical papers. For the purposes of this analysis, only papers that empirically evaluated the success (or otherwise) of intersectoral collaboration for health equity were included. The rationale for this decision was that only evaluative papers empirically demonstrated the characteristics and dynamics of collaboration, and several also engaged with pathways along a complex causal chain in a specific context.20 definition: Case studies were identified using Shankardass and colleagues’An intersectoral initiative toward healthy public policy making, where sectors collaborate by developing policies, programmes and projects that include interventions addressing health upstream of inequities in healthcare utilization” (2015: 467). “37 Screening by title and abstract was undertaken by one reviewer with uncertain items referred to a second researcher. Screening at the full text phases was shared between three reviewers with uncertainties about inclusion/exclusion agreed by consensus. Two data extraction templates were devised, applied, refined and then implemented. The first was constructed in EPPI Reviewer and captured evidence of the state of the field, the components of intersectoral collaboration, collaborative dynamics and the outcomes of collaboration. Collaborative outcomes were extracted in terms of their reported success (or otherwise). The second extraction template was designed in MS Word and focussed on theories of change including inputs, mechanisms and outcomes. Extraction templates are provided in  Papers were screened, downloaded and data were extracted using a template designed and tested in the software EPPI-Reviewer 4.38 This technique was sufficiently flexible to allow the inclusion of a broad range of studies.39 No papers were excluded on the basis of quality as long as it could be discerned how the research was conducted (the methods used) and with whom (the case study). As such, the review followed the principles of others41 whereby exclusion is kept to a minimum in order to ensure fine-grained detail and potential insight was not missed. This was done in the recognition of the inherent subjectivity of the process of quality assessment in largely qualitative evidence syntheses.42 This also aligns with the conception of the review as seeking to identify the component parts of intersectoral collaborations for population health and to understand the dynamic relationships within this. Insight into processes and dynamics was, however, contingent on the depth of the analysis (or their richness or thickness43) in the papers. This represents a limitation of the review. The critical assessment of papers was informed by Dixon Woods and appraisal prompts.44 Theories of change were derived from data extracted in the MS Word template that detailed programme inputs, strategies, functions, mechanisms of action along causal chains and outcomes. Each paper was identified as a case study and guided in synthesis by the steps described by Hoon45 on meta-synthesis of case studies. In doing so, emphasis was placed on analysing each case study independently, then as a collection to identify strong and emergent themes across causal pathways with the objective of developing a causal loop diagram and accompanying narrative. Our process included: (i) identifying and collating variables in the extraction tables and mapping these onto an early stage diagram, (ii) interrogating the reported interconnection of factors in the extraction tables to connect variables on the emergent diagram, (iii) using extracted data to identify the polarity of interconnections. The process of identifying ‘higher order’ or systems level causal mechanisms across the dataset was an inductive process of interpreting repeated patterns in the evidence base of how policy level collaborations were able to effect change. An example of this method was operationalised is outlined in  Data were analysed and synthesised using a critical realist-informed perspective. This helped us identify some potential causal mechanisms driving the social phenomena of collaboration. Thirty-one publications based on 40 case studies from nine high-income countries were included in the review. The sifting process is identified in  Of the 31 publications included, 14 specifically focused on HiAP, using the concept explicitly to describe the approach of the policy intervention. Of those, eight were case studies of South Australia, indicating the dominance of HiAP in that context. The remaining 17 publications described intersectoral collaboration for population health and/or health equity more generally, employing a range of terms. For the remainder of this results section and where possible, we will use HiAP specifically where studies used this term. Reflecting the findings of other reviews, we identified only a limited evidence base assessing the effectiveness and processes of intersectoral collaborations for health and equity, which employed a narrow range of methods. Table outlines key characteristics of studies.46 workshops,48 focus groups49 and analysing routine administrative data,52 progress reports53 and policy tracking forms.54 While it is notable that over half of the case studies included the United States or Australia, many other case study locations were represented, particularly in Europe. South Australia was particularly dominant, with seven out of the eight Australian studies located there. Retrospective evaluations using only