Supplemental Digital Content is available in the text There has been a historic separation between systems that address behavioral health problems and the medical care system that addresses other health issues. Integration of the 2 has the potential to improve care.The aim of this study was to evaluate the impact of Integrated Behavioral Health program on health care utilization and costs.Claims data between 2015 and 2018 from Rhode Island's All Payers Claims Database representing 42,936 continuously enrolled unique patients.Retrospective study based on propensity score–matched difference-in-differences framework.Utilization  and costs .Integrated Behavioral Health intervention in Rhode Island was associated with reduction in healthcare utilization. Emergency department visits reduced by 6.4 per 1000 people per month and office visits reduced by 29.8 per 1000 people per month, corresponding to a reduction of 7% and 6%, respectively. No statistically significant association was observed between the intervention and hospitalizations. The evidence was mixed for cost outcomes, with negative association recorded between the intervention and the likelihood of incurring non-zero cost but no significant association was observed between the intervention and the level of costs. This relationship held true for most of the cost measures considered.Integrated Behavioral Health intervention in Rhode Island was associated with significant reductions in emergency department visits and office visits, with no effects on hospitalizations. In terms of the cost outcomes, we found evidence that the intervention negatively affected the likelihood of incurring any non-zero costs but did not affect the level of costs. In 2017, BH-linked comorbidities accounted for nearly $406 billion in additional health care cost. Despite their widespread prevalence, these conditions are neither sufficiently diagnosed nor receive timely treatment.Behavioral health (BH) conditions include issues related to “the promotion of mental health, resilience and wellbeing; the treatment of mental and substance use disorders; and the support of those who experience and/or are in recovery from these conditions, along with their families and communities.” Evidence suggests that effective medical management, social support, and patient experience are greatly enhanced when behavioral health and, primary care, and medical services are integrated.At a broad level, the magnitude of the BH challenge in the United States is linked to the historic separation that exists between systems that address BH conditions and the medical care system which addresses other health issues. These systems differ in organization and financing, and are often represented by separate institutions and different professions. Such separation has meant that BH services are often provided separately from primary care. Despite the growing number of interventions and initiatives to integrate BH into primary care, rigorous evidence on the impacts of integrated behavioral health care delivery models within the context of primary care remains limited. This article addresses the evidence gap on the effects of integrating behavioral health into primary care on health care utilization and cost. Using propensity score-matched difference-in-differences (DiD) research design, this article examined the effects of an integrated behavioral health program in Rhode Island on health care utilization and cost.Integrated behavioral health is a care delivery model that combines or coordinates care for physical, mental, and substance use disorders, usually to address problems identified during primary care visits.1.2Integrated Behavioral Health (IBH) program in Rhode Island was a practice-level intervention rolled out across 11 primary care practices in the state. The intervention entailed a number of inter-related components including: the universal screening of patients for depression, anxiety, and substance use disorders; hiring BH providers  onsite; tracking and treating  patients who are screened positive at baseline ; making referrals to specialty care  as needed; and care coordination across different types of care. Participating practices used the following tools to screen patients: Patient Health Questionnaire-2/Patient Health Questionnaire-9, General Anxiety Disorder-7 and CAGE Adopted to Include Drugs. Based on their screening scores, patients were referred to a BH provider onsite. As a part of the IBH program, participating practices also received financial payments towards infrastructure support and a performance incentive for meeting pre-identified screening thresholds for depression, anxiety and substance disorder use.To participate in the IBH program, the practices needed to be a Patient Centered Medical Home and had to have achieved National Committee for Quality Assurance (NCQA) Level 2 recognition. The participating practices also had access to and participated in on-site IBH trainings and webinars, with the goal of helping the practices implement an IBH program that provided holistic patient-centered primary care services.The IBH program was rolled out in 2 waves across the 11 primary care practices. As a part of the first wave, 6 primary care practices received the intervention between January 2016 and December 2017. In the second wave, 5 practices received the intervention between November 2016 and October 2018. The intervention lasted for 2 years in both the waves. For the purposes of this article, we focused on the first wave of the intervention.The IBH program was implemented by the Care Transformation Collaborative of Rhode Island in 2016. Since its inception in 2008, Care Transformation Collaborative of Rhode Island has embarked on a number of initiatives, including Patient-Centered Medical Homes and Community Health Teams (CHTs). The IBH program was one of such initiatives aimed at improving primary care in Rhode Island.22.1We used Rhode Island's All Payers Claims Database, which includes medical and pharmacy claims from Medicare, Medicaid and commercial payers in the state. We used data between January 2015 and December 2018. Our analytical sample comprised of 42,936 adults who were 18 years and older and were continuously enrolled between 2015 and 2018.2.2The intervention group consisted of people who received healthcare from the primary care practices that participated in the IBH program. This included all the individuals who received services from the 6 practices that made the first wave of the IBH program. Comparison group consisted of a matched set of individuals that resembled the intervention group in terms of its baseline characteristics. The 12 months’ window preceding the intervention start date was considered as the pre-period .2.3 All outcome measures were analyzed at the person-month level.We looked at 2 sets of outcomes: utilization and cost. Utilization was measured by office visits, emergency department (ED) visits, and inpatient visits or hospitalizations. For cost outcomes, we looked at 5 measures: total cost, inpatient cost, outpatient cost, pharmacy cost, and professional cost. The cost measures were based on the 2017 Health Care Cost and Utilization Report.2.4We employed the propensity score matching (PSM) technique to identify a comparison group that was similar to the intervention group. The DiD framework was then used to identify the changes in outcomes associated with the intervention. This is achieved mainly by matching on observable characteristics. Unlike a randomized control trial, however, PSM cannot match on unobservable characteristics. In our data, selection into receiving the IBH intervention was non-random. By design, the intervention was offered only to those practices that chose to participate . We used the PSM technique to create a matched comparison group.In the absence of an experimental data, PSM can be used to make observational data mimic, to some extent, data from a randomized control trial. Our matching variables included age, sex, Medicare status, Medicare status category, dual status, Medicaid status category, and Elixhauser Comorbidity Index. The variables were selected because they were identified, based on the existing literature on health services research literature, as potential confounders that bias intervention effect size estimates. We examined covariate balance and analyzed graphically the region of common support to ensure that the intervention group  and the comparison group  at baseline looked similar in terms of their baseline characteristics.To implement the PSM technique, we estimated a propensity score based on a bivariate probit model. The propensity score was then used to create a matched comparison group with observable baseline characteristics similar to the intervention group. We matched at the individual level using one-to-many matching with replacement.,14 For cost outcomes, we used the 2-part model, in which the first part models the probability of non-zero costs and the second part models the level of costs for observations with non-zero costs. This approach of modeling cost has been described and used widely in the existing literature.