The purpose of the study was the clinical implementation of a kV cone beam CT (CBCT) for setup correction in radiotherapy.For evaluation of the setup correction workflow, six tumor patients  were selected. All patients were treated with fractionated stereotactic radiotherapy, five of them with intensity modulated radiotherapy (IMRT). For patient fixation, a scotch cast body frame or a vacuum pillow, each in combination with a scotch cast head mask, were used. The imaging equipment, consisting of an x-ray tube and a flat panel imager (FPI), was attached to a Siemens linear accelerator according to the in-line approach, i.e. with the imaging beam mounted opposite to the treatment beam sharing the same isocenter. For dose delivery, the treatment beam has to traverse the FPI which is mounted in the accessory tray below the multi-leaf collimator. For each patient, a predefined number of imaging projections over a range of at least 200 degrees were acquired. The fast reconstruction of the 3D-CBCT dataset was done with an implementation of the Feldkamp-David-Kress (FDK) algorithm. For the registration of the treatment planning CT with the acquired CBCT, an automatic mutual information matcher and manual matching was used.Bony landmarks were easily detected and the table shifts for correction of setup deviations could be automatically calculated in all cases. The image quality was sufficient for a visual comparison of the desired target point with the isocenter visible on the CBCT. Soft tissue contrast was problematic for the prostate of an obese patient, but good in the lung tumor case. The detected maximum setup deviation was 3 mm for patients fixated with the body frame, and 6 mm for patients positioned in the vacuum pillow. Using an action level of 2 mm translational error, a target point correction was carried out in 4 cases. The additional workload of the described workflow compared to a normal treatment fraction led to an extra time of about 10–12 minutes, which can be further reduced by streamlining the different steps.The cone beam CT attached to a LINAC allows the acquisition of a CT scan of the patient in treatment position directly before treatment. Its image quality is sufficient for determining target point correction vectors. With the presented workflow, a target point correction within a clinically reasonable time frame is possible. This increases the treatment precision, and potentially the complex patient fixation techniques will become dispensable. A CT scan acquired for treatment planning usually represents only a single snapshot of the anatomical structures in time and is gathered several days before treatment. The shape and location of internal soft tissue structures at the time of treatment may deviate considerably from the initial scan. This problem cannot be solved by further improvements of external patient positioning like more rigid fixation devices. Especially in high precision radiotherapy, the daily position of the target needs to be confirmed before irradiation by a reliable imaging modality.Different approaches are available for three-dimensional image acquisition inside the radiation treatment room. Megavoltage CT and kilovoltage CT  has been tested so far -3. KilovUsing an in-line imaging setup attached to the gantry of the linear accelerator allows to overcome these disadvantages. Such an equipment consisting of an x-ray tube and a flat panel imager (FPI) attached to a linear accelerator (LINAC) (Siemens OCS) is available in our institution. The purpose of the present study was the clinical implementation of the kV cone beam CT (CBCT) and its application for patient setup correction in radiotherapy (RT). The paper focuses on the development of a reliable workflow from image acquisition to correction of interfraction setup deviations. We will also discuss further improvements and the potential clinical impact.For evaluation of the setup correction workflow, six tumor patients were selected. Two of them suffered from localized prostate cancer, the remaining from lung cancer, sacral chordoma, head and neck and paraspinal tumors. The patient characteristics are summarized in table All patients were treated with fractionated stereotactic RT. All except the lung cancer patient were treated with IMRT. Every patient was treated in an individually customized fixation device. Patients with prostate cancer and paraspinal tumors were immobilized by a wrap-around body cast and a head mask. For treatment of the thoracic and head-and-neck-region, a vacuum pillow was used. Both extracranial fixation devices were complemented by a head mask to eliminate head rotations which might translate into movements of the spine. Both systems were embedded in a stereotactic frame enabling stereotactic image correlation .Dose plans for both IMRT and conventional treatment planning in 3D conformal technique (CRT) were calculated using the treatment planning system Voxelplan . InverseIn daily clinical routine we normally use the available in-room CT scanner of the PRIMATOM to detect and, if necessary, correct for interfractional setup errors. For the study presented in this paper, we installed the in-line imaging equipment onto the linear accelerator of the PRIMATOM system.The integrated imaging system presented in this paper consists of a kV x-ray tube (Siemens "Optitop") and a flat panel radiation image detector (FPI) from PerkinElmer XRD 1640) attached to the Primus LINAC following the in-line approach. For this approach the diagnostic kV x-ray tube is mounted at an angle of 180 degree with respect to the therapeutic treatment beam , the pulse length (ms) and the high voltage value (kVp). These parameters are then transferred to the x-ray hardware control system. Typical acquisition parameters for a projection image were 120 kVp, 20 ms and 50 mA. For 200 projections this leads to dose of 14 mGy at the isocenter of a cylindrical water phantom with a diameter of 18 cm.To acquire a cone beam CT, the gantry of the linear accelerator rotates around the patient in treatment position at a fixed speed. An inclinometer attached to the linac's gantry generates a trigger signal for gantry angles with a fixed angle increment. This signal finally generates the x-ray pulse for one CT-image projection which is directly transferred from the detector to the reconstruction computer for further image processing.The raw images obtained from the detector are then corrected for pixel based dark image offset and detector gain structure as well as for corrupt pixels and x-ray field inhomogeneities. These corrections are performed with the help of previously stored offset and gain correction images. The offset images were acquired directly prior to the patient images, while only one gain image was acquired in the morning .A geometrical calibration is necessary due to mechanical flexibility of the x-ray tube holder and the FPI during gantry rotation. This is done using a cylindrical calibration phantom with regularly placed bullets on a helical trajectory at its periphery. The detailed calibration procedure is described in the thesis of M. Ebert .The processed images and the calibration data are transferred to an in-house developed reconstruction tool using the standard Feldkamp-David-Kress (FDK) algorithm for cone beam CT reconstruction . The outTo reconstruct a complete 3D data set of a cone beam CT, projections over a range of at least 200 degrees (180 degree + two times the fan beam angle)(Ref auf Ebert) must be acquired. This procedure is called "short scan". We used a spacing of one degree and therefore acquired 200 projections per patient.The workflow schematically shown in figure For the translations an action level of 2 mm for each axis of the translation vector was defined. The threshold for the rotation angles and the transversal shift vector were derived from the applied safety margin to the CTV during the planning process. Only if the offset components were larger than the action level, the patient was shifted to the new treatment position.The residual error after the correction of the transversal components is mainly given by the positioning precision of the table which is +-0.5 mm. Additional intrafractional variations also contribute to the remaining error, however, these were not analyzed in the present study.Figure At the current stage of development, the overall image quality and especially the soft tissue contrast of the CBCT scans do not reach the standard of dedicated diagnostic CT scans. The reduced contrast is partly due to scattered photons. For larger patients the distance between the object to image and the detector is reduced and therefore the percentage of scattered photons compared to the primary photons is increased. This problem is inherent to the cone-beam design, since collimating the photons cannot be as strict as in fan-beam CT scanners. Truncation artifacts are deteriorating the image quality further, see e.g. the outer body contour of patient 4 in fig. In all evaluated cases the threshold of 2 degree rotation was not violated. The detected maximum setup deviation was 3 mm for patients immobilized with the body frame, and 6 mm for patients positioned on a vacuum pillow. Due to the action level of 2 mm translation, a target point correction was carried out in 4 cases  and correction of tranlational errors only into the clinical workflow. In cases where the rotations exceed the threshold, it might be helpful to temporarily losen the patient's fixation and reposition him with the observed deviation in mind . Then the workflow would start again with the acquisition of a new 3D image data set. This would add another 3–5 minutes to the workflow. Another approach for better compensation of rotational errors would be not only to shift the patient but also to modify the gantry, collimator and couch angle .There is fairly strong evidence that at least patients with localized prostate cancer with intermediate to high risk benefit from higher than conventional prescribed total dose values . There iIn a first step of registration, we gave preference to bony landmarks of the pelvic region and calculated a target shift. We verified the correct position of the prostate and seminal vesicles after shifting the target point by visually assessing the image data sets. In the presented cases, there was a sufficient match (not more than 2 mm of deviation) between the prostate visible on the CT for treatment planning and the actual cone beam CT. In both cases, the irradiation could be started as intended. The process of soft tissue comparison is slightly hampered by the reduced image quality of the cone beam CT compared to the diagnostic CT. If an additional shift of the prostate would have been visible compared to the shift of bone structures, this would have been corrected manually. The manual match of soft tissue is a little elaborate due to the low contrast of the CT slices. Nevertheless, the entire procedure can be carried out within a reasonable time frame. The extra time needed is in the range of 8–10 minutes for image acquisition and setup evaluation and is prolonged for additional 2–3 minutes if a setup correction is necessary. We are currently working on matching algorithms that will enable the automatic correction of interfractional displacements of the prostate itself. Here, an improved image quality is necessary especially for obese patients.In the presented case, artifacts in the cone beam images were visible close to the plane of the head-ring of the