qualitative methods were common (10/31); as were mixed methods designs (20/31), including stakeholder interviews (20/20), documentary review (16/20), surveys (4/20) and other methods such as stakeholder forums,55 Two multiple case studies explored HiAP initiatives across policy levels.56 Collaborations for health frequently included a whole systems focus (14/31) and a wide range of policy partners were identified across the papers including planning (7/31), transport and travel (6/31), economic/urban regeneration (4/31) and housing (3/31). Less frequent but also appearing in the case studies were partnerships with the police and social services,57 leisure and recreation58 and immigration.59 Reinforcing the complexity of many of the case studies, intersectoral collaborations included not only the public sector but also voluntary and community sectors (8/31) and sometimes the private sector (2/31). The level of collaboration was overwhelmingly local  (16/31) and regional  (11/31) with one national collaboration initiated and then implemented at the local level. A broad range of collaborative activities was identified across the studies , highliga priori understandings translated into practice. Multiple dimensions of collaboration, or the ways in which collaboration was manifested in the activities of collaborating partners, were explored . The ext62 and joined-up government.60 Theoretical frameworks that would help explain the process and outcomes of collaboration were few. ‘Systems thinking’ or a complex systems approach was adopted in five cases,56 Kingdon’s ‘multiple streams’ theory twice and social ecological theory twice.54 Fifteen papers made no reference to a theoretical framework for interpretation and analysis. A range of concepts were employed to make sense of collaborative arrangements. The concept of partnerships or partnership working was most frequently applied (10/31), followed by collaborative governance64 the creation of new requirements or commitments for local governments to report on HiAP,65 the appointment of additional, sometimes boundary spanning, employees,66 the development of more tailored intersectoral projects58 and the creation of additional on-line supportive networks66 or decision support tools.55 The design of the case studies allowed only for largely qualitative outcome measures that mostly focussed on intermediate, process outcomes relating to the perceived success (or otherwise) of the collaboration itself. Examples of such outcomes included the increased references to health in strategic documents,61 but these judgements were based on divergent ‘success’ criteria. In 23 cases, activities that indicated partial success in collaboration were highlighted. One study noted that collaboration had been largely unsuccessful67 and in the case of three papers, it was unclear or unknown if the collaboration itself had been successful.68 Given these mixed findings, it is unsurprising that 21/25 papers did not identify if the collaborations had impacted population health or health equity, in line with the findings of other recent reviews.30 Two studies concluded that health equity outcomes had largely not been achieved.70 Five papers reported partial outcomes, with logic provided about how measured outcomes were consistent with steps along a causal pathway to reduced inequities.71 Programme-level outcomes reporting was partial or ambiguous in most cases. Only four evaluations identified that collaborative outcomes had been largely achieved We sought to elucidate the causal pathways through which health and other sectors collaborate to address population health and equity via the body of included case studies. The diversity of dimensions explored  and dive The collaborative governance loop represents the components of intersectoral collaboration and their interconnections. It shows the variables contributing to collaboration, linked by arrows. Links connected with a + (plus polarity symbol) show that variables move in the same direction, for example, the presence of a common language and understanding leads to more relational trust. Links with a – (minus polarity symbol) indicate the opposite, for example, the occurrence of personnel and organisational churn leads to less relational trust. Loops indicated with an R are reinforcing loops. An example in this causal loop diagram is the relationship between capacity building and the creation of a common language: more of one, means more of the other and so on. A variety of factors act recursively; feeding back to amplify or dampen aspects of collaboration along the causal chain. Components of the pathway to collaboration are structural (boxes without a border) or relational (shaded in grey). Structural governance components are those elements of the system intentionally put in place to govern collaborative effort. These include resources, such as time and money, as well as structural inputs, such as training, decision support tools, processes for documenting action and establishing decision-making structures. Relational governance components are the processes of the relationships between people as professionals, as representatives of organisations or institutions, as politicians or as citizens in the collaborative nexus. Some factors blend relational and structural aspects. For example, senior ‘championing’ leadership includes both the ways in which collaborations put in place a leader within a structure of governance