–17 We assumed binomial distribution with log-link to model the first part. The second part was modeled assuming gamma distribution with log link. Modeling of the utilization outcome assumed negative binomial distribution with log-link.Both utilization and cost outcomes were modeled using generalized estimating equations.The following framework was used to estimate the effects of the intervention on outcomes:Yim represents an outcome (utilization or cost) for person i in month m. For both utilization and cost models, we included an indicator variable to denote whether someone received services from an IBH practice (IBH Status); an indicator variable for the post period ; and an interaction term between IBH Status and Post (IBH Status ∗ Post). The coefficient on the interaction term (δ) is the coefficient of our interest and represents the DiD estimator. Vector X includes age, sex, Medicaid coverage, Medicaid eligibility basis, Medicare coverage, dual eligibility, zip code-level poverty rates, and Elixhauser Comorbidity Index in the baseline period.where  We controlled for time trends using monthly fixed effects (vm), and used robust standard errors and individual-level clustering to account for the repeated nature of the data. The DiD estimates are presented as the mean marginal effects, with the exception that the DiD estimates for part one of the 2-part model for cost measures are presented as the odds ratios (ORs). This was done because ORs are relatively easier to estimate and interpret.,21,22Our models are based on analyses at the person-month level. The models accounted for one-to-many matching, with replacement, by using frequency weights.http://links.lww.com/MD2/A349), in parts B and C.As a part of our sensitivity analyses, we did 2 things. First, to test the parallel trends assumption, we graphically analyzed the linear trends of the outcome measures for the intervention group and the comparison group. Second, we carried out an analysis to check whether there was any kind of “maintenance effect” . To do this, we re-defined the post period to include both the actual intervention period as well as the additional time until the end of 2018. Results of the sensitivity analyses are included in supplementary digital content . The study was approved by the Institutional Review Board of Brown University.33.1P value, was higher for the comparison group than the intervention group.As shown in Table 3.2P = .004), representing a percent reduction of 7%. Office visits reduced by an average of 30 visits per 1000 people per month (P < .001) and represented a percent reduction of 6%. The coefficient on hospitalizations was negative but not statistically significant.IBH intervention was associated with a significant reduction in ED visits and office visits but with no change in hospitalizations Table . ED visiP < .001), 0.94 (P < .001), 0.93 (P < 0.001), and 0.97 (P = .03) for total cost, outpatient cost, professional cost, and pharmacy cost, respectively. There was no significant association between the intervention and the odds of having non-zero inpatient costs. Conditional on having non-zero costs, however, the IBH intervention had no significant association with the level of costs.We found a statistically significant association of the IBH intervention with the probability of having non-zero costs. For observations with non-zero costs, however, we did not find any significant associations of the intervention with the level of costs. This pattern held true for nearly all measures of cost. As shown in Table http://links.lww.com/MD2/A349). As shown in SDC, http://links.lww.com/MD2/A349 part C table A1, http://links.lww.com/MD2/A349, the significant association observed between the IBH intervention and the utilization and cost measures was sustained even a year after the end of the intervention. ED visits reduced by 7.6 per 1000 people per month (P < .01), whereas office visits reduced by 31.6 per 1000 people per month (P < .001). No significant association was observed with respect to hospitalizations. In terms of the cost measures, IBH was associated with a decrease in the odds of incurring non-zero costs, with the ORs ranging between 0.86 and 0.96. Inpatient cost was the only exception. For a given level of non-zero cost, no significant association was observed between IBH intervention and the level of cost. This relationship held true across all cost categories.Results of the sensitivity analysis were consistent with the main results .Our study has limitations. First, the IBH program was a practice-level intervention but our analysis is at the person-level. This means that it is possible for someone without any BH issues to visit an IBH practice and receive care that is unrelated to the IBH intervention itself. Yet, the person can be flagged as an IBH person in our sample simply because he/she visited an IBH practice. If/when this occurred, however, the potential bias would be toward the null. Second, the intervention had a number of inter-related components to it. Although some components were likely more important than other components, we cannot disentangle the effects of the different components of the intervention. The results presented are the combined effects of the components that make up the intervention. As such, the exact results are a lower bound of the most active components that may have been “watered down” by the inability to separate. Third, in implementing the DiD strategy, we pool all the pre periods into one period, and pool all the post periods into one period. While this is an accepted practice in the literature and allows us to compare pre and post outcomes means between the intervention and the comparison group, we lose some granularity in the process. Fourth, in looking at the utilization outcomes, we are unable to disentangle BH-linked utilization from overall utilization. For example, we cannot say the share of office visits that were directly attributable to BH conditions.Our study has important implications for health policy and practice. We showed that there was reduction in ED and office visits associated with the IBH intervention. This suggests the potential role of IBH interventions in reducing unnecessary ED and office visits, thereby saving scarce resources. Although we mostly saw no effects of the intervention on the level of costs, our finding that the intervention was associated with reduction in the odds of incurring non-zero costs bodes well for the possible role of IBH interventions in reducing overall costs. Future research in the area should try to quantify and contextualize the effects of IBH-like interventions on necessary care versus care that is deemed not as necessary.The authors thank Deepak Adhikari, MS and Xinqi Li, PhD, for their analytical insights. The authors also thank Debra Hurwitz, MBA and Pano Yeracaris, MD, for their insights on the IBH program.Conceptualization: Michael Barton Laws, Omar Galarraga.Data curation: Bishnu Bahadur Thapa.Formal analysis: Bishnu Bahadur Thapa, Omar Galarraga.Funding acquisition: Omar Galarraga.Investigation: Bishnu Bahadur Thapa, Michael Barton Laws, Omar Galarraga.Methodology: Bishnu Bahadur Thapa, Michael Barton Laws, Omar Galarraga.Project administration: Omar Galarraga.Resources: Michael Barton Laws, Omar Galarraga.Supervision: Michael Barton Laws, Omar Galarraga.Writing – original draft: Bishnu Bahadur Thapa, Michael Barton Laws, Omar Galarraga.Writing – review & editing: Bishnu Bahadur Thapa, Michael Barton Laws, Omar Galarraga.


An established dataset of histopathology images obtained by biopsy and reviewed by two pathologists is used to create a two-stage oral squamous cell carcinoma diagnostic AI-based system. In the first stage, automated multiclass grading of OSCC is performed to improve the objectivity and reproducibility of histopathological examination. Furthermore, in the second stage, semantic segmentation of OSCC on epithelial and stromal tissue is performed in order to assist the clinician in discovering new informative features. Proposed AI-system based on deep convolutional neural networks and preprocessing methods achieved satisfactory results in terms of multiclass grading and segmenting. This research is the first step in analysing the tumor microenvironment, i.e., tumor-stroma ratio and segmentation of the microenvironment cells.Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC. Cancer is a major public health problem and the second leading cause of death in the developed world. Oral cancer  is proposed to enhance high-frequency components in the case of multiclass classification and to extract low-level features in the case of semantic segmentation. This approach allows more effective predictions and improves the robustness of the entire AI-based system.The main contributions in this research are as follows:Many studies have used various models, techniques, and methodologies in order to develop AI-solutions for classification and segmentation of oral cancer.Ariji et al. demonstrated the use of artificial intelligence for the diagnosis of lymph node metastasis in patients with OSCC. The dataset consisted of 441 computed tomography images in total. The performance of the proposed system resulted in 75.4% sensitivity, 81% specificity, and 78.2% accuracy. The obtained results were similar to those found by the radiologists which prove that the proposed AI system can be valuable for diagnostic support . HalicekThe literature reveals that most of the researchers have applied CNN in the retrospective study to detect and classify oral cancer. It can be observed that most of the classification tasks were binary classification, where the features from the image like colour, shape, and texture are used. Even though, CNN is a powerful visual model for image recognition tasks, some researchers used deep CNNs which outperform conventional CNN in classification tasks.Chan et al. proposed an innovative deep CNN combined with a texture map for oral cancer detection. The proposed model consists of two collaborative branches, one to perform oral cancer detection, and a second one to perform region of interest (ROI) marking and semantic segmentation. The experimental results of detection, based on a wavelet transform, are up to 96.87% for sensitivity and 71.29% for specificity . Most of the segmentation tasks were performed using deep CNN architectures on histopathological images. A shortcoming of the prementioned studies is that they were trained to determine a binary state of carcinoma or non-carcinoma. Based on a literature study only Das et al. proposed a deep learning model for the classification of cells into multiple classes in epithelial tissue of OSCC. The dataset consisted of image patches derived from whole slide biopsy images. Proposed CNN model resulted in 97.5% accuracy . According to the presented studies different AI approaches have proven successful for clinical analysis ,27,28,29The overall workflow of the research can be described as follow; first, the original data will be augmented and used as an input variable in order to perform multiclass classification by utilizing AI-based models. After evaluating the performance of the models, the obtained results will be compared, and the best performing model will be selected. Second, the input data will be decomposed using stationary wavelet transform in order to obtain wavelet coefficients. The obtained coefficients will be preprocessed and the data will be reconstructed. Afterwards, the data preprocessed with SWT will be used to train the selected model, and the obtained results will be compared. Third, the selected model will be used as Deeplabv3+ backbone, by which, semantic segmentation into two classes will be performed. Fourth, the impact of data preprocessing on the model performance and robustness will be examined. Lastly, the best performing configuration in terms of semantic segmentation will be used to show predictions of the epithelial and stromal tissue on three samples from new, unseen data. The overview of the proposed framework is given in For this research, 322 histology images with 768 × 768-pixel size have been used to create a dataset. The formalin-fixed, paraffin-embedded oral mucosa tissue blocks of histopathologically reported