patient fixation, where significant data loss occurred due to attenuation, and in the plane of metallic implants. Nevertheless, the image quality was sufficient to work out bone structures in almost all CT slices. Since the soft tissue of the target volume is entirely framed by bone structures, the correlation of bones is sufficient for detection of setup deviations which can be carried out for the entire data set.The target volume for head-and-neck tumors regularly includes the base of skull and extends to the upper thoracic aperture. The patients are fixated with a head mask and a vacuum pillow. The cranial part inside the head mask is very accurately repositioned during the whole treatment course. However, the location of the lower extracranial part shows more variations. The result is a complex deformation of the target volume that cannot be described by a simple translation and cannot be easily corrected by shifting the target point without changing the treatment plan . In firsFor high level adaptivity, the complex deformation of the target in head-and-neck irradiation can not be ignored. Changes of the patient's anatomy during the treatment course like weight loss or tumor response are common which require repeated treatment planning. Here, algorithms for automatic deformation of images and structures and automatic adaption of dose distribution by deformation of treatment fields and intensity maps are desirable. First promising approaches were presented by Mohan et al.  and HansDose escalation seems to be a useful strategy in treating non small cell lung cancer. A simple increase in the dose by giving additional fractions is limited by the tolerance doses of the surrounding tissue. The use of 3D-conformal radiotherapy significantly reduces doses to the spinal cord, heart and esophagus but does not improve lung sparing . Lung haUsing the mutual information matching algorithm to match the two data sets can result in a different registration where the tumor volumes might not match. This is due to the nature of the mutual information algorithm. Therefore a manual matching method with special care given to the tumor volumes was preferred.We are currently working on a method for respiration-triggered acquisition of cone beam CT slices by means of a belt sensor. This technique would enable gated IMRT correlated with respiration-triggered on-line fluoroscopy .Radiotherapy of tumors near the spine is a challenge when the required total dose exceeds the tolerance of the myelon. This is the case for malignant processes like chordoma and sarcoma, but also for metastases in case of re-irradiation when the myelon tolerance was reached by the first irradiation. In the presented cases, the myelon was spared while the surrounding tumor has to be provided with high doses. Thus, a highly precise target positioning is mandatory for paraspinal targets.On the positive side, targets near the spine are scarcely affected by intrafraction organ motion e.g. due to breathing, and a substantial distortion of the target structures does not have to be taken into account . TherefoIn this paper the first clinical applicaton of adaptive radiotherapy using an in-line cone beam CT attached to the linear accelerator was presented. We developed and tested a method for on-line target setup detection and correction for different tumor sites. The treated patients suffered from prostate, head-and-neck, paraspinal and thoracic tumors. The applied repositioning procedure was adapted to the special requirements for each tumor site. The additional workload of the described workflow compared to a normal treatment fraction leads in average to an extra time of about 10–12 minutes, which allows for clinical application of the process when high precision is recommended due to steep dose gradients. Partly responsible for the variation of the time values for image registration and setup deviation is the limited experience with the new hardware and software components. The total time for the entire process will most likely be reduced further by streamlining the different steps. The mutual information registration algorithm is relatively time consuming and might be replaced by a cross correlation registration algorithm in suitable cases. The image acquisition time is correlated with the speed of the gantry rotation which is actually limited to reduce any collision risk. Here, further shortening of the process seems to be possible. It seems realistic that the entire process of cone beam set up evaluation can be limited to 5 minutes.The cone beam CT attached to a LINAC allows the acquisition of a CT scan in treatment position just before treatment in sufficient image quality. The presented workflow allows target point correction in a reasonable amount of extra time, which might make sophisticated patient fixation techniques dispensable. As a result of the in-line geometry, this technology has the additional potential of being used for fluoroscopic tracking and targeting.CT and CTK participated in the patient treatment and drafted the manuscript. CT, UO and SN conceived of the study. TT, BH and LD participated in the design of the mounting devices and the detector. TT, SN and RB provided the used software tools, AH carried out the image registration and matching. SN, BR and PH were in charge of the approval procedure and carried out the quality assurance. JD, UO, PH participated in the study design and coordination. All authors read and approved the final manuscript.


A comparison of five different measures of pathway expression and a public map of pathway expression in human tissues are presented. Interpretation of lists of genes or proteins with altered expression is a critical and time-consuming part of microarray and proteomics research, but relatively little attention has been paid to methods for extracting biological meaning from these output lists. One powerful approach is to examine the expression of predefined biological pathways and gene sets, such as metabolic and signaling pathways and macromolecular complexes. Although many methods for measuring pathway expression have been proposed, a systematic analysis of the performance of multiple methods over multiple independent data sets has not previously been reported.Five different measures of pathway expression were compared in an analysis of nine publicly available mRNA expression data sets. The relative sensitivity of the metrics varied greatly across data sets, and the biological pathways identified for each data set are also dependent on the choice of pathway activation metric. In addition, we show that removing incoherent pathways prior to analysis improves specificity. Finally, we create and analyze a public map of pathway expression in human tissues by gene-set analysis of a large compendium of human expression data.We show that both the detection sensitivity and identity of pathways significantly perturbed in a microarray experiment are highly dependent on the analysis methods used and how incoherent pathways are treated. Analysts should thus consider using multiple approaches to test the robustness of their biological interpretations. We also provide a comprehensive picture of the tissue distribution of human gene pathways and a useful public archive of human pathway expression data. Microarray experiments typically measure mRNA populations in tissue samples and changes in those populations following perturbations. The main result of a microarray experiment is a list of genes whose expression is significantly changed relative to a comparison sample. This gene list will typically contain hundreds to thousands of genes, and biological interpretation of this list is often the most time-consuming analysis step. To interpret the set of differentially regulated genes, a scientist may order them by statistical significance or expression fold-change and then work through the list, picking out familiar genes, grouping genes that appear to have similar functions, and conducting literature searches to help understand the functions of unfamiliar genes. Eventually, most of the genes in the list are grouped and understood in terms of biological processes that have meaning to the scientist, such as the activation or repression of particular pathways or sets of genes with common function. Recent increases in available gene annotation and pathway databases have made it possible and worthwhile to complement this manual approach with automated analysis of pathway expression changes, the coordinated induction or repression of multiple genes in a predefined pathway, by reference to a database of known pathways. Here, we present and examine approaches that pre-filter gene sets in a database for correlated behavior over multiple experiments and then test the differential regulation of each gene set or pathway. In what follows, we use the terms 'pathway' and 'gene set' interchangeably.The idea of inspecting output gene lists from microarray experiments for statistical enrichment of previously annotated gene sets emerged with early microarray studies ,2. Over Here, we compare five different methods for defining pathway expression over nine publicly available mRNA expression data sets. Many pathways are identified by all methods as significantly changed. However, there are also a number of pathways that are only identified as significantly changed by a subset of the measures. These results are dependent on whether and to what extent pathways with incoherent (uncorrelated) expression  are remoWe compared the following five pathway 'activation metrics' for mapping the vector of expression values for all genes in a pathway to a scalar value representing the expression level of the pathway Figure .Z score used here represents the difference (in standard deviations) between the error-weighted mean of the expression values of the genes in a pathway and the error-weighted mean of all genes in a sample after normalization. The result reflects both the magnitude and relative direction of a gene set's expression.Suggested recently in a microarray context ,23, the p value using the hypergeometric distribution. It requires the user to define a statistical threshold for significant induction or repression. To reflect directionality, induced and repressed genes are considered separately and the more significant of the two p values, along with the appropriate sign  is used.This metric measures the enrichment of transcriptionally active genes in a gene set by calculating a The first principal component of the expression values in a gene set captures the dominant linear mode of covariation of the expression of the genes in that gene set.This metric is the mean rank of the genes in the pathway , normalized to mean zero and standard deviation one.The Kolmogorov-Smirnov (KS) statistic used here represents the maximum absolute deviation between the cumulative distribution function (CDF) of the expression values of the genes in the pathway and the CDF of all the genes in the experiment. To reflect directionality, we give a sign to the KS statistic according to whether the maximum absolute deviation arose from a positive or negative difference between the two CDFs.Separately, we developed a simple metric to quantify the degree of co-regulation, or 'coherence', of the genes in the gene set over a given set of experimental samples. Pathways whose component genes are perturbed in a correlated manner are, in general, more likely to be relevant to biological interpretation of the experimental