but also the ways senior leaders relate to others across collaborations to enable the functioning of the collaborative system. There are also political dimensions to collaboration identified in  Internal and external credibility  and internal and external legitimacy (the acceptance of the collaboration as an authority) are the changes anticipated to happen through lower level collaborative effort. They are the ‘higher order’ change mechanisms that are both internal to and external of the collaborations themselves. This finding is significant in that the meta-synthesis reveals that legitimacy and credibility both internal to collaborations and in the recognition they are given externally are important. These mechanisms coalesce in intersectoral collaborations for health and health equity in that they may deliver the collaborative power necessary to enable change to take effect. The notion of collaborative power is introduced to represent uncertainty about the extent to which the presence or absence of these factors (credibility and legitimacy) translate to intersectoral collaborations for health or health equity. It is not considered inevitable that credible and legitimate collaborations are capable or able to exercise the power necessary to lead to intersectoral change. Indeed, power was disputed across the case studies and fluctuated over time.72 The scale and level of advancement of the South Australia partnership was unusual across the studies; it had a central mandate, was backed by legislation and strategy and had high level political support.65 As Delaney et al noted, however, these structural supports did not ensure implementation success, with a series of factors, such as resource-constraint and personnel/leadership churn, undermining the initiative.48 The strongest features of the evidence base on structural governance components and processes included: the role of collaborative working structures, strategies and leadership and the requirement for adequate human and financial resources and time to effect long-term change. Eighteen papers reported that formal structures were put in place to encourage collaboration between participating partners. In addition, formal leadership structures were embedded across the interventions, although these varied in form, scale and contributing partners. In South Australia, for example, several studies noted that a dedicated HiAP Unit had been created alongside the implementation of a joint governance structure that created an ‘authorising environment’ for intersectoral action.69 Carlisle described Social Inclusion Partnerships in Scotland that were organised around committee-style management board meetings.68 A wide range of working group, advisory group and partnership structures were evident across included studies.69 This variety, as well as the often multiple governance structures put in place for HiAP initiatives, make conclusions about optimal or most appropriate structures difficult to establish. In their assessment of two Canadian cancer prevention partnership projects , Politis et al noted that broad, inclusive formal structures were important facilitators to intersectoral collaboration but that these required time to find ‘common ground’ and identify shared objectives.55 Holt et al, in a study of HiAP in 10 Danish municipalities, cautioned that the two most common governance structures designed to transcend organisational boundaries – the central unit and the intersectoral committee – reproduced existing organisational problems, particularly reinforcing silos.60 They noted an example of a ‘matrix’ style intersectoral committee in one municipality that showed promise. This combined strategy, policy and implementation level partners in a long-term (10-year) structure that enabled strong, embedded relationships to develop over time.60 These findings highlight two things: first, that collaborations should be designed to span across and between levels of governance and, second, structures in themselves may be less important than the long-term relationships they enable to develop since it is these relationships that appear key for collaborative legitimacy and credibility. Other evaluations described intersectoral administrative health committees  that included steering committee members, public health departments and a series of ‘intersectoral working groups’ concerned with different populations including children and young people, at-risk populations and ill and debilitated people.72 Such high profile leadership positively affected the perceived internal and external credibility and legitimacy of the collaboration.70 Characteristics of perceived effective collaborative leadership were commonly described as: ‘committed,’ ‘strong’ and ‘with decision making powers.’68 In a health and housing context, where housing were the driving partners, Haigh et al identified that leaders required seniority to be perceived as effective: “They have some power , they understand the system well, often have pre-existing relationships that they can utilise and are in a position to influence the implementation of recommendations.”46 However, Gase et al caution that different professions within a collaboration may have contrasting expectations about policy leadership. If these expectations are not met, then the legitimacy of leadership can be eroded which, in turn, has the potential to undermine collaborative efforts.50 Plochg et al highlighted that