cases of OSCC were retrieved from the archives of the Clinical Department of Pathology and Cytology, Clinical Hospital Center in Rijeka. Sample slides were reviewed by two unbiased pathologists and classified following the 4th edition of the World Health Organization (WHO) classification of Head and Neck tumors .The paraffin blocks tissues were selected from the patients with primary invasive squamous cell carcinomas, where the invasion of the epithelium of the oral cavity through the basement membrane was confirmed with certainty. All patients who received preoperative chemo- or radiation-therapy, as well as patients with in situ carcinoma and relapsed or second primary OSCC were excluded from the research.Kappa coefficient was used to determine the degree of agreement between the pathologists. The value of Kappa coefficient was found to be 0.94. In accordance with the aforementioned classification, images have been divided into three classes: well-differentiated (grade I), moderately differentiated (grade II), and poorly differentiated (grade III) OSCC, as shown in Briefly, 4 μm sized paraffin-embedded tissue sections were deparaffinized in tissue clear solution, rehydrated in a series of different concentrations of alcohol and stained using monoclonal mouse anti-MT I + II antibody ((clone E); DAKO, Santa Clara, CA, USA; diluted 1:100 in PBS with 1% BSA)) and polyclonal rabbit anti-megalin  using a standard protocol . ImmunorImages were captured using the light microscope  equipped with a digital camera  and transmitted to a computer by CellF software (Olympus). Furthermore, images were captured at 10 × objective lenses. Corresponding clinic-pathological reports of the patients were collected and used for pTMN classification . PatientDeep CNNs are heavily reliant on a large number of samples in order to achieve satisfactory performance and avoid overfitting. Since domains, such as medical image analysis, do not have access to a large number of samples very often, it is necessary to perform augmentation techniques, by which, the size and quality of the data can be significantly increased . Due to Geometrical transformations used for the augmentation procedure are: 90 degrees anticlockwise rotation, 180 degrees anticlockwise rotation, 270 degrees anticlockwise rotation, horizontal flip, horizontal flip combined with 90 degrees anticlockwise rotation, vertical flip, and vertical flip combined with 90 degrees anticlockwise rotation. Since newly generated data are variations of the original data, the augmentation procedure is utilized only for the creation of the training samples, thereby, testing samples are not augmented. Due to the high-imbalance of OSCC classes, stratified 5-fold cross-validation is used to estimate the performance of AI-based models. This way, the representation of each class across each test fold is approximately the same . AccordiBy utilizing the aforementioned transformations, a new training set with additional 1799 images has been created, which gives a total of 2056 images as shown in The wavelet transform (WT) is a powerful technique commonly used in data preprocessing . Applyinobtained . The disobtained :(2)Xk,l=When dealing with images, DWT is applied in each dimension separately; therefore, the image is divided into four subbands LL, LH, HL, and HH where LL represents approximation coefficients while LH, HL, and HH represent detail coefficients of an image . DWT redno decimation step—provides redundant information,better time-frequency localization, andtranslation-invariance.The advantages of SWT are as follows :no decimAfter the SWT decomposition process, obtained coefficients are weighted using a mapping function. This way important features of an image can be further enhanced. The following considerations are taken into account when determining the mapping function; First–coefficient mapping is performed only on detail coefficients. Second–details with high, as well as details with low coefficient values preserve valuable information, thereby, they are heavily weighted. Wavelet coefficient mapping function can be mathematically defined as follows:The quality of weighted coefficients directly depends on mapping function constants and wavelet function; therefore, a careful selection of these parameters is necessary. Additionally, the selection of the parameters also depends on the type of input data and expected output. More traditional approaches for parameter determination such as random search or grid search can sometimes be infeasible since evaluating each point in large search-spaces is extremely costly  . These mIn order to determine optimal values of wavelet coefficient mapping function constants In this subsection, a brief overview of deep CNN architectures suitable for OSCC classification problem is given.Chollet demonstrated a novel architecture named Xception, based on Inception V3. Compared to Inception V3 on ImageNet, Xception resulted in larger performance improvement . In a coAs neural networks become deeper, they become difficult to train due to the notorious vanishing gradient problem. In order to ease the training of deep neural networks, He et al. propose a residual network (ResNets) . They reSandler et al. introduced in their article  the mobiMobileNetv2 for classification task was compared with MobileNetv1, NASNet-A, and ShuffleNet on the ImageNet dataset. The results show that presented architecture with specific design variations improves the state-of-the-art for a wide range of performance points .The process of assigning a semantic label to each part of the image is called semantic segmentation. Chen et al. proposed the DeepLab system as state-of-the-art at PASCAL VOC 2012 semantic segmentation task and based on the experimental results they achieved 79.7% mIOU on the test set . Later, The architectures described in In order to evaluate and analyse the obtained results, it is necessary to describe evaluation metrics. Statistical measures such as micro- and macro- area under the curve (AUC) are adopted to evaluate the classification performance of models. In the second stage, semantic segmentation performances are evaluated using mean intersection-over-union (mIOU), Dice coefficient (F1), accuracy (ACC), precision, sensitivity, and specificity.micro can be calculated. TP represents true positives, i.e., cases where the predicted and actual values are positive. TN represents true negatives, cases where the actual and predicted values are negative. False negatives (FN) capture cases when the prediction is negative and the actual value is positive. Furthermore, FP represents false positives, where the prediction is positive, and the actual value is negative [The AUC is an evaluation metric for calculating the performance of the binary classifier. To extend AUC to multiclass classification, it is necessary to conceptualize the problem as a binary classification problem, by using One vs. All technique, which means that one class is classified against all other classes. Micro averaging of true positive rate (TPR) calculates the number of correct classifications for each class and uses it as the numerator, while for the denominator it uses the total number of samples. Furthermore, fallout or false positive rate (FPR) calculates the ratio of incorrect classifications for each class and the total number of samples . The matnegative .macro is based on the calculation of TPRmacro as well as FPRmacro and can be calculated as followsMacro averaging for k classes compute the metrics individually for each class and averages results together. AUCmacro and -micro measure will result in better classification performance of the model. Higher values of AUCOne of the most used metrics for semantic segmentation is IOU, also known as the Jaccard Index. If the previously introduced terms TP, TN, FP, and FN are used, the Jaccard index can be defined as :(8)IOU= Since this research deals with multiple classes (more than one), mean IOU (mIOU) is used as a metric. It can be calculated as a ratio between a total number of IOU-s for each semantic class and total number of classes. Dice coefficient is positively correlated with the IOU. It is an overall measure of a model’s accuracy and can be defined as :(9)F1= 2F1 score ranges in 0–1 where the model with low false positives and low false negatives performs well. Accuracy measure points out what percentage of the pixels in the image are assigned to the correct class, and can be calculated as follows :(10)ACC=essed as :(11)PrecMathematically, sensitivity and specificity can be calculated as the following (12)SensHigher values of performance measures defined by Equations (8)–(13) mean better segmentation performance of the model.This section demonstrates the experimental results obtained at each step of the proposed methodology. The first experimental results are achieved with Xception, ResNet50, ResNet101, and MobileNetv2 architectures which are pre-trained on ImageNet. Each model architecture is trained with three optimizers: stochastic gradient descent (SGD), Adam, and RMSprop, as shown in macro and -micro values in the case of ResNet50, ResNet101, and MobileNetv2 architectures. However, RMSprop optimizer in a combination with Xception architecture achieves the overall highest values of AUCmacro, and -micro. Summarized mean values of performance measure along with corresponding standard deviation for each model architecture is shown in According to the 5-fold cross-validation results, the Adam optimizer achieves the highest AUC−6. The training process in the second stage was performed with a learning rate of 0.0001 and the same learning rate decay of 1 × 10−6.The best results obtained in the first step were achieved when two additional layers were added at the end of the base Xception architecture. After the separable convolutional layer, and ReLU activation function, the global average pooling layer followed by the output layer with three neurons and Softmax activation function were added. This way, a classification of OSCC into three classes was enabled. The training process was divided into two stages; the first stage where only added layers were trainable while the others were frozen, and the second stage, where all layers were trainable except the last two added layers. In the first stage, the training process was performed with a learning rate of 0.001 and learning rate decay of 1 × 10The second step of the proposed methodology is data preprocessing using Stationary Wavelet Transform, where original histopathology images were decomposed at level 1 using Haar, sym2, db2, and bior1.3 wavelet functions. After the decomposition process, high-frequency wavelet coefficients LH, HL, HH are weighted