results, while pathways whose component genes demonstrate uncorrelated, incoherent expression are less likely to be relevant to the biological meaning of the list of perturbed genes. We also explored the hypothesis that different metrics of gene set activation are more likely to give concordant results for coherent pathways than for incoherent pathways. As the measure of coherence, we use the percentage of total variance of the expression values within the gene set captured by the first principal component across all samples. Unlike some of the other possible methods for measuring coherence, this measure is not biased against gene sets whose component genes are regulated in opposing directions over the samples, as long as the relative behavior of pairs of component genes is consistent across the samples. This is not a perfect filter, however, since it may miss certain activated pathways, for example, certain signaling pathways that may not exhibit a strong transcriptional response.To test the ability of each metric to convert gene-level expression into pathway expression, we assembled a database of 1,401 annotated human pathways and gene sets: 120 from KEGG ,25, 1040p values ≤ 0.01, 0.05, 0.10, and 1.0). Sensitivities at a given FDR were averaged over all nine data sets for each of the metrics and for each coherence threshold. The combined performance results are shown in Figure Using receiver-operator characteristic (ROC) curves we measured the sensitivity of each pathway activation metric to differences between the two sample subsets in each of the nine independent data sets as a function of FDR. For comparison, we also measured the sensitivity of the expression vectors of individual genes . To test the hypothesis that coherence-filtering would affect the results, we studied each metric for its performance on gene sets of varying coherence  was the most sensitive for at least one of the data sets. In general, for data sets with very different samples and thus large numbers of genes with significant differential expression ('signature genes'), all of the metrics tended to perform well and the choice of metric is less critical. However, for data sets with lower numbers of signature genes, results were much more variable. For example, because the hypergeometric metric considers only the set of predefined signature genes, it performed poorly when there were very few such genes and should not be used in such circumstances. The other metrics take into account the expression of each gene in a pathway, regardless of whether the individual gene expression differences are above or below a threshold of significance.The relative performance of the different metrics varied widely over the data sets . The best performing metric also varied over the data sets; each of Z score performed second best, slightly outperforming the Wilcoxon Z score metric, which in turn slightly outperformed the KS metric. The sensitivity of the signed hypergeometric metric, which is arguably the most commonly applied method in gene expression analysis publications, was uniformly inferior to the other metrics and often not as sensitive as individual genes. The sensitivity of all methods declined as a function of decreasing pathway coherence, presumably because the activation signal from a coherent gene set, in which most of the genes are upregulated or downregulated in concert, is stronger than that from a set that is not coherent. The PCA activation metric is the least affected by this trend, retaining reasonable sensitivity even for incoherent gene sets . This analysis shows that often a pathway that is detected as significant for one metric is not detected as significant by another. Motivated by these differences, we further compared the similarity of the five metrics by computing the Spearman (rank) correlation between them. Using all nine data sets we measured their correlation as a function of the FDR and coherence. At low FDRs we computed the Spearman correlation using only the most strongly differentially activated gene sets, while at higher FDRs we include a progressively larger subset of the 1,401 gene sets. Interestingly, we found the correlation between metrics depended only weakly on the FDR. Table p value < 0.05. Although the exact correlation values vary with the FDR and coherence range, the Z-score, Wilcoxon, and KS metrics all had similar gene set rankings. However, the correlation of these three metrics to the PCA and hypergeometric metrics was substantially weaker. The correlation of these three with PCA was weaker still when incoherent sets were included (data not shown), indicating that pathway interpretations using different metrics are more consistent for coherent than for incoherent sets.Not only does the detection sensitivity of the metrics vary Figure , the patet al. [p value < 0.01, the Z score activation metric detects 22 activated gene sets and the PCA metric detects 41.We can conclude from the above analyses that the list of pathways significantly changed between two sets of biological samples is strongly dependent on the type of data set , the selected pathway activation metric, and whether or not 'incoherent' pathways are removed. For deeper exploration of these points, we use the data set of Farmer et al. , focusinZ score metric in the apocrine versus basal data set, almost all are related either to the cell cycle or to protein and amino acid metabolism. Compared to basal-type breast cancer samples, apocrine-type cancers demonstrate consistently lower activation of gene sets related to the cell cycle, particularly mitosis, and higher levels of activation of gene sets related to regulation of protein synthesis. The inflammatory response (presumably related to the infiltration of lymphocytes into the tumor) is lower in apocrine-type samples. If we rank pathways by the number of metrics showing a Wilcoxon rank sum p value for differential activation of <0.01, similar trends emerge; in addition, multiple sex-hormone related pathways demonstrate increased activation in apocrine-type cancers. This latter finding is consistent with the main hypothesis of Farmer et al. However, our conclusion that mitotic cell cycle pathways  are expressed at significantly higher levels in apocrine samples relative to basal samples - detected by both Z score and PCA at a FDR of <10% - is not made by Farmer et al. This indicates the potential value of using multiple methods for assessing pathway activation. Likewise, several of the pathways listed in Farmer et al. as significantly upregulated in the apocrine samples  do not pass the coherence threshold of p < 0.01. The expression of the individual genes in the sulfur metabolism pathway is shown in Supplemental Figure F11  as one example. Although the expression of several individual genes in these pathways can separate the two tumor types, the vast majority of the genes in these pathways are not differentially expressed between the two sample types, and biological conclusions about the pathways' differential expression may not be warranted.Of the 22 gene sets detected at a FDR of 10% by the As a second illustration of the methods described above, we compiled a human gene expression atlas of approximately 11,000 RefSeq transcripts in 44 normal tissues and 8 cell lines. Most of the data were obtained by re-analysis of expression data from a genome-wide scan of alternative splicing as described in Materials and methods . The datZ score did not perform as well as PCA in the combined ROC analysis described above, we selected it for further analysis and discussion of the human body atlas data because it characterizes each gene set with an intuitive interpretation as induced or repressed, provides a magnitude of activation that can be used in further analyses, had a similar pathway expression profile to PCA . The discarded sets and pathways may, of course, be actively transcribed and highly relevant in certain cell types within human tissues and yet represent only small fractions of the RNA populations within these tissues. For each coherent gene set in each tissue, we analyzed the expression level of that set using each of the five pathway activation metrics. Each resulting map is a matrix of 52 tissues and cell lines versus 290 gene sets and pathways Figure . ResultsA Figure , and is Z-score map is shown at higher resolution in Figure The The pigmentation block consists of gene sets related to melanin synthesis, expressed at high levels in retina and a melanoma cell line. The eight gene sets in the muscle block are specifically expressed in heart and skeletal muscle, and includes expected categories such as 'sarcomere' (CC), 'myofibril' (CC), and 'regulation of muscle contraction' (BP). Two sets ('muscle contraction' (BP) and 'muscle development' (BP)) are also active in smooth muscle. Interestingly, these gene sets are also expressed in the tonsil sample; this is assumed to be a contaminant from the dissection process. This contamination was much easier to identify by upregulation of a muscle-specific pathway as a whole than by inspection of individual genes, illustrating the utility of the pathway expression map for quality control of tissue samples.The energy block consists of gene sets of mitochondrial proteins, most highly expressed in striated muscle and at moderate levels in cancer cell lines, thyroid, and kidney. Activation of these energy pathways is not observed in some normal tissues with high expression of cell cycle-related gene sets, such as testis, bone marrow, and thymus. This shows that high cell turnover does not necessarily imply high levels of energy utilization. Examination of the expression of component genes from a representative pathway from this block, 'oxidative phosphorylation' (BP), demonstrates that there is coherent activation of approximately two-thirds of these genes in skeletal muscle, heart, and cell lines, accounting for the strong activation in these tissues, with only scattered activation of other genes in this GO category in other tissues . This coherently activated set of genes consists primarily of mitochondrial ATPases, and most of the apparent activation in other normal tissues, including brain, is accounted for by lysosomal (vacuolar) ATPases. The expression in thyroid is presumably related to the fact that lysosome formation is part of the pathway for cleavage of active thyroid hormone from thyroglobulin for release into the circulation. In kidney, vacuolar ATPases are essential for bicarbonate resorption in the nephron . These oThe cell-line selective block includes tRNA metabolism and proteasome subunit gene sets, indicating that certain aspects of protein biosynthesis and degradation are highly and selectively activated in malignant cells but not in normal highly proliferative tissues like bone marrow, thymus and testis. The differential expression of proteasomal genes seen here may be partly related to the increased susceptibility of cancer cells versus normal cells to proteasome inhibitors like bortezomib . The houThe collagen/smooth muscle block consists of six pathways relating to smooth muscle contraction or collagen production. As expected, these gene sets are expressed primarily in mesenchymal tissues and not expressed in brain or cell lines. The immune block consists of gene sets specific to lymphoid-derived tissues . These gene sets are expressed at high levels in lymphoid tissues and the two lymphoma cell lines studied here. These gene sets are also expressed in other tissues, particularly