narrow, self-selected leadership was damaging to collaboration,62 suggesting that, like collaborative structures, strong collaborative leadership should focus on how structural decisions can create, reinforce or damage the relationships collaborative partners are seeking to achieve. The value of mutlidisciplinarity, for example, was highlighted as a way to enhance collaborations through the development of a common language and trust between partners.73 Morteruel et al explicitly noted that the participation of people with different profiles  were seen as enhancing the quality of planning-related HIAs.74 Two studies used multiple cases to demonstrate that a common language was positively related to the adoption and implementation of ‘win-win’ strategies56; factors promoting the internal and external credibility of collaborations. There was consistent evidence of formal leadership appointments across collaborations (16/25). These tended to include health leaders, senior system leaders (in policy and delivery) and high profile ‘champions.’74 Studies also pointed to some potentially problematic elements of political leadership,71 with Holt et al identifying the importance of politicians ‘leav[ing] space’ for the professional judgements of others around the implementation of interventions.69 This suggests a careful balance is required. As Haigh et al note, in the context of local political settings, politicians can play a useful role as advocates throughout the collaborative process46; in Baum and colleagues’ terms they are powerful ‘norm entrepreneurs’ who can increase the chance of the institutionalisation of HiAP.47 Conversely, if political priorities shifted, such as in the cases of South Australia and Spain during healthcare budgetary pressure and economic downturn, then a failure to advocate or prioritise politically can reduce collaborative power.74 Where a political component of leadership was considered in studies, there was evidence that political leadership and ‘drive’ were important for instigating and sustaining the prioritisation of HiAP interventions.74 Perceived complicated and bureaucratic delays in implementing tools could, however, undermine HiAP initiatives.75 A mitigating strategy of embedding milestones into the process of assessment could reduce this kind of ‘HiAP fatigue.’75 Tools that supported intersectoral collaboration were used in 17 cases, most commonly HIAs. These were reported to support internal credibility of collaborations when assessments were documented and scientific knowledge was applied. Relational factors refer to those elements of collaboration that provide people opportunities to form and maintain relationships across sectors. It is notable that the diagram in 74 Practices supporting this included early sharing of work,59 having one-to-one meetings63 and frequent contact with collaborators.66 This enabled an understanding of other sectors and the creation of a common language between partners67 which further reinforced relationships. Lawless et al describe this as part of a deliberate and necessary ‘engage step’ in the collaborative process that facilitated social learning between partners.59 Mathias and Harris-Roxas identified that creating opportunities for health and urban design sectors to come together in the context of recognised linked agendas positively impacted partners’ feeling of ‘being in it together.’66 This ethos of mutuality or reciprocity was commonly identified across the studies and was positively linked to the determination of intersectoral trust, collaborative legitimacy and credibility.66 Trust emerged as a critical mechanism to external credibility and internal legitimacy across a broad range of contexts. In a child health network in rural Canada, McPherson et al identified that through the 13-year life of the Network, trust was developed through processes of exposure to and respect for networked collaborators, good communication between parties, interdependent working and positive peer influence.61 They described this as creating a ‘collective responsiveness’ that enabled on-going collaboration and reflected that trusting relationships had become deeply embedded within the Network.61 The internal legitimacy of collaborations was causally connected to partners’ access to opportunities for relationship building. There was strong evidence that creating and accessing opportunities to build relationships facilitated the development of trust between sectors and aided collaborative legitimacy and credibility.60 or as part of broader public sector change/reform programme71 or as an outcome of political change was problematic for developing and maintaining trust.76 Trust between people and partners could be disrupted, however, particularly during periods of personnel or organisational change. Organisational restructuring either to accommodate intersectoral collaboration77 The strongest evidence related to capacity building, training and other engagement activity with communities affected by interventions to improve health or reduce inequalities.77 There were nine studies from eight interventions and one multiple case study overall where community representation was explicitly part of the collaborative exercise.77 Meaningful community engagement developed trust but also directly impacted on the external credibility of intersectoral collaborations. To illustrate at the local policy level, Lachance et al identified that bringing