using mapping function defined by Equation (3), which resulted in new, modified LH’, HL’, HH’ subbands. Modified subbands along with unmodified LL subband were used for SWT reconstruction in order to obtain input image for AI model, as shown in micro performance measure was monitored during the process of optimizing. Each Bayesian iteration involved data preprocessing with a defined set of mapping function constants, model training process, and performance evaluation. After 25 steps of random exploration and 20 steps of Bayesian optimization, the best performing constant configuration was obtained as shown in By utilizing Bayesian optimization, the goal was to find optimal values of wavelet mapping function constants, by which, the maximal value of performance measure is achieved. In the case of this research, AUCAfter multiclass grading of OSCC from histopathological images, the third step is semantic segmentation of the epithelial and stromal tissue. In contrast to the aforementioned preprocessing approach for multiclass classification, the data preprocessing in the case of semantic segmentation utilizes only a low-frequency subband. Therefore, after one-level decomposition utilizing the SWT with Haar, sym2, db2, and bior1.3 wavelet functions, only low-frequency coefficients with applied Greys colormap were used as input for the AI model. SWT decomposition of an image at level one using Haar wavelet is shown in According to the results presented in By using data preprocessed with SWT at level one using a Haar wavelet function and a fully-trained DeepLabv3+ model, the predictions were made for the three cases of new and unseen data, as visually represented in AI experiments were performed using Python on a GPU-based High Performance Computing (HPC) server. The server consists of two Intel Xeon Gold CPUs , 768 GB of ECC DDR4 RAM, and five Nvidia Quadro RTX 6000 GPUs, with 24 GB of RAM, 4608 CUDA and 576 Tensor cores.The diagnosis of OSCC is based on a histopathological assessment of altered oral mucosa which despite high subjectivity, still is the most reliable method of diagnosing oral carcinoma. The pathologist evaluates and grades the tumor depending on resemblance to the normal oral squamous epithelium as well, moderately or poorly differentiated . HistopaThis research proposes an AI-based system for multiclass grading and semantic segmentation of OSCC. The proposed system is compared with similar approaches presented in the literature ,59 to vaThe training process, in the case of multiclass grading, was performed in two stages as described in results section. The purpose of the two-stage learning process is to achieve better performance and at the same time increase the robustness of trained models. Since two additional layers were added at the end of the original Xception architecture, their weights were randomly initialized; therefore, training with a learning rate value of 0.001 was performed. However, in the second stage of the training process, the learning rate was set to a lower value of 0.0001. This way, the weights of layers pre-trained on ImageNet were only adapted to the new problem of multiclass grading.macro and 0.942 AUCmicro are achieved with a combination of Xception architecture and RMSprop optimizer. Furthermore, ResNet50 in a combination with the Adam optimizer showed AUCmacro and -micro values of 0.871 and 0.864, respectively, which is slightly lower than ResNet101 performance (0.882 AUCmacro and 0.890 AUCmicro). However, ResNet101—Adam was worst-performing in terms of standard deviation with values of ±0.125, and ±0.112. Lowest values of standard deviation were obtained in the case of MobileNetv2 architecture in a combination with Adam optimizer.From the presented results, in the case of multiclass grading with no preprocessing, it can be concluded that high values of 0.929 AUCSince important features for distinguishing the differences between OSCC gradings are mostly contained in high-frequency components of the image, the wavelet coefficient mapping function was proposed. With the help of SWT, the image was decomposed on the LL, LH, HL, and HH subbands which allowed coefficient weighting. Mean values of the high-frequency components were located around zero; therefore, constants of coefficient mapping function were determined in a way that features with high- and low-coefficient values were enhanced. SWT reconstruction process with LL, LH’, HL’, and HH’ subbands, resulted in images with enhanced high-frequency features.macro value of 0.947 and AUCmicro value of 0.954 or higher. Moreover, when all results are summed up, it can be noticed that the highest values of performance measure are achieved using the proposed methodology with coefficient mapping function constants a, b, c, and d with values of 0.091, 0.0301, 0.0086, and 0.3444, respectively, and db2 as wavelet function. Performance of the proposed model in terms of AUCmacro and AUCmicro values is 0.963 ± 0.042 and 0.966 ± 0.027, respectively; therefore, it can be concluded that not only performance measure was increased, but also the values of standard deviation were decreased. A decrease in standard deviation value resulted in increased robustness of the model.If the performances, in the case of multiclass grading with preprocessing, are compared, it can be seen that all of the presented configurations achieved AUCThe foremost task for OSCC diagnosis and treatment plan is accurate histopathology examination of samples to detect morphological changes of the tumor cells. Histopathological grading is focused on the tumor cells features like nuclear pleomorphism, mitotic activity, depth of invasion, tumor thickness and degree of differentiation . HoweverThe conventional practice, histopathological assessment on light microscopy is time-consuming, tedious, and relies on the experience of pathologists. The characteristic of the microscope  and preparation of the tissue samples may hamper diagnosis and affect manual judgment. The histological parameters can be investigated under different magnification . Literature reveals that most of the segmentation research has been performed on 40× magnified images ,66,67,68Recently, many computer-aided tools for medical image analysis show significant progress towards better healthcare services. The second stage of the proposed AI-based system is semantic segmentation which is a mandatory step towards tumor microenvironment analysis. The semantic segmentation of epithelial and stromal tissue is performed on 10× magnification histopathology images. In contrast to the preprocessing in the case of multiclass grading, preprocessing for semantic segmentation implies using only low-frequency coefficients, i.e., LL subband, while the high-frequency subbands are discarded. Therefore, by using extracted low-frequency features, DeepLabv3+ in a combination with Xception_65 as backbone achieved satisfactory results in the case of multiclass semantic segmentation of tumor tissue. From the obtained results it can be noticed that the segmentation model in a combination with low-frequency features outperformed the model which used original, non-preprocessed data as input. In terms of performance measures, the highest mIOU (0.879), F1 (0.955), and Accuracy (0.941) values are achieved using low-frequency features obtained at SWT decomposition level-one using Haar wavelet. However, in terms of Precision and Specificity performance measures, data preprocessed with db2 wavelet function outperforms others with values of 0.952 (Precision), and 0.911 (Specificity). When Sensitivity measure is observed, non-preprocessed data achieved the highest value of 0.967.Presented results indicate that segmenting of epithelial and stromal tissue has a great potential in the quantification of qualitative clinic-pathological features in order to predict tumor aggressiveness and metastasis. Segmented areas will be used in future work for analysing the tumor microenvironment, where the stroma is essential for the maintenance of epithelial tissue. Stromal and epithelial regions of the OSCC have a different impact on the disease progression which is important for the histopathology image analysis.AI computer-aided systems for analysing the tumor microenvironment might contribute to modified treatment planning, improving prognosis and survival rates, and maintaining a high-quality life of patients.Histology grading is a way to classify cancer cells based on tissue abnormality. It relies on the subjective component of the clinician and as such may adversely affect appropriate treatment methods and outcomes of the patient. This research highlights the huge potential of the application of image processing techniques with the aid of Artificial Intelligence algorithms in order to achieve an effective prognosis of OSCC and increase the chance of survival among people.macro and 0.966 AUCmicro with the lowest standard deviation of ±0.042 and ± 0.027, respectively. In the first stage of the research, the authors demonstrate the integration of deep convolutional neural networks with stationary wavelet transform along with wavelet coefficient mapping function for multiclass grading of OSCC. From obtained results, it can be concluded that integration of Xception and SWT resulted in the highest classification values of 0.963 AUCIn the second stage, semantic segmentation was performed. Xception_65 as DeepLabv3+ backbone, integrated with low–frequency subband resulted in 0.879 ± 0.027 mIOU, 0.955 ± 0.014 F1 score and 0.941 ± 0.015 accuracy. Segmentation of the tumor on the epithelial and stromal regions is the initial step in the study of the tumor microenvironment and its impact on the disease progression. Based on the results of the proposed methodology, a two-stage AI-based system has been proven successful in terms of multiclass grading as well as segmenting of epithelial and stromal tissue and has a great potential in the clinical application in prediction of tumor invasion and outcomes of a patient with OSCC. Since the limitation of this research was data availability, future work should use a dataset with more histopathology images, to achieve a more robust system. The presented approach will be the first step in analysing the tumor microenvironment, i.e., tumor-stroma ratio and segmentation of the microenvironment cells. The main idea is to develop an advanced automatic prognostic system that could analyse parameters such as cell shape and size, pathological mitoses, tumor-stroma ratio and distinction between early- and advanced-stage OSCCs. Moreover, try to create predictive algorithms that would improve prognostic indicators in clinical practice and thus improve therapeutic treatment for the patient.