gut, probably representing the normal presence of lymphocytes in gastrointestinal tissue in the form of Peyer's patches.The liver-selective block contains five sub-blocks of gene sets, all of which are highly upregulated in liver and fetal liver. Some of these sub-blocks also appear to be upregulated in other tissues. For example, while the complement pathway is strongly activated in liver and fetal liver, some activity is also seen in gut and lung. Recent reports support the existence of locally, that is, extrahepatically, synthesized complement . Acute pThe hormone biosynthesis block contains genes involved in sterol biosynthesis , which includes cholesterol synthesis, and more specific pathways relating to the synthesis of C-21 steroids, such as progesterone, glucocorticoids, and mineralocorticoids . Finally, the CNS-selective block consists of a series of pathways that are largely specific to neural tissue, including corpus callosum, spinal cord, retina, and brain. These pathways cover a multitude of aspects of nerve cell growth and signaling, including nerve maturation, axonic transport and ion channels, glial cell growth and differentiation, synaptic transmission, neurotransmitter regulation, perception of pain, as well as gene sets for Alzheimer's and Parkinson's diseases. Some of the apparent expression of these pathways in other tissues arises from the properties of the GO hierarchy. For example, the Biological Process classification 'Sodium ion transport' includes both genes expressed in neurons and genes expressed in colon and renal tubules, while the 'Microtubule-based process' classification and related gene sets includes genes involved in mitosis (and thus highly expressed in cell lines) and in axoplasmic transport . Sodium and potassium transport pathways are also expressed in gut.The gene sets used in the pathway map above have relatively consistent expression of their constituent genes because we have filtered out the sets with the least coherent expression over the 52 human mRNA samples. In most of the gene sets that remain there is a large group of regulated genes and a smaller number of discordantly regulated genes. In many cases, however, a gene set passing the coherence filter still contains one or more genes with markedly different expression pattern from the global pattern. The five pathway activation metrics sometimes treat these cases differently. Three examples of this are discussed below and shown in Figure KIF11. A second major subset of genes is expressed at low levels in the proliferative tissues, but at high levels in neural tissues. This set contains genes involved in organelle transport, synaptic transmission, and synaptogenesis, like KIF1A, KIF5A, and MAP2. The rest of the genes display little correlated expression with either of the two major subsets or with each other. All of the pathway metrics consider this gene set activated, but for many samples PCA and hypergeometric disagree with the consensus of other metrics on the sign of the activation  deviates from the dominant expression pattern of the set, which shows higher expression in proliferative tissues, and instead shows high expression in brain  is a promising approach for refinement and extension of gene set and pathway membership. It should also be noted that different coherence filters may apply to each data set or experimental regime. Pathways perturbed by drug treatment or a disease state, for instance, could differ from those observed in a large set of pooled, normal tissues as presented here.Although there are set-specific differences in how the five metrics handle incoherent expression within these three gene sets, overall, the examples highlight the strong correlation of the Wilcoxon, KS, and et al. [Arguably the most common method for analyzing experimental gene lists for pathway enrichment involves setting a threshold for differential expression of genes, and then using the hypergeometric distribution to determine if there is an excess number of induced or repressed genes in a predefined pathway relative to what is expected by chance ,19,43. Aet al.  comparedZ score, and KS metrics had approximately equal results in the ROC comparison and showed a high correlation of detected pathways  and avoiding the over-fitting to a specific data set that can occur with PCA. For more subtle perturbations with limited numbers of genes differentially regulated, PCA was clearly the most sensitive and hypergeometric the least. To achieve maximum biological sensitivity and accuracy in these cases, we recommend using PCA, combined with at least one of the other three methods, Z score, KS, or Wilcoxon Z.We also showed by comparing the sensitivities of five measures of pathway expression over nine other mRNA expression data sets that conclusions about pathway activation are sensitive to the method of measurement and whether or not incoherent pathways are excluded. The most appropriate pathway analysis is, therefore, dependent on the data set being analyzed, since the subset of coherent pathways is highly variable over different experiments. We recommend using coherence filtering in all cases to select the pathways most relevant to the samples in the study. When there are large gene expression differences between samples, all of the methods performed well, with mostly similar results. In this situation, we recommend using the The nine data sets used to compare the gene-set activation metrics were selected from eight studies in the GEO database. Each data set contained two relatively homogeneous subsets of samples. One study GDS1329) provided two data sets. These subsets consisted of a baseline type and pathological samples or, in some cases, two different but related disease types. (Samples not in either subset were omitted from the comparisons.) We treated these single-channel data sets as ratio data sets by computing the median for each gene over all the baseline samples and dividing all expression values by the corresponding median and taking the base-10 logarithm. For each data set, Table 329 proviet al. [To create the human body atlas, oligonucleotide probes were placed at each exon-exon junction of 11,138 RefSeq transcripts . Purchaset al. , six of a = 100 and b = 0.2 were empirically derived from individual same-versus-same and same-versus-different hybridization experiments and represent single-hybridization, single-probe estimates of background (a) and fractional error (b). As we used multiple probes per gene and two hybridizations per sample pair (a dye-swap), final error estimates for gene expression are a combination of both propagation of this model measurement error and variance over the repeat measurements. These error estimates were then propagated to ratio and log10 ratio error estimates . Since the initial array design, NCBI has removed over 300 of the RefSeq transcripts from their databases. After removing these transcripts and any other transcripts currently unmapped to Entrez gene identifiers from our data set, the remaining 10,815 RefSeq transcripts map to 9,982 genes. Finally, using all gene-associated probes, we calculated an error-weighted average of log10 ratios for each gene in each tissue. Probe-level expression data have been deposited in the GEO database [where database  (GSE740)database .S|, we generated 1,000 random gene sets of size |S|, and calculated the distribution of coherence values. The random-set coherence distribution was approximately normal, although its mean and standard deviation were size-dependent. Of the initial 1,401 gene sets, 290 had a coherence over the human body atlas data set that was more than 2.6 standard deviations greater than the mean of the random-set distribution for that size , and these 290 sets were retained for further analysis. The mean number of genes in these 290 coherent gene sets was 33.8 ± 32.9 .We compiled 1,281 gene sets from the 1 November 2004 Release of GO , and 117 gene sets from KEGG Release 33, downloaded 11 January 2005. The mean number of genes in each set was 23.8 ± 28.5 . To build the human pathway expression map we reduced these to 290 gene sets  by applying two filters. First, we required that each gene set retained contain at least five genes and no more than 200 genes. Second, we filtered gene sets based on their coherence, the percentage of total variance of the expression values within a gene set captured by the first principal component across all tissues. This idea has been discussed previously [Some of the 290 gene sets overlap in component genes, and some gene sets are subsets of others. This is due to the hierarchical nature of GO and functional overlap with gene sets in KEGG. Rather than merge these sets we kept them all in order to maximize the functional annotation conveyed by the gene set names. To measure the overlap between two gene sets we used the average of the two ratios of the number of genes in the intersection of the two gene sets to the total number of genes in each gene set. The overlap is most significant between gene sets in the same block ranging from a low of 7% in the Cell-selective block to a high of 85% in the Hemoglobin block with a mean within-block overlap over all 14 blocks of 31%.g, let Xtg be the expression value  for gene g in tissue t. Let S be the set of genes in a pathway. For tissue t, if <XtS > and <Xt > are the mean of Xtg over the genes in S and all the genes on the microarray, respectively, and σt is the standard deviation of Xtg over all the genes on the microarray, then the Z-score activation metric used to measure the relative expression level of pathway S in tissue t is:We compared five gene-set activation metrics. Given a gene S| is the number of genes in S. The value of Z is expressed in units of standard deviation and is a measure of violation of the null hypothesis that the genes in S are independently sampled from a distribution similar to that of all the genes on the microarray. If the null hypothesis is valid, then Z will have approximately a standard normal distribution, and so a large positive value of Zt suggests collective upregulation of the genes in S (which we consider to represent 'activation' of S) in tissue t; a large negative value suggests collective downregulation. The normalization by where |Z-statistic essentially measures a shift in location (mean expression) for the genes in S, we compared its sensitivity to several other possible signed measures of location shift, which were created by modifying, where necessary, standard statistics with a sign to indicate the direction of expression change. The Wilcoxon Z statistic is a well-known statistic that is calculated according to a similar formula, but using the ranks of the Xtg among all genes in tissue t, rather than the actual fold changes. To calculate a signed KS statistic, we computed each of the two one-sided KS statistics, comparing the distribution of the expression values in S with the distribution of the genes on the microarray as a whole, and took the larger of the two statistics, with the appropriate sign. To calculate a hypergeometric p value, we used a threshold of two-fold differential expression  to define an induced or repressed gene, and then calculated the probability that the relative enrichment of differentially expressed genes observed in a gene set in a particular tissue could have been observed by chance, using the hypergeometric distribution. To provide a sign for the hypergeometric p value, the calculation was done separately for the induced