diverse stakeholders together in a healthy food and community partnership in the United States acted as opportunities to learn (‘learning communities’) which built capacity and leadership skills across community and policy stakeholders, thereby developing a broader skill set to support and build further credible intersectoral projects.52 In addition, this community-driven intersectoral partnership purposefully recruited from affected communities thereby ensuring that genuine advocates for the neighborhood were represented, that capacity among residents was developed and that policy change was sustainable.51 Evenson et al went further, observing that in some communities that adopted active living built environment policies, residents who experienced the positive benefits of intersectoral health promotion activity became less wary of subsequent efforts, suggesting a reinforcing feedback loop.58 Cheadle identified that working in a small or well-deﬁned community and having a stable group of core members enabled “workable decision-making processes and finding a match between community priorities and partnership activities.”53 Another component of collaboration that developed trust was the relational process of building capacity through training and knowledge sharing.68; a risk that could damage external credibility and, hence, collaborative power. One study noted that late inclusion of community representatives in the policy cycle and contestation about who the ‘community’ was led to conflict within the partnership, power imbalances and poorly addressed community concerns.68 The dangers of ‘tokenism’ were raised, however, in relation to community engagement The analysis revealed four important intermediate processes in collaboration that coalesced to deliver the collaborative power necessary for HiAP: internal and external legitimacy and internal and external credibility. Internal legitimacy was driven by multiple structural elements and processes, many of which were instrumental in developing relational trust. Internal credibility was supported by adequately resourced, multi-level designed collaborations, effective use of collaborative tool  and power-sharing. External legitimacy and credibility was created through meaningful community engagement, championing leadership and the adoption of win-win strategies. Insight from 15 we continue to lack evidence linking intersectoral collaborations to population health outcomes. Although this is disappointing, we do have a lot of evidence, and thinking, around what makes for successful intersectoral collaboration. This paper brings this knowledge together into a causal loop diagram, providing the foundations for future research to better explore the causal pathways underlying successful intersectoral collaboration and in ways that might lead to health outcomes. Our three main conclusions are: This evidence synthesis of case studies of intersectoral collaborations for health highlights that, reflecting earlier reviews,78 Initiatives such as the UK Prevention Research Partnerships and the Australian Prevention Partnership Centre advance this notion by intentionally integrating policy, practice, community and research in collaborations for healthy public policy.81 Evidence from these consortia should play an important role in the design of future intersectoral collaborations. While studies recognise the complexities of HiAP-style interventions, none of the studies applied an explicitly a complex systems lens to their analysis. The presentation of intersectoral collaboration for health and health equity in a causal loop diagram  has not,73 In addition, Delany-Crowe et al deconstruct the concept of ‘trust’ in joined-up government activities, highlighting how trust can bridge the gap between the known and unknown and act as a resource to stimulate action within government systems that are perceived to feature high levels of risk.82 In network analyses of advancing strategies for getting health and equity into policy, McGetrick et al identify the important role of policy influencer networks and the way issues in chronic disease prevention are primed and framed to effect policy-making.83 These dynamics of problem priming and framing, as well as coalitions of trust, are promising avenues for further exploration and mobilisation in the context of efforts to understand, and improve, intersectoral collaboration for population health. The review also raises questions of if and how affected communities are involved and the extent to which (dis)benefits are experienced by them affect existing and future collaborations. This also requires further examination, especially as much of this research focused on collaborations at a local level, leaving open questions about the importance of community and stakeholder engagement for intersectoral collaboration at national or regional policy levels. A further limitation of the model presented is its level of analysis; it does not represent some of the finer, granular detail of what happens in some of the many dimensions of collaborative activity. There is some progress in this task. For example, insight into how capacity can be built through collaboration in sport and physical activity promotion in England has revealed that, at the practitioner level, mutual confidence in the abilities and intentions of partners increased trust between partners which, in turn, led to more knowledge and skill sharing.85 Its development – as comprising structural and relational factors – and application to the context of intersectoral collaboration has helped to develop theory on how HiAP-style interventions have worked. Notably, structural and relational factors interrelate in the causal pathway, contributing to the credibility and legitimacy of collaborations and functioning to establish collaborative power. The interdependence of factors, their reinforcing capacity and the potential of external credibility to be undermined through a failure to properly engage with affected communities all highlights the potential fragility of the realisation and sustainability of collaboration. Indeed, while relationships may be central to collaborative governance and, as McPherson et al suggest, may ‘trigger’ system change,61 they are inherently intangible and often unstable in the face of internal and external change. Relationships between people and organisations are also expressions of power (im)balances; a factor that requires further examination in future studies . The concept of governance has been applied rarely in analyses of intersectoral collaboration for population health.86 These include legitimacy and voice , direction  and performance . Study findings, however, focussed little on issues of accountability and transparency, indicating possible democratic and governance deficits in intersectoral collaborations for health equity to date. Indeed, Holt et al identified that an absence of accountability mechanisms led to the deprioritsation of the goals of collaboration60; lack of accountability and transparency may be a barrier to healthier, cross-sectoral public policy. This synthesis raises further questions about governance. Many of the interventions studied aligned with the principles of ‘good governance.’87 What is required now is further examination of dynamic components, particularly with reference to politics and the power dynamics of intersectoral collaboration.88 Recent research has applied political science and critical policy analysis techniques to identify the dilemmas, tensions, emergent properties and context driven nature of HiAP-style initiatives.90 Their results caution against approaching HiAP as solely a question of centrally controlled ‘public administration’ but one of ‘public policy’ that invites us to take governance dilemmas seriously.30 Applying systems thinking as a means of understanding policy as a dynamic, often-unpredictable system, may offer a helpful way of making sense of HiAP and offering opportunities to intervene when efforts to collaborate appear to be failing. The proposed causal loop diagram offers a starting point for future research to examine the causal pathways that underpin successful collaborative governance in the inevitably emergent, adaptive and dynamic context of policy development and implementation. Taken together, these conclusions offer a way forward for thinking about and researching intersectoral collaboration. The causal loop diagram presented here may serve as a guide to constructing a clearer, more comprehensive picture of how collaboration of for health may play out in a given context and, importantly, link to health outcomes. This would represent a much-needed shift towards research that can identify and explain if and how intersectoral collaboration for health connects to population health outcomes. This review also provides insights that may help support the development and implementation of HiAP-style initiatives, especially at the local level . Overall, the synthesis offers a model of some of the causal pathways through which intersectoral collaborations have been governed. Until now, as Godziewski observes, there has been a dominance of examinations of intersectoral collaboration from a technocratic or structural perspective with acknowledgement, rather than analysis, of the dynamic interaction of structure and relationships. Many thanks go to Wellcome PhD students Jen-Yu Amy Chang and Jennifer Boyd. Thanks also to Suzy Paisley, ScHARR, for comments and encouragement on the final draft. Not applicable. Authors declare that they have no competing interests. ES: conception and design, acquisition of data, analysis and interpretation of data, drafting of manuscript, critical revision of the manuscript for important intellectual content, obtaining funding. KS: Analysis and interpretation of data, drafting of manuscript. HBW: Acquisition of data; administrative and technical support. PM: Analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, obtaining funding. The views expressed are those of the authors and not necessarily those of the funders. The funders had not role... etc. This work was supported by the UK Prevention Research Partnership (MR/S037578/1), which is funded by the British Heart Foundation, Cancer Research UK, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division (Welsh Government), Medical Research Council, National Institute for Health Research (NIHR), Natural Environment Research Council, Public Health Agency (Northern Ireland), The Health Foundation and Wellcome. ES is also funded by an NIHR Knowledge Mobilisation Research Fellowship (KMRF-2017-06-ST2-003).Supplementary file 1. Search Terms.Click here for additional data file.Supplementary file 2. Extraction Templates.Click here for additional data file.Supplementary file 3. Included Empirical Evaluative Studies and Their Characteristics.Click here for additional data file.