The HGS was determined by dynamometry. The anthropometry  and physical activity were also measured. The relative HGS (RHGS) was calculated by dividing the maximum HGS of the dominant hand by the body mass index. The AO was defined as a waist circumference (WC) >88 cm for women, and >102 cm for men. From the sample, 52.6% were women, 56.4% had excessive weight, and 42.7% had AO. The absolute and RHGS were greater in men compared to women (p < 0.001) and were decreased with age in both sexes. We observed a moderate negative correlation between WC and RHGS . The RHGS was lower in individuals with AO, independent of age and sex (p < 0.05). For each cm increase in WC, the odds of low RHGS (<25th percentile) increased by 12 and 9% for men and women, respectively. The AO is related to higher odds for low RHGS . In our sample of healthy adults, a higher AO was associated with a lower muscle strength measured by dynamometry.Handgrip strength (HGS) is a well-established indicator of muscle strength and a reasonable clinical predictor of metabolic health and diseases. This study explores the association between relative muscular strength and abdominal obesity (AO) in healthy Chilean adults. A convenience sample was recruited ( The relationship between adipose tissue and muscle function has attracted interest in recent years –9. Increvia neuroendocrine dysregulations  was associated with stronger HGS, but a larger waist circumference (WC) was associated with a weaker HGS  defined as absolute HGS divided by BMI (HGS/BMI). When muscle strength is compared without correcting for body mass, women with obesity present a higher level of absolute muscle strength , 6 compaIn recent population-based studies, stronger correlations have been observed between RHGS and cardiovascular biomarkers, compared to absolute HGS and dominant HGS . The RHGAn observational, cross-sectional study was conducted in a non-random sample of healthy Chilean adult subjects (18–64 years old) from the Metropolitan and Valparaiso regions of Chile. Individuals were recruited between June 2018 and November 2020 in shopping centers, the waiting rooms of health centers, supermarkets, banks, universities, gyms, and private companies. The exclusion criteria included illness, hospitalization within the past month, subjects with any pathology that affected strength, and sensitivity of upper extremities, history of upper limb injury or deformity with motor impairment, neurological disorders involving the hand , and others who routinely perform a high-demand upper-extremity work, professional sportsmen/women, and being a foreigner.The study protocol followed the Helsinki Declaration and was approved by the Ethics Committee of the Universidad del Desarrollo – Facultad de Medicina Clínica Alemana. A total of 1,016 subjects consented to participate in the study; of these 1,004 completed the handgrip assessments and 988 had the WC measured. The final sample was composed of 976 participants.one, two, three, squeeze… harder… harder…”), and the participants were asked to use the non-dominant hand first, followed by the dominant hand. Three measurements of HGS were taken for each hand, with 10–20 s of rest between measurements to avoid fatigue. Absolute HGS was considered as the maximum strength of the dominant hand achieved in any of the three attempts. The RHGS was defined as the absolute HGS divided by BMI and was expressed as kg/kg/m2. We defined low relative muscle strength values as under the 25th percentile by sex.The Jamar® digital hand dynamometer  was used to measure HGS. The participants were seated on a standard height chair without armrests and positioned per the American Society of Hand Therapists' recommendation . The sub2 using the measured weight and height. The participants were categorized as normal weight when the BMI was <25 kg/m2, overweight with a BMI between 25 and 29.9 kg/m2, and obese with a BMI ≥ 30 kg/m2.Anthropometric measures  were conducted by trained evaluators following the National Health and Nutrition Examination Survey protocol . The parThe WC was determined using a flexible metric tape at the midpoint between the iliac crest and the last rib. The participants remained standing with arms alongside the body and the trunk that is free of clothing. The measurement was performed with the relaxed abdomen at the end of expiration. The AO was defined as a WC >88 cm for women and >102 cm for men . The AO Type of activity, frequency, and duration were self-reported through a questionnaire developed for this research based on the International Physical Activity Questionnaire (IPAQ) . The typT-test, Kruskal–Wallis, and Mann–Whitney U tests were used depending on variable type and distribution. To correlate WC (cm) and RHGS (kg/kg/m2), the Spearman's rank correlation coefficient was used. The multivariate logistic regression was used to assess the association between AO and low RHGS, stratified by sex. Models were adjusted for the following covariates: age (years), physical activity status, and mid-arm circumference (cm) regardless of their levels of significance. This is based on the available evidence that the aforementioned variables have been associated with the outcomes of our study .Descriptive data from categorical variables were expressed in relative and absolute frequencies. Numeric data were described as medians, interquartile ranges, and percentiles , according to sex and age. The Shapiro–Wilk test was used to determine the normality of the distribution. For bivariate analysis assessing sex differences in covariates, the chi-square, Student ur study , 16, 17.p < 0.001). In relation to the dominant hand, 92.7% were right-handed, with no difference by sex (p = 0.413). Men presented higher HGS (around 20 kg of difference) than women in both hands, and right hands had higher values in both sexes (p < 0.001).. When considered the RHGS, in both sexes, the correlation was moderate, negative, and significant .The results of the correlational analysis between HGS and WC are shown in p < 0.05).p < 0.001). In addition, having AO increased the probability of presenting a low RHGS in the overall sample by 72%. This association remained significant for women only in adjusted models .The association between low RGHS (<25th percentile) and AO was significant both in unadjusted and adjusted models. It can be seen from the data in The present study explored the associations of AO and muscle strength. We hypothesized and found that the participants with AO would present lower muscle strength. As expected, compared to the women, the men presented higher HGS, and HGS decreased with age in both sexes. Another study carried out in Chile  and studSeveral studies have shown that aging is associated with a decline in HGS, and these studies highlighted the fact that an increase in fat mass contributes to deterioration of HGS in older adults , 20. TheIn this study, the subjects with AO had lower HGS than the subjects without AO, in both sexes. This result is in accordance with other studies –3, 19. IFurthermore, Otten et al. indicate that the grip strength cannot be considered an indicator of whole-body strength in obese individuals because the relationship between obesity and upper and lower extremity strength differs . Since aOur results should be interpreted with the following limitations. First, we did not measure body composition, which is a more specific indicator of adiposity . NeverthIn conclusion, our findings contribute to the understanding of the association of AO and low RHGS in the adult population. The WC measurement seems to be a good parameter to identify a low RHGS, since both studied measures revealed associations with lower muscle strength. Additionally, the cut-off points for RHGS by sex and age may be a clinically useful tool to contribute to the evaluation of risk of sarcopenia in young and middle-aged adults. In view of these findings, the WC and HGS measurements should be standard protocols for healthcare professionals to carry out the prevention and adequate treatments, starting in adulthood.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of the Universidad del Desarrollo – Facultad de Medicina Clínica Alemana (record number 2018/42), dated Jun 13, 2018. The patients/participants provided their written informed consent to participate in this study.ACP, XD-T, and DQ-S: conceptualization, investigation, methodology, validation, writing—original draft, and writing—review and editing. ACP: supervision. DQ-S: analysis and software. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


For colorectal cancer patients, traditional biomarker deficient mismatch repair/microsatellite instability (dMMR/MSI) is an accurate predictor of immune checkpoint inhibitors (ICIs). Recent years, researchers considered tumor mutation burden (TMB) as another predictive biomarker which means the number of nonsynonymous mutations in cancer cells. Several studies have proven that TMB can evaluate the efficacy of ICI therapy in diverse types of cancer, especially in non-small cell lung cancer and melanoma. However, studies on the association between TMB and the response to ICI therapy in colorectal cancer alone are still lacking. In this study, we aim to verify the effect of TMB as a biomarker in predicting the efficacy of ICIs in colorectal cancer.I2 statistic and p-values and performed publication bias assessments, sensitivity analyses, and subgroup analyses to search the cause of heterogeneity.We searched the PubMed and Ovid MEDLINE databases up to May 1, 2021 and screened studies for eligibility. Thirteen studies published from 2015 to 2021 with 5062 patients were included finally. We extracted and calculated hazard ratios (HRs) and odds ratios (ORs) of overall survival (OS) and objective response rates (ORRs) and their 95% confidence intervals (95% CIs). Pooled HR and OR were evaluated to compare OS and ORR between TMB-high and TMB-low groups in colorectal cancer patients. Meanwhile, we assessed heterogeneity with the The TMB-high patient group had a longer OS than the TMB-low patient group  among colorectal cancer patients receiving ICIs. In addition, the TMB-high patient group was superior in terms of ORR  compared to the TMB-low patient group.In conclusion, this meta-analysis revealed that TMB can be used as a potential predictive biomarker of