and repressed genes in each set, and the smaller of the two p value was used, as well as its 'sign' . The relative insensitivity of the HG metric was little changed by varying the differential expression threshold. Finally, for the PCA statistic, we calculated PC1, the first principal component of the expression values of the genes in S across all tissues, and used the projection  of the expression values in a tissue with PC1 as a measure of activation of the gene set in that tissue.Because the p ≤ 0.01, 0.05, 0.10, and 1.0) in each of the nine GEO data sets. For each data set we compared two classes that were known to be different . Each gene set was measured in each sample in each of the two classes by each metric. We used a two-sided Wilcoxon rank sum test for equal medians to test the null hypothesis that the activation metric values for each gene set in the two classes come from distributions with equal medians. The result of this test is quantified by the returned p value. The smaller the p value, the more unlikely is the null hypothesis that the gene set median values are equal. We performed this test between the two classes for all gene sets. In a similar manner, we used the same test to compare individual gene expression values between the two classes. We used the p value from the two-sided Wilcoxon rank sum statistic to compute a false detection rate for each p value threshold using the adaptive method of Benjamini and Hochberg [We compared the five activation metrics for measuring gene set expression, and the individual genes in the expression data set, for their detection sensitivity. We applied each metric to measure the activation of the gene sets that met a coherence threshold (Hochberg  and dispHochberg . The x-aThe following additional data are available with the online version of this paper. Additional data fileSupplemental figuresClick here for fileSupplemental tablesClick here for file


While medical data are increasingly acquired in a multidimensional space, in clinical practice they are mainly still analyzed as images. We propose a wavelet-based coding technique exploiting the full dimensionality of the data distribution while allowing to recover a single image without any need to decode the whole volume. The proposed compression scheme is based on the Layered Zero Coding (LZC) method. Two modes are considered. In the progressive (PROG) mode, the volume is processed as a whole, while in the layer-per-layer (LPL) one each layer of each sub-band is encoded independently. The three-dimensional extension of the Embedded Zerotree Wavelet (EZW) coder is used as reference for coding efficiency. All working modalities provide a fully embedded bit-stream allowing a progressive by quality recovering of the encoded information.The 3D DWT is performed mapping integers to integers thus allowing lossless compression. Two different coding systems have been considered: EZW and LZC. LZC models the expected statistical dependencies among coefficients by defining some conditional terms (contexts) which summarize the significance state of the samples belonging to a generalized neighborhood of the coefficient being encoded. Such terms are then used by a context adaptive arithmetic coder. The LPL mode has been designed in order to be able to independently decode any image of the dataset, and it is derived from the PROG mode by over-constraining the system. The sub-bands are quantized and encoded according to a sequence of uniform quantizers with decreasing step-size. This ensures progressiveness capabilities when decoding both the whole volume and a single image.Performances have been evaluated on two datasets: DSR and ANGIO, an opthalmologic angiographic sequence. For each mode the best context has been retained. Results show that the proposed system is competitive with EZW, and PROG mode is the more performant. The main factors compromising compression efficiency in the LPL mode are the restriction on the choice of the contexts, and the overheading resulting from the independent coding of each layer in each sub-band. This mainly depends on the number of decomposition levels and the volume size. The isotropy of the data distribution for DSR volume results in better compression efficiency.The exploitation of the whole 3D correlation among data samples improves coding efficiency with respect to 2D systems, encoding each image independently. The number of layers to be decoded in the LPL mode in each sub-band to recover a given image is a function of the length of Wavelet filters, which makes short ones particularly suited. For the filter used the maximum this number is 4. This makes the system particularly efficient at decoding, ensuring a fast and effective access to data. Among the possible applications, it is worth citing low-rate transmission (telemedicine), archiving and remote access.


Physicians in Canadian emergency departments (EDs) annually treat 185,000 alert and stable trauma victims who are at risk for cervical spine (C-spine) injury. However, only 0.9% of these patients have suffered a cervical spine fracture. Current use of radiography is not efficient. The Canadian C-Spine Rule is designed to allow physicians to be more selective and accurate in ordering C-spine radiography, and to rapidly clear the C-spine without the need for radiography in many patients. The goal of this phase III study is to evaluate the effectiveness of an active strategy to implement the Canadian C-Spine Rule into physician practice. Specific objectives are to: 1) determine clinical impact, 2) determine sustainability, 3) evaluate performance, and 4) conduct an economic evaluation.We propose a matched-pair cluster design study that compares outcomes during three consecutive 12-months "before," "after," and "decay" periods at six pairs of "intervention" and "control" sites. These 12 hospital ED sites will be stratified as "teaching" or "community" hospitals, matched according to baseline C-spine radiography ordering rates, and then allocated within each pair to either intervention or control groups. During the "after" period at the intervention sites, simple and inexpensive strategies will be employed to actively implement the Canadian C-Spine Rule. The following outcomes will be assessed: 1) measures of clinical impact, 2) performance of the Canadian C-Spine Rule, and 3) economic measures. During the 12-month "decay" period, implementation strategies will continue, allowing us to evaluate the sustainability of the effect. We estimate a sample size of 4,800 patients in each period in order to have adequate power to evaluate the main outcomes.Phase I successfully derived the Canadian C-Spine Rule and phase II confirmed the accuracy and safety of the rule, hence, the potential for physicians to improve care. What remains unknown is the actual change in clinical behaviors that can be affected by implementation of the Canadian C-Spine Rule, and whether implementation can be achieved with simple and inexpensive measures. We believe that the Canadian C-Spine Rule has the potential to significantly reduce health care costs and improve the efficiency of patient flow in busy Canadian EDs. Physicians in Canadian emergency departments (EDs) annually treat 185,000 alert and stable trauma victims who are at risk for cervical spine (C-spine) injury (CSI). However, only 0.9% of these patients have suffered a cervical spine fracture. Current use of radiography is not efficient. More than 98% of C-spine radiographs are negative, and there is considerable variation among hospitals and physicians in radiography use. C-spine radiographs are "little ticket" items, low cost procedures that significantly add to health care costs due to high volume. In addition, alert and stable trauma patients often are immobilized on a backboard with a rigid collar and sandbags for many hours. This leads to considerable patient discomfort and to unnecessary use of valuable time and space in our crowded EDs.A clinical decision rule is derived from original research, and is defined as a decision-making tool that incorporates three or more variables from the history, examination, or simple tests. These rules help clinicians with diagnostic or therapeutic decisions at the bedside. We previously developed decision rules to allow more selective use of radiography for patients with ankle -4 and knThis protocol builds on previous funded grants to determine feasibility , phase , and is The goal of the current protocol (phase III) is to evaluate the effectiveness of an active strategy to implement the Canadian C-Spine Rule into physician practice in multiple EDs. We wish to test both the impact of the rule and the effectiveness of an implementation strategy that is inexpensive and easy to adopt. In other words, we wish to determine whether the Canadian C-Spine Rule can actually be adopted into clinical practice and whether the efficiency of patient care can be improved. Secondary objectives are to determine the sustainability of the intervention, to further evaluate the accuracy of the rule, and to conduct an economic evaluation of the potential for cost savings.Clinical decision (or prediction) rules help to reduce the uncertainty of medical decision-making by standardizing the collection and interpretation of clinical data -13. A deImplementation to demonstrate the true effect on patient care is the ultimate test of a decision rule . UnfortuOne of the most consistent findings in health services research is the uneven uptake of research across different healthcare settings, countries and specialties. Recognition of failure of traditional dissemination approaches has led to greater policy and research interest into the effectiveness and efficiency of different dissemination and implementation strategies. For example, several large studies in multiple cities have clearly demonstrated the effectiveness of implementing the Ottawa Ankle Rules ,4,28. HoThere is a growing body of rigorous evaluations of different dissemination and implementation strategies -32. GrimIn addition, Grimshaw has completed a systematic review of rigorous evaluations of guideline dissemination and implementation strategies . In all,Another important issue is sustainability. Implementation studies are often criticized because effects of the intervention are only transient, and followed by significant decay. For example, Fowkes and colleagues observed decay effects amongst four interventions to improve radiology referral in in-patient settings over 12 months . In an oThe results of these systematic reviews highlight the imperfect evidence base currently available to support decisions about which dissemination and implementation strategies are most likely to be efficient under different circumstances. Grimshaw and colleagues called for further rigorous evaluations of the effectiveness and efficiency of different dissemination and implementation interventions.In 1994 and 1995, a research formulation workshop and a funded pilot study were conducted to evaluate current practice patterns, and this demonstrated very large variation across Canada in the use of cervical spine radiography . Two maiJAMA in October 2001 . Th. Th45]. All alert and stable adults presenting to the study hospital EDs after sustaining acute blunt trauma to the head or neck will be eligible, and consecutive eligible trauma patients will be entered into the study. Patient eligibility will be determined based on these criteria at the time of arrival in the ED."Trauma to the head and neck" will include patients with either: i) neck pain with any mechanism of injury ; or ii) no neck pain, but all of the following: some visible injury above the clavicles, has not been ambulatory, and associated with a high-risk mechanism of injury ."Alert" is defined as a Glasgow Coma Scale  score of"Stable" refers to normal vital signs as defined by the Revised Trauma Score  (systoli"Acute" refers to injury within the past 48 hours.Exclusion criteria include: a) patients under the age of 16 years; b) patients who do not satisfy the definition of "trauma to the head and neck" as defined above ; c) patients with Glasgow Coma Scale score less than 15; d) patients with unstable vital signs ; e) patients whose injury occurred more than 48 hours previously; f) patients with penetrating trauma from stabbing or gunshot wound; g) patients with acute paralysis ; h) patients with known vertebral disease ; or i) patients who return for reassessment of the same injury.We are convinced that the use of the Canadian C-Spine Rule is accurate and reliable, and that the proposed study will respect patient safety at all times. Use of the rule will be encouraged, but the decision to order radiography will always be at the discretion of the attending physician, as it is at present. Physicians will know that they can "override" the rule at any time when they have concerns about patient welfare. The Canadian C-Spine Rule has proven to be very sensitive in identifying CSI and, in fact, one could argue that the rule is more accurate than Canadian emergency physicians. We do know that, in current Canadian practice without the rule, patients are being discharged from the ED with undiagnosed fractures. We expect this occurrence to be less frequent in the proposed study when the rule is available as a guide.All the respective research ethics boards have approved the study without the need for informed patient consent at the time of the ED visit. During a particular period in time at a given site, all eligible patients will be managed by the physicians in the same manner, because the unit of study allocation is the hospital, not the patient. As is typical of cluster allocated, matched-pair design studies, individual patients will not be randomized and physicians will order cervical spine radiography in a similar fashion for all patients at their site . PatientThe study setting will be six "teaching" and six large "community" hospital EDs, with a combined annual ED volume of approximately 670,000 patient visits. We believe that the generalizability of our findings will be greatly enhanced by including both teaching and community hospitals from a variety of cities  in different areas of Canada. We define a "teaching" hospital as one that is a core educational institution for a medical school's undergraduate and postgraduate students, and whose hospital staff physicians have full-time appointments to that medical school. "Community" hospitals may provide experience for some medical trainees, but the majority of patient care is provided by staff physicians who do not have fulltime appointments with a medical school.JAMA in October 2001, as well as scientific presentations at national meetings in Canada and the U.S. and a few presentations at continuing education meetings in Canada.No specific interventions will be undertaken to alter the cervical spine radiography ordering behavior of the ED physicians. These sites will exemplify the impact of "diffusion" of new medical information. The Canadian C-Spine Rule will be familiar to some clinicians because of the publication of our phase I results in We intend to pursue simple and inexpensive strategies to actively implement the use of the Canadian C-Spine Rule at the intervention sites. Therefore, we have designed an intervention that we consider is deliverable throughout Canadian settings with few additional resources.Each ED physician group will be asked to discuss and agree to a policy of ordering cervical spine radiography for alert and stable trauma patients according to the Canadian C-Spine Rule. Minor educational initiatives for the ED physicians will include the distribution of manuscripts, pocket cards, and posters, as well as a single one-hour teaching session to review the evidence and clinical application of the Canadian C-Spine Rule. The ED and Radiology departments will collaborate to institute a process-of-care modification with a mandatory "online" reminder of the Canadian C-Spine Rule at the point of requisition for cervical spine radiography. All cervical spine radiography ordered in the ED will require that the ordering physician complete a special paper or computer-based requisition that includes the Canadian C-Spine Rule algorithm criteria. The physician must "check off" the criteria, or the radiology department will not process the request. The physician may override the rule, and order radiography according to his/her clinical judgment, but will be asked to indicate the reason. Those sites that use paper requisitions will implement a new pad of special cervical spine radiography requisitions. Those sites that order radiography by computer will have an on-screen version of the rule made available by software modification.We have designed our intervention based upon theoretical considerations, currently available evidence, and discussions with collaborators. The theory of planned behavior proposes that behavior is determined by the individual's intentions to engage in a behavior, and the degree of control they feel they have over that behavior. Intention strength is determined by three variables: attitudes toward the behavior, subjective norms, and perceived behavioral control . ED physEmpirical evidence for our choice of intervention is available from the review by Solomon and colleagues . They suThe following will be collected at both the intervention and control sites during all three study periods by dedicated study personnel who will review daily patient logs, ED patient records, radiology reports, and inpatient records.Cervical spine radiography ordering proportions will be the primary study outcome, such as the proportion of eligible blunt trauma patients referred for plain cervical spine radiography during the initial ED visit. Daily patient census logs will be reviewed to identify potential injury patients, and then ED patient records  will be assessed to determine eligibility. Radiology reports and census lists will be used to determine if cervical spine radiography was performed.Number of missed CSI, such as number of clinically important CSI not identified during initial ED visit. We validated the safety of the Canadian C-Spine Rule with detailed follow-up of patients in phase II. In order to significantly reduce the resources required for phase III, we propose not to specifically follow all patients who do not undergo radiography in the ED. Telephone follow-up of patients is very labor intensive and expensive. Rather, we propose to institute a strategy of surveillance to identify the uncommon occurrence of a fracture missed because no radiography was ordered. The more common missed fracture cases due to mis-reading of the radiograph will be identified through the normal radiology department quality assurance processes. ED patient visit logs at each study site will be monitored for 30 days to identify return visits by patients who do not undergo radiography during their ED visit. In addition, we will review the neurosurgery patient logs at all neurosurgical centers that are the traditional referral sites for the study hospitals. In many cases, the regional neurosurgical centers will be our study hospitals. Application of the same surveillance approach, regardless of phase or intervention group, minimizes the likelihood of bias. We recognize that there is a very small risk of not identifying a missed fracture, but feel that this approach is pragmatic and feasible.Number of serious adverse outcomes, such as development of neurological deficit after initial assessment in ED. We will use the same surveillance approach described above for identifying missed fractures to identify the extremely rare occurrence of motor weakness and disability that develops after initial assessment in the ED.Length of stay in ED, such as the total length of stay from registration to discharge for patients who are neither admitted nor have a CSI. This will be considerably impacted by the duration of cervical spine immobilization and radiography procedures.Patient satisfaction with ED care will be determined by a random sample of 5% of "before" and "after" period patients (both those who did and those who did not receive radiography), who will be asked via telephone interview to rate their satisfaction on a five-point Likert scale at 30 days.The same clinical impact measures will be collected during the "decay" period to determine whether the effects achieved during the "after" period have been sustained.This is a secondary study outcome. The rule will be evaluated during the "after" period at the intervention sites for those cases where physicians have completed the special study requisition and checked off the rule criteria. Rule criteria are:Accuracy of the rule, such as sensitivity and specificity for identifying clinically important CSI. In interpretation of the rule, physician accuracy will be determined by comparing the physicians' notation on the radiography requisition to the "gold standard" interpretation of the rule made by the investigators' steering committee. Attention will be focused on fractures missed or potentially missed by physician misinterpretation.Physician comfort and compliance with use of the rule. On the radiography requisition, physicians will be asked to indicate their comfort in following the rule for that specific patient, using a five-point Likert scale. When physicians choose not to follow the rule, they will be asked to indicate reasons for non-compliance.The following will be evaluated for the economic impact of the C-spine rule: radiography rates after discharge will be determined by a random sample of 5% of "after" phase patients, who will be followed by telephone interview 30 days after the initial ED visit. This will ascertain if the patient has obtained cervical spine radiography through a family physician, clinic, or ED. We also will examine the length of stay in the ED , if admitted, hospital admission for CSI (as opposed to other injuries), and operative repair of CSI.p values will be two-tailed. The primary analyses will compare the "before" and "after" periods. Secondary analyses will compare the "after" and "decay" periods in order to evaluate sustainability.Every eligible patient who satisfies the inclusion and exclusion criteria during each of the three periods at all 12 sites will be included in the final analysis. No patient will be excluded due to non-compliance by the physicians or radiology departments. Sub-group analyses will evaluate teaching and community hospitals separately. Comparison of patient characteristics will be tested. All k-1 = 5 degrees of freedom) to the differences in the event rates in the intervention and control site pairs. Although the assumptions of equal variances and approximate normality may not be satisfied, empirical studies suggest that this test procedure is robust to departures from these assumptions [k-1 = 5 degrees of freedom) to the differences in the mean response between the intervention and control site pairs will be used. If the relevant information