colorectal cancer patients receiving ICI therapy. Nevertheless, this finding is not stable enough. Therefore, many more randomized controlled trials are needed to prove that TMB is reliable enough to be used clinically to predict the efficacy of immunotherapy in colorectal cancer. And the most relevant biomarker remains to be determined when TMB high overlaps with other biomarkers like MSI and TILs. Colorectal cancer (CRC) is a common intestinal malignancy. On the basis of the Global Cancer Epidemiological Statistics GLOBOCAN2020) released by World Health Organization, it is estimated that in 2020, the number of new cases and deaths of colorectal cancer ranked second and third among all malignant tumors, respectively . With th020 releaIn addition to the status of dMMR/MSI, scientists have identified several biomarkers, including programmed cell death ligand 1 (PD-L1) expression, tumor infiltrating lymphocytes (TILs), POLE mutation and tumor mutation load (TMB) –10. TMB The study of TMB as a predictive biomarker has made great progress in NSCLC and melanoma , 21, butThis meta-analysis conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines .We searched Ovid MEDLINE and PubMed databases up to May 1, 2021 with the following search terms:  AND  AND .Studies were independently screened and reviewed by two investigators (Li and Ma), and differences were resolved through discussion and consensus. After removing duplicate reports, we first assessed the titles and abstracts of studies for eligibility following the present inclusion criteria: (1) studies had to assess the effect of TMB in predicting the outcomes of ICIs, such as anti-PD-1, anti-PD-L1, anti-CTLA-4, their combination, or other ICIs, in colorectal cancer. (2) Studies ought to provide the TMB-related hazard ratio (HR) and its 95% confidence interval (95% CI) of overall survival (OS) or odds ratio (OR) and its 95% CI of ORR. If above conditions were not met, studies had to give Kaplan–Meier curves or original OS or ORR data to generate calculable metrics. (3) Animal studies, reviews, comments, case reports, editorials, and conference abstracts were excluded, and studies were written in English. Studies deemed eligible were enrolled after full-text view.We extracted the following data from the eligible studies: author, publication year, number of patients, type of ICI therapy, recruitment area of patients, proportion of dMMR/MSI, sample source, sequencing method of TMB, TMB cutoff value, TMB median value and its range, survival outcomes and HRs/ORs, and 95% CIs.via the Newcastle-Ottawa Scale (NOS) with scores of 0-9. Studies scores as 8-9 were recognized as high-quality studies, 5-7 indicated intermediate-quality studies and lower than 5 indicated low-quality studies and high risk of bias  and cohort studies. The quality assessment of cohort studies was performed  of bias . Specifi of bias .p-values were estimated via STATA 15.0 . Heterogeneity was also evaluated by the 2I statistic and p-value using STATA. High heterogeneity meta-analysis was conducted under a random-effects model, and low heterogeneity meta-analysis was performed under a fixed-effects model. Values of 25% < 2I < 50%, 50% < 2I <75% and 2 <I 75% suggested low heterogeneity, intermediate heterogeneity and significant heterogeneity, respectively . Similarly, we assessed the summary OR of ORR in 3 studies with 1213 patients under a fixed-effects model .Under a random-effects model, the summary HR of OS between the high and low TMB patient groups and the corresponding 95% CI were calculated based on 10 studies including 3849 patients , 36, 37.ts model , 34, 35.p = 0.053) quantitatively indicated that no publication bias existed. Sensitivity analyses suggested that after removing the study reported by Zaidi et al. , but the heterogeneity was significant . In terms of the number of patients < 100 subgroup, the TMB-high and low patient groups had no statistically significant difference  with low heterogeneity . In addition, no heterogeneity was noted between the two subgroups (p = 0.783). For the recruitment area of patients subgroup analysis, in the Western countries subgroup, the TMB-high patient group also showed superior OS , while no significant difference was found in the multiple areas subgroup . The heterogeneity of each subgroup was high  in the Western countries subgroup; and 2I = 82.7%, p < 0.001 in the multiple areas subgroup). Similarly, no heterogeneity existed between them (p = 0.400). The sequencing method subgroup analysis only included studies using NGS for the detection of TMB and excluded the study (p < 0.001) and no heterogeneity was found in this subgroup . The NGS (non-MSK-IMPACT) subgroup included the NGS (GuardantOMNI) and NGS (AmpliSeq panel) methods and had a lower OS in the TMB-high patient group  than in the NGS (MSK-IMPACT) subgroup, although the difference was not statistically significant in the NGS (non-MSK-IMPACT) subgroup. The heterogeneity was high in the NGS (non-MSK-IMPACT) subgroup  and no heterogeneity was found between the two subgroups (p = 0.362). Furthermore, we conducted subgroup analysis according to TMB cutoff. In the TMB cutoff < 28 muts/Mb subgroup, a better OS was observed in the TMB-high patient group . Besides, this subgroup showed a better response to ICIs in CRC patients with higher TMB than the TMB cutoff ≥ 28 muts/Mb subgroup . No heterogeneity was observed in both the TMB cutoff < 28 muts/Mb subgroup  and the TMB cutoff ≥ 28 muts/Mb subgroup  and the heterogeneity was significant between the two subgroups (p = 0.021).Subgroup analyses were performed on 4 aspects: number of patients, recruitment area of patients, sequencing method and TMB cutoff Table 2.he study  using WE2I = 50.5%, p = 0.059), and heterogeneity was further reduced after removal of the study reported by Zaidi et al.   . The lacTMB refers to the number of nonsynonymous mutations in somatic cells in a specific genomic region, which can indirectly reflect the ability and degree of tumors to produce neoantigens. A large number of studies have shown that TMB can predict the efficacy of immunotherapy for many kinds of tumors , 11, 38.Another factor affecting TMB detection is the source of samples. Tissue TMB (tTMB) detection is most common in clinical practice. In 2019, Samstein et al. conducted targeted NGS (MSK-IMPACT) of tumor tissues from 1662 patients with 10 advanced cancers treated with ICIs, including CRC. The results revealed that higher tTMB was responsible for better OS, and in most patients, the higher the tTMB was, the better the response to ICI treatment. This study provides strong evidence for the applicability of tTMB detection to more types of cancers . Althoug-6), which is by far the largest cohort of patients receiving ICI treatment and may be a strategy for TMB threshold screening in multiple cancers . The TMB level was significantly different in various cancer types, with a difference of more than 1,000 times, and it was also highly heterogeneous in different patients with the same cancer type; for example, in malignant melanoma and lung cancer, the level of TMB of different patients varies from 0.1 to 100 mut/Mb . In 2020 cancers . In our  cancers –50. For  cancers , Samstei cancers , while V cancers , i.e. BeIn theory, the more genetic mutations in cancer patients, the more neoantigens the cancer cells produce, and the more likely they are to be recognized by immune cells. There is a positive correlation between TMB and neoantigens, which is an important factor indicating that TMB can be used as a biomarker of ICIs. In addition to TMB, several other biomarkers also play an important role in predicting the prognosis of colorectal cancer and the efficacy of ICI. It is reported that these biomarkers overlap in varying degrees in CRC . AlthougAccording to the expert opinions on immunotherapy for patients with CRC, immunotherapy or combination therapy are not recommended for pMMR/MSS CRC patients outside the trial conditions, while some combination therapies have shown potential activity in phase I/II studies for mCRC patients with pMMR/MSS , 58. CheThis study is the first meta-analysis on the effect of TMB in the efficacy of ICI therapy in patients with colorectal cancer, which is of reference value for future studies on the association between TMB and immunotherapy and the clinical application of TMB in colorectal cancer patients. However, there are still some shortcomings in this study. Generally, TMB is still a controversial biomarker in clinical practice. There are still many problems in the standardization of TMB detection. More prospective studies are needed to verify how to select the TMB detection platform and targeted sequencing panel, how to determine a threshold screening strategy that can be applied to all kinds of tumors, whether there is a correlation between tTMB and bTMB in the field of colorectal cancer, and the independent predictive value of TMB in the Chinese population. On the other hand, studies on TMB as a predictive biomarker in immunotherapy of colorectal cancer are still insufficient. The interaction between TMB and other predictive biomarkers and the optimal prediction combination need to be verified by large scale randomized trials. Researches on TMB mainly focused on melanoma and NSCLC due to the higher tumor mutation load compared with colorectal cancer . This isOur meta-analysis demonstrated that CRC patients with high TMB can benefit from the ICI therapy, indicating that tumor mutation load can be used as another potential predictive biomarker for immunotherapy in CRC. However, this finding is not stable enough according to the results of sensitivity analysis. Hence, a large number of RCTs will be needed in the future to prove that TMB is a reliable biomarker for predicting the efficacy of immunotherapy for colorectal cancer. And the most relevant biomarker remains to be determined when TMB high overlaps with other biomarkers like MSI and TILs.The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.YL and YM designed the study. YL, YM, ZW, FZ, and JL collected the data. YM, ZW, and YZ performed the statistical analysis. YL and YM wrote the first draft of the manuscript with support from other authors. BS, SL, and MW contributed to manuscript revision. All authors contributed to the article and approved the submitted version.This work was supported by the Natural Science Foundation of China (Grant 81501462); the Chengdu International Science and Technology Cooperation Funding (Grant 2019-GH02-00074-HZ); the 1·3·5 Project for Disciplines of Excellence-Clinical Research Incubation Project, West China Hospital, Sichuan University; and the Functional and Molecular Imaging Key Laboratory of Sichuan Province (Grant 2012JO0011).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