from the previous period is available, the change from baseline for each cluster will be determined and used in the calculation of the difference in the event rate or response for each intervention and control site pair.For the analysis of dichotomous data from this matched-pair design, a parametric approach will be used, based on the standard paired t-test  for each cluster will be determined, used in the calculation of the differences in the event rates (mean response) for each intervention and control site pair, and analyzed according to the above analysis plan. Clinical impact outcome variables include: cervical spine radiography ordering proportions, proportion of missed fractures, proportion of serious adverse outcomes, length of stay in ED in minutes and, patient satisfaction with the proportions indicating "very satisfied" or "satisfied."Performance of the Canadian C-Spine Rule will be evaluated by the following measures.Accuracy of the rule: The classification performance of the rule for clinically important CSI will be assessed with 95% CIs for sensitivity, specificity, negative predictive value, and positive predictive value. The "criterion interpretation" of the rule, such as positive or negative for CSI, will be made by the investigators based on the status of the patient for the component variables as documented by the physician.Physician accuracy in interpretation of the rule will be calculated as the simple agreement between the physicians' notation on the requisition – to the investigators' "criterion interpretation" of the rule.Physician comfort and compliance with use of the rule will be tabulated in a simple descriptive format.We will adopt a decision analytic approach, whereby we will identify the incremental cost savings from both a health care sector and a societal perspective of the active strategy of implementation . The modVar(dj) = 0.0066125) [k =(1.96+0.84)2(0.0066125)/.1142 = 3.9891 and the number of matched-pairs required is (7/5)(3.9891) = 5.5847 where 7/5 adjusts for the small number of degrees of freedom [Refer to the supplemental file for more details on the sample size calculation ,65-67 s. The stuHence, we will require 4,800 eligible patients at the 12 sites for each of the three study periods, for a total of 14,400 patients. We plan three consecutive 12-month periods representing, respectively, the "before," "after," and "decay" periods. Based upon our knowledge of seven sites from phase II, and extrapolating from census information of five new sites, we expect no difficulty in achieving our sample size goals. Because all eligible patients are enrolled by default, and because no consent is required, we will not lose patients to physician non-compliance nor to patient refusal.Why a matched-pair design? The matched-pair design is frequently used in community intervention studies and offers several advantages for studies like ours, in which the unit of allocation is the hospital rather than the patient. Matching on baseline data, such as radiography rates from the "before" period, helps prevent imbalance between the control and intervention groups. This design helps preserve power of analysis when relatively few (12) clusters are being studied.Why three study periods? The "before" period will provide the baseline radiography rates, which will be the basis for the matching and the baseline for measuring change. The "after" period will measure the time to onset of the effect of the intervention, as well as the maximum effect. The "decay" period will allow us to evaluate the sustainability of the effect of implementation, such as whether our simple and inexpensive implementation strategy can be expected to have a long-term effect.Will incorporation of phase II sites in phase III lead to contamination? Seven of the 12 sites participated in phase II, which will end at least six months prior to the onset of phase III. We believe there will be little or no carry-over effect on physician behavior in phase III because physicians did not apply the rule in phase II, but continued to order radiography according to their judgment. If anything, physicians ordered radiography at a higher rate (71.7%) during phase II than during the phase 0 baseline (58.0%).Blunt trauma is a very common condition that is associated with excessive and variable use of radiography, and with prolonged periods of patient immobilization in the ED. The Canadian C-Spine Rule has been derived and validated in more than 16,000 patients, and would appear to have the potential to lead to important reductions in the use of radiography, health care savings, and diminished waiting times in our crowded EDs. However, many decision rules and guidelines have no impact on health care because of inadequate dissemination strategies. What we hope to demonstrate in the proposed phase III implementation study would affect an actual change in clinical behavior. We propose to evaluate the effectiveness of implementing the Canadian C-Spine Rule, and whether such implementation can be achieved with simple and inexpensive measures. We believe that the Canadian C-Spine Rule has the potential to significantly reduce health care costs and improve the efficiency of patient flow in busy Canadian EDs.Details of Economic Analysis. The document provides a detailed description of the decision analytic model design as well as the sources of data and uncertainty.Click here for fileDetails of Sample size Calculation. The document provides a more detailed explanation of the variables and equation used to calculate the matched-pair cluster sample size.Click here for file


The post-genomic era is characterised by a torrent of biological information flooding the public databases. As a direct consequence, similarity searches starting with a single query sequence frequently lead to the identification of hundreds, or even thousands of potential homologues. The huge volume of data renders the subsequent structural, functional and evolutionary analyses very difficult. It is therefore essential to develop new strategies for efficient sampling of this large sequence space, in order to reduce the number of sequences to be processed. At the same time, it is important to retain the most pertinent sequences for structural and functional studies.An exhaustive analysis on a large scale test set (284 protein families) was performed to compare the efficiency of four different sampling methods aimed at selecting the most pertinent sequences. These four methods sample the proteins detected by BlastP searches and can be divided into two categories: two customisable methods where the user defines either the maximal number or the percentage of sequences to be selected; two automatic methods in which the number of sequences selected is determined by the program. We focused our analysis on the potential information content of the sampled sets of sequences using multiple alignment of complete sequences as the main validation tool. The study considered two criteria: the total number of sequences in BlastP and their associated E-values. The subsequent analyses investigated the influence of the sampling methods on the E-value distributions, the sequence coverage, the final multiple alignment quality and the active site characterisation at various residue conservation thresholds as a function of these criteria.The comparative analysis of the four sampling methods allows us to propose a suitable sampling strategy that significantly reduces the number of homologous sequences required for alignment, while at the same time maintaining the relevant information concerning the active site residues. Recent developments in whole genome sequencing, assembly techniques and expressed sequence tag (EST) methodsThus, sequence database mining and analysis have become essential first steps for a wide range of applications in molecular biology. One of the most widely used methods for detecting homologous sequences is Blast. The BlaAs a direct consequence of the recent database growth, Blast searches frequently lead to the identification of hundreds to thousands of potential homologues for a single query sequence. Dealing with so much data can be detrimental, not only in terms of computational and human analysis time, but also in terms of the accuracy and the significance of the results. Problems, such as sequencing or intron/exon prediction errors, redundancy or the presence of partial sequences, may represent a significant source of noise, depending on the biological question under study.a priori reduction of the sequence database search space or an a posteriori sampling of the sequences detected by the database search program. Some types of studies have intrinsic a priori sequence filters; e.g. the construction of a phylogenetic distribution of proteins from complete genomes or the analysis of proteins belonging to specific clades. Another a priori strategy is the use of a pre-processed non-redundant database, where sequences are clustered by means of their percent identities, such as the UniRef database series[a posteriori sampling methods are generally based on sequence similarity criteria and frequently require user intervention. For example, UniqueProt[It is therefore essential to develop novel strategies to reduce the set of sequences to be processed at the earliest possible stage of an analysis, which is generally during the sequence database search. There are clearly two possibilities: an se series (UniRef9niqueProt is a fasniqueProt to improMethods) which can be divided into two categories:Here we analyse a number of different methods for the sampling of protein sequences detected by BlastP searches, aimed at significantly reducing the set of sequences to be processed, while maintaining the same information potential. We have considered sampling methods only based on E-values because this parameter integrates several factors such as sequence length, subject and query similarity scores, database size, although it would be theoretically possible to take into account other factors such as the matching sequences themselves or the species from which they are derived. Four methods have been studied , or the percent reduction rate ,- two automatic methods based only on the E-values calculated by BlastP: the mean method (mm) and the second derivative method (sdm). These methods automatically determine the number of sequences in the sampled set.We focused our analysis on the potential information contained in the sampled sets of BlastP sequences, using the MACS as our main validation tool. Our analysis focused more precisely on the conservation of residues implicated in the active sites of 284 proteins with known and annotated 3D structure selected from the Protein Data Bank (PDB). A good The multi-step process used to compare the different sampling methods is shown in Figure - a BlastP search was performed in the Uniref90 database to identify the set of potential homologous sequences. Of the 284 BlastP searches, 36 detected more than 1000 sequences with E-value ≤ 0.001, which illustrates the necessity for new strategies that are capable of reducing the number of sequences to process in subsequent analyses.