N = 159). Rumination and worry items were loaded into separate RNT factors. Girls showed a higher use of rumination and worry and higher levels of depression and anxiety than boys. Structural equation modeling supported that both RNT factors accounted for gender differences in symptom levels: rumination was the strongest mediator for depression and worry the strongest mediator for anxiety. Our findings support both general and specific contributions of RNT to account for affective symptomatology during adolescence, particularly in girls.Rumination and worry are vulnerability factors involved in the early development of depression and anxiety during adolescence, particularly in girls. Current views conceptualize rumination and worry as transdiagnostic forms of repetitive negative thinking (RNT). However, most of research has analyzed them separately, without considering gender differences. We analyzed common and specific roles of rumination and worry in accounting for depressive and anxiety symptom levels overall and as a function of gender in adolescents (The online version contains supplementary material available at 10.1007/s41811-022-00133-1. Adolescence is a crucial developmental period, involving a profound amount of change in all individual domains: biological, cognitive, psychosocial, and emotional. Most of these changes   is a common feature of both depression and anxiety. RNT can take the form of rumination , rumination and worry present multiple formal commonalities  that have led to consider them as potential forms of a unique higher RNT transdiagnostic factor  to account for gender differences observed in depression and generalized anxiety symptoms during adolescence. We evaluated different forms of RNT, both rumination and worry, as well as depression and generalized anxiety levels in a non-clinical sample of adolescents in order to (a) determine common and specific dimensions of RNT processes of rumination and worry and its general contribution to anxiety and depression in adolescence, in line with previous research  were recruited from several high schools in Spain. The average age of the sample was 14.64 years .Indicate the gender with which you feel most identified? (boy/girl). The questionnaires used were those ones explained in the next Measures section. No other measures were used.High schools were first contacted and sent a cover letter with all the information about the research. High schools that agreed to participate were then contacted to review the protocol of data collection. Adolescents whose parents agreed that they could participate in the study received full instructions and the questionnaire package via email. First, adolescents were informed that their responses were anonymous and confidential and that participation was voluntary. All the adolescents consented to participate in the study. They then completed the questionnaires online, through a survey in Google Forms, supplied via a link in the contact email. Prior to starting the questionnaire administration, participants were informed of the importance of maintaining a quiet environment free from distractions to complete the questionnaires as well as of the importance of following the instructions for each questionnaire carefully. Before completing the questionnaires, gender was assessed asking adolescents to reply to the question: Data collection was carried out in the period between November 2019 and January 2020, before the COVID-19 pandemic. The study was conducted in accordance with the Declaration of Helsinki. Consent forms were collected both from the parents in the first place and then from the adolescents before starting their participation. The project was reviewed by the evaluation committee of the Masters’ program of Clinical Psychology of Complutense University of Madrid.Ruminative Responses Scale , as well as in terms of test–retest reliability   and an appropriate 1-month test–retest stability , and good test–retest stability 1 week later (r = 0.84). The GAD-7 has also proven to be a reliable and valid instrument for the estimation of individual differences in general anxiety levels. In this regard, the convergent validity with other general anxiety instruments such as, for example, the subscale of anxiety in the Depression, Anxiety and Stress Scale 21 (DASS)   for each symptomatology variable (dependent variables).To test hypothesis 1, analyses of bivariate correlations as well as a series of factor analyses were conducted to estimate the degree of covariance between rumination and worry, as well as to test the support for a general loading factor of RNT items into a single higher-order RNT factor vs. into specific factors of rumination and worry. In order to test hypotheses 2 and 3, correlations between rumination and worry and levels of depressive and anxiety symptomatology were computed, followed by tests for comparing the resulting correlation coefficients  to test supported RNT factors as intervening factors among gender and psychological symptom outcomes. SEM accounts for multiple accumulative relationships among variables, while handling numerous sources of variance, and allows to test the directional hypothesized relationships . RNT and symptom variables in the model comprised total scale scores. Maximum-likelihood estimation (ML) was used to estimate the model. Model fit was assessed using the chi-square goodness of fit statistic, the root mean-square error of approximation (RMSEA) , the results confirmed a significant positive correlation both between worry and the rumination total score, In order to further test common vs specific loadings of rumination and worry forms of RNT, and according to sample size in the study, an exploratory factor analysis was chosen, extracting dimensions within a pre-defined subgroup of items of each RNT scale. Regarding the rumination scale, specifically, items assessing the maladaptive subcomponent of ruminative brooding were selected, given its stronger relation to the onset and maintenance of emotional symptomatology in the previous research , selectr = .67 and p < .001, and anxiety symptom levels, r = .66 and p < .001. Similarly, worry was significantly positive correlated with both depression, r = .51 and p < .001, and anxiety symptom levels, r = .68 and p < .001.Analysis of bivariate correlation showed significant positive correlations between the rumination total score and both depression, z = −0.11 and p = .91. In contrast, hypotheses 3 was supported, with worry being significantly more related to anxiety than to depression levels, z = 2.35 and p = .02.To contrast the hypotheses 2  and 3 , tests for comparing correlation coefficients were performed  = −3.11; p =.0 02; d = .51) and anxiety (t(157) = −2.86; p = .005; d = .47), as well as in the rumination total score (t(157) = −2.54; p = .012; d = .41) and worry (t(157) = −3.51; p = .001; d = .57), were all statistically significant.To test hypothesis 4  and 5 , t tests did no longer reach significance after that RNT variables were controlled as covariates in the corresponding model, depression  = 1.91, p = .17, ηp2 = .01) and anxiety  = 0.16, p = .69, ηp2 = .01). Thus, it might be considered that the differences found between boys and girls in symptomatology outcomes could be explained by observed gender differences in both RNT dimensions. This full model was conversely tested in the final structural equation model.Provided that gender differences were supported not only in terms of symptom level measures but also in the two RNT dimensions, a series of steps were followed to test the intervening role of RNT in accounting for gender differences in symptomatology in adolescents. A preliminary step comprised analyses of covariance to test whether RNT dimensions accounted for the observed variance between genders in each symptomatology outcome. Thus, gender was introduced as the independent variable and the two dimensions of RNT — rumination total score and worry — as covariates in two separate models for depression and anxiety levels as outcomes. Results indicated that gender differences previously found in depression and anxiety outcomes in the p = .37; RMSEA = .01; NFI = .98; CFI = .99.The results of the SEM model with path coefficients  are shown in Fig. β = .20 p < .05) and worry , whereas the RNT dimensions were both significantly predictive of depression  and anxiety levels . In terms of indirect effects within the model , results are summarized in Table Gender was predictive of both differences in the rumination total score , whereas analyses showed positive significant correlations of both RNT forms  with both depressive and anxiety symptom levels, symptom specificity was supported for worry but not for rumination, in the general sample. Thus, rumination had similar a strength of associations with both depression and anxiety levels, pointing to a common role in contributing to both forms of emotional psychopathology, in general. In contrast, the contribution of worry was significantly higher for anxiety symptom than for depressive symptom levels. These results reveal the importance of worry for early anxiety onset. As for rumination, common contributions to depression and anxiety are in line with former studies supporting it as a predictive factor of both depressive and anxiety symptoms  and how they account for observed differences between girls and boys in terms of their levels of depression and anxiety experienced during adolescence (hypothesis 5). In line with previous research in these issues  and worry (PSWQ) are by definition closely linked to depressive and anxiety symptomatology, respectively. Thus, it could be argued that specificity findings might be dependent on the nature of the instruments used to index different sets of RNT processes Supplementary file2 (SAV 25 KB)Below is the link to the electronic supplementary material.


Introduction: Racial minority groups have been disproportionately affected by the 2019 novel coronavirus disease (COVID-19). Vaccine hesitancy may be a major barrier to achieving equitable herd immunity and must be addressed to reduce the excess morbidity and mortality of COVID-19 in disproportionately affected communities. This study aimed to determine if COVID-19 vaccine hesitancy, and its factors vaccine complacency and confidence, are more prominent among disproportionately affected racial minority groups.Methods:We collected data from participants aged 18 years or older from the four most populous U.S. states, including New York, California, Florida, and Texas, and Canada. Data were collected using a web-based survey platform. Data are available at http://www.covid19-database.com.Results:Data from 4,434 participants were included [mean (SD) age = 48.7 (17.2) and 50.4% women]. Vaccine hesitancy was higher in Black, Indigenous , and Latinx compared to White participants, while no difference was found between East Asian and White participants. The group differences in vaccine hesitancy for Indigenous and Black compared to White participants remained after controlling for sociodemographic factors. Determinants of vaccine complacency were equivalent between disproportionately affected racial groups and white participants. Vaccine confidence  was generally lower in all racial groups compared to White participants. Differences in vaccine mistrust comparing Black and East Asian to White participants remained after controlling for sociodemographic factors.Discussion:Disproportionately affected racial minorities may have higher vaccine hesitancy and lower confidence in COVID-19 vaccines. Public health and other relevant government services should address vaccine hesitancy among racial minorities using a culturally sensitive, community-centered approach to attain equitable herd immunity. The 2019 novel coronavirus disease (COVID-19) has disproportionately affected racial minorities in the United States (U.S.) and Canada, resulting in higher rates of infection, hospitalization and death . Black, Despite efforts to promote equitable distribution of vaccines, vaccine hesitancy is a likely barrier to achieving herd immunity and reducing the excess morbidity and mortality attributable to COVID-19. Some evidence suggests that Indigenous , Black, and Latinx individuals have higher rates of vaccine hesitancy; however, this research lacks information on the key underlying drivers –13. An elack confidence in the safety and effectiveness of the vaccine and the system recommending and providing it; (2) are complacent, in that they do not believe the vaccine-preventable disease is serious and vaccination is not necessarily required to prevent infection; and (3) perceive that access to the vaccine is inconvenient, uncomfortable or unaffordable (According to the WHO Strategic Advisory Group of Experts (SAGE), vaccine hesitancy emerges when individuals (1) fordable . The prefordable ].In a large sample of people in the U.S. and Canada, this study aimed to determine whether there are differences in vaccine hesitancy, complacency, and confidence across the following racial/ethnic groups: Indigenous, Black, Latinx, East Asian and White. We hypothesized disproportionately affected racial minority groups would have higher COVID-19 vaccine hesitancy attributable to differences in the determinants of lower vaccine confidence compared to East Asian and White participants.n = 1001), California (n = 1001), Florida (n = 501), and Texas (n = 503) and from English-speaking Canada (n = 1936). Data are available at http://www.covid19-database.com. Data were collected at three time points, in May and July 2020 using a web-based survey. The survey was developed, pre-tested, and collected using a web-based platform Dynata, a global market research company. We placed a quota restriction on age to ensure that data from a representative sample of participants from the U.S. and Canada were collected. We aimed to include approximately an equal number of respondents from the following age ranges: 18–24, 25–34, 35–44, 45–54, 55–64, and 65+ years of age from each region. Responses were collected between May to August 2020. The study was approved by our institution's Research Ethics Board (REB). All participants provided informed consent prior to starting the survey.We collected data from 4,434 participants aged 18 years or older from the four most populous U.S. states, including New York , “Black,” “Latinx” (Hispanic), “East Asian” , and “White.” These categories were chosen based on the NIH guidelines for racial and ethnic categories and by Statistics Canada , 16. Not2) statistics were performed to examine the differences in sociodemographic, vaccine hesitancy, complacency, and confidence variables between racial groups. Participants who identified as being White were used as a reference group in all pairwise comparisons. Bonferroni correction for multiple comparisons was applied and a threshold of p < 0.002  was used to establish significance. For exploratory purposes, the analyses were repeated for Canada and the United States separately. Multivariate analyses of variance (MANOVA) were subsequently performed to examine the differences in vaccine hesitancy, vaccine complacency and confidence between the racial groups, controlling for sociodemographic variables found to be significantly associated with vaccine hesitancy, including age, education, religion, region of residence, healthcare worker status, income, employment status, and political affiliation. Bonferroni correction for multiple comparisons was applied and a threshold of p < 0.003  was used to establish significance. Statistical analyses were performed using IBM SPSS Statistics . The EQUATOR Reporting Guidelines were followed. Additional survey details can be found in http://www.covid19-database.com.One-way analysis of variance (ANOVA) or chi-square (χThe mean age was 48.7 (SD = 17.2) and 50.4% of the participants were women. The majority of participants were White (74.4%). One percent of the participants were Indigenous, 4.9% Black, 11.9% East Asian, and 7.6% Latinx. Indigenous, Black, and Latinx participants were more socioeconomically disadvantaged than East Asian and White participants. Sociodemographic characteristics of the participants included in the study can be found in In the unadjusted analyses, vaccine hesitancy was significantly higher in Black, Indigenous, and Latinx compared to White participants . When coIn terms of determinants of vaccine complacency, disproportionately affected racial minority groups perceived COVID-19 with the same degree of seriousness as White participants. East Asian participants were more likely than White participants to believe they are susceptible to infectious disease and less likely to perceive the current social distancing and community restrictions to be safe and restrictive enough. East Asian participants also had a fewer number of health risk factors for COVID-19 compared to White participants. The group differences in the determinants of vaccine complacency remained when controlling for sociodemographic differences, with the exception of health risk factors for COVID-19, which became non-significant.Vaccine confidence was generally lower in all racial minority groups compared to White participants. Attitudes toward vaccinations, including mistrust in vaccine benefit , worriesAddressing vaccine hesitancy prior to the availability of vaccines for COVID-19 is essential to achieve equitable herd immunity among racial minorities who have been disproportionately affected by COVID-19. At the time of this study, only 43.7% of Indigenous, 33.4% of Black, and 56.5% of Latinx are “very probably” to “definitely” likely to get a COVID-19 vaccine, as compared to 59.6% of East Asians and 67.4% of Whites.Racial minorities had lower vaccine confidence, while no notable group differences were found in vaccine complacency. In other words, all groups viewed COVID-19 with the same degree of seriousness, yet differed in their degree of vaccine confidence. In the current sample, racial minority groups disproportionately affected by COVID-19 were more socioeconomically disadvantaged and more likely to be personally affected by COVID-19. Disproportionately affected groups had lower years of education, higher unemployment, and less income, and were also generally younger, more religious, and less conservative than White participants. Notably, group differences in vaccine hesitancy between Indigenous and Black compared to White participants remained after accounting for these sociodemographic differences. The persistence of group differences after accounting for socioeconomic disparities may reflect the historical and contemporary systemic factors that contribute to mistrust in medical interventions among racial minorities in North America. These include the Tuskegee Experiment where Black American men were deceived subjects of an observation study of untreated syphilis and the Qu'Appelle BCG Vaccine Trial in which First Nations children of the Qu'Appelle reserves in southern Saskatchewan were subjects of a vaccine trial for tuberculosis, while their impoverished living conditions were left unaddressed , 18.There are a few limitations to this study. First, only English-speaking participants who are familiar with using a computer were included. Second, the sample size for Black, Indigenous, and Latinx participants was relatively low compared to East Asian and White participants. As such, some of the null results may be attributed to a lack of statistical power. Third, we recognize that the use of racial and ethnic categorizations as employed in this study are imperfect. Participants had the opportunity to select the category they most identified with, which was felt to be the best means to overcome this limitation where only a single response option was available. Participants were also offered the option of choosing “other” if they did not feel one of the categories represented them. It is possible some participants may not identify with the nomenclature of the racial and ethnic categories and thus were not included in the study. Fourth, the study included a convenience sample and thus may not be representative of the general population. Lastly, the efficacy and the specific risks associated with the COVID-19 vaccines were unknown at the time of the study.In summary, disproportionately affected racial minority groups may have higher vaccine hesitancy, in particular lower COVID-19 vaccine confidence. If the societal objective is to ensure the equitable attainment of herd immunity among racial minority communities disproportionately affected by COVID-19, in addition to optimizing vaccine accessibility , speciahttp://www.covid19-database.com.The raw data supporting the conclusions of this article are available, without undue reservation, at All studies involving human participants are reviewed and approved by the Centre for Addiction and Mental Health. Participants provided their written informed consent to participate in this study.PG formulated the research aims, designed the research methodology, provided oversight in executing the study, and wrote the first draft of the manuscript. JK conducted statistical analyses and assisted with writing the manuscript. LQ assisted with designing the study and interpreting the data. SW assisted with designing the study and edited the manuscript. EB assisted with formulating the research aims, designing research methodology, validating research outputs, and editing the manuscript. BA assisted with formulating the research aims, interpreting the data, and edited the manuscript. BP assisted with interpreting the data and edited the manuscript. AG-G provided oversight in executing the study and edited the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the CAMH Foundation, an Academic Scholars Award from the Department of Psychiatry, University of Toronto, and Canadian Institute of Health Research (CIHR) (PJT-159807 to PG).PG reports receiving research support from the Canadian Institute of Health Research (CIHR), Ontario Ministry of Health and Long-Term Care, Ontario Mental Health Foundation (OMHF), and the Centre for Addiction and Mental Health (CAMH). AG-G has received support from the United States National Institute of Health, CIHR, OMHF, Consejo Nacional de Ciencia y Tecnologia, the Instituto de Ciencia y Tecnologia del DF, the Brain & Behavior Research Foundation (Formerly NARSAD), the Ontario Ministry of Health and Long-Term Care, the Ontario Ministry of Research and Innovation Early Research Award, and Janssen. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.