- each sampling method  was independently applied to the set of detected sequences, resulting in a sampled sequence set containing a reduced number of sequences.Five sets of sequences were thus associated with each initial protein: the unsampled set of sequences detected by BlastP and the 4 sampled sequence sets. These sets of sequences were then multiply aligned , resulting in five multiple alignments of complete sequences (MACS): respectively MACS_init containing the top 500 sequences detected by BlastP, MACS_mm, MACS_sdm, MACS_sm and MACS_rm.The first part of the analysis studies the reduction rate associated with the different sampling methods, depending on the initial number of sequences in the BlastP results and their E-value distribution. We also studied the amount of sequence coverage between the different methods, in order to estimate their redundancy or complementarity. The second part then studies the effect of the sampling methods on the MACS information content by considering the quality of the MACS and the conservation of documented active site residues.We studied the behaviour of the different sampling methods for a large set of diverse BlastP searches (concerning 284 protein families). We analysed the effect of the BlastP results on the ability of the sampling methods to effectively reduce the number of sequences according to two criteria: the total number of sequences detected and their E-value distribution. We also compared the sequence coverage between the different sampling methods.For each protein in the 284 protein dataset, the evaluation of the reduction rate associated with each sampling method was based on the calculation of the ratio between the number of sequences obtained after sampling and the number of sequences in the initial BlastP result file with an E-value ≤ 0.001. For each sampling method, the mean reduction rate is then defined as the average of the 284 computed reduction rates. Surprisingly, the mean reduction rate obtained with the automatic methods mm and sdm is very similar to that obtained using the customisable method sm, with all methods resulting in approximately 70% reduction in the number of sequences  is approximately twice as large as that associated with the automatic methods (both 14), suggesting that the methods may have distinct behaviours depending on the BlastP results. Also, the total number of sequences selected by each method varies significantly. Of the total 153128 sequences detected with E-value ≤ 0.001, sm selects 12108 sequences, sdm 26676 sequences, mm 28061 sequences and rm 45992 sequences. Therefore, in order to gain more insight into the influence of the BlastP results on the behaviour of the sampling methods, we divided the 284 protein dataset into three subsets of comparable size according to the total number of sequences detected by BlastP with E-value ≤ 0.001:- subset-100: 91 proteins for which BlastP detected less than 100 sequences- subset100–500: 114 proteins for which BlastP detected between 100 and 500 sequences- subset+500: 79 proteins for which BlastP detected more than 500 sequencesThis division of the initial dataset shows that the number of sequences in the BlastP results file affects the mean reduction rate associated with all of the sampling methods tested Table . A similMethods and Figure -200 to 4.10-67 while E-cluster 10 is mainly composed of sequences detected with E-values ranging from 2.10-5 to 0.001. As a consequence, when BlastP searches detect a majority of weakly related sequences , mm selects very few sequences. For the sdm method, a surprising tendency is observed: regardless of the BlastP sequence distribution, reduction rates are mostly between 60 and 70% with a slight increase for E-clusters 8 to 10. Thus, we conclude that sdm is weakly influenced by the E-value distribution. This somewhat surprising result may be due to a biased composition of the databases or may reflect a particular characteristic of the BlastP E-value calculation. The sm reduction rate is highly variable for all the E-clusters and is in fact, more closely related to the number of sequences detected by BlastP. This behaviour can be explained by the pre-defined maximal number of sequences to be selected, which is equal to the number of strips (set to 100 in our study).We therefore analysed the impact of the distribution of BlastP E-values on the sampling reduction rate. The 284 BlastP search results were categorized in 10 clusters (named E-clusters) according to the type of E-value distribution observed  as the main tool. We analysed the diversity of the sequences included in the MACS, the global quality of the MACS and the extent to which active site residues were observed in conserved columns in the different MACS.At the structural and functional level, closely related sequences may not add relevant information, whereas diversity is usually more informative. In the Methods).As the information content of a set of homologous sequences is generally related to their diversity, we investigated the SDS (Sampled Distant Sequences), whose selection increases the diversity of the sampled MACS compared to the MACS_init  had high norMD scores. Sampling by mm, sdm and rm resulted in similar proportions of high quality MACS  whereas sm increases this proportion to 95%. As the 284 protein dataset is composed of PDB sequences, it is likely to be enriched in single domain sequences. To verify that the MACS quality was not affected by the presence of multidomain proteins, we compared the norMD scores obtained with and without the multidomain proteins defined according to PFAM[Methods), results in high-quality alignments, even in the case of multidomain proteins [In order to be informative, a MACS needs to be of high quality. The quality of the alignments used in this study were evaluated using the norMD  as shown in Table In order to investigate the relationship between MACS quality and the number of sequences detected by BlastP, we also studied the quality of the MACS obtained in the three subsets of comparable size defined in the section From a quality point of view, we conclude that sm is the most appropriate sampling method since a higher proportion of good quality MACS is obtained after sampling, as well as an increased mean norMD value. Moreover, by significantly reducing the number of sequences to be aligned, the sm method also reduces the computation time required to construct the MACS.x, a column is considered to be "conserved" if x% of the residues, including gaps, are identical in the column. The sensitivity and specificity of the active site detection can then be computed (see Methods).The information content of a MACS is difficult to measure objectively. We therefore decided to investigate the residues annotated in the PDB database as being involved in functional active sites. These residues are usually well conserved in a protein family,19 and wIn this study, we only considered those tests for which the init, mm, sdm and sm all resulted in good quality MACS, which represents 192 of the 284 protein dataset. The rm method has been excluded from this study based on the results of the MACS quality analysis (see above). In subset+500, low quality MACS were obtained after rm sampling. Furthermore, preliminary analyses of active site detection using rm indicated that the mean sensitivity is much lower compared to all the other methods  is slightly reduced after sm (0.79) and a larger reduction is observed after mm and sdm (both 0.76). This decrease is caused by a loss of specificity of the active site detection, directly linked to the reduction of sequence diversity after sampling. Indeed, when only a small number of homologues are detected by BlastP, the variability between the sequences is usually relatively low. Consequently the associated MACS contains a higher proportion of conserved columns, and more false positive predictions are obtained. However, this is not a serious problem as the unsampled MACS_init alignments for this subset are generally of high quality and the small number of sequences (<100) in the BlastP results means that reduction of the sequence set is not necessary for computational purposes.For subset100–500, the most suitable column conservation threshold is 75%. The highest sensitivity for active site discrimination was obtained using sm sampling (0.89). However, the sensitivity and specificity scores are quite similar for all the sampling methods  and the differences observed between the methods cannot be considered to be significant. Nevertheless, we observed previously that in this subset, the sm sampling is more accurate in terms of reduction rate and MACS quality, and consequently sm seems to be the most suitable sampling method.Finally, for subset+500, a 80% conservation threshold was determined. The sensitivity of active site determination after mm and sdm sampling decreases drastically (0.72 and 0.68 respectively compared to 0.80 with no sampling), whereas the sensitivity and specificity of the sm sampled set are both close to the values obtained for MACS_init (Se = 0.79/Sp = 0.85 and Se = 0.80/Sp = 0.82 respectively). This leads to similar G-mean values for MACS_init and mm (0.81 and 0.80 respectively), a small decrease is observed for sdm (0.78), whereas sm shows a better G-mean value (0.82), indicating a better accuracy for active site detection. These observations correlate with the MACS quality results in which the proportion of good quality MACS is higher after application of sm sampling compared to the other methods. The study of sequence coverage showed that the mm and sdm sampled sets both contain a higher proportion of SDS compared to sm Table . MoreoveThe rapid accumulation of numerous homologues in the sequence databases is a problem for which no unique solution exists. This study demonstrates that it is possible to sample the homologous sequences detected by BlastP while at the same time retaining the relevant information concerning the active site residues inside the sampled set of sequences.We showed that on average 30% of the detected sequences are sufficient to efficiently maintain the relevant functional information, however the sequence selection cannot be performed randomly.An exhaustive analysis allowed us to define the most suitable sampling strategy depending on the number of sequences detected by BlastP searches and in conjunction with the use of a non redundant database:1. The reduction of the sequence set is not necessary with proteins having few homologues in sequence databases (less than 100). In this case, the variability between the sequences is usually relatively low and sampling the sequences results in a loss of information.2. The strips sampling (sm) is the most suitable sampling method for the effective reduction of the sequence set when more than 100 sequences are detected by BlastP searches. This method maintains the potential structural and functional information in the sampled set and by defining the maximal number of sequences (set here to 100) the computation time remains reasonable.In conclusion, regardless of the size of the initial BlastP results, our sampling strategy produces a set of sequences that is computationally and humanly manageable.In the future, we will study the conservation of other kinds of information that can be extracted from a set of homologous sequences, such as secondary structure information or motif conservation.active" or "catalytic" words were found in the associated definition. The 284 proteins consisted of a total of 96403 residues, of which 1045 represented active site residues (from 1 to 20 residues per protein).We defined a set of 284 distinct proteins, which we refer to as the "284 protein dataset" using a similar methodology to that developed by Aloy and co-workers for the The 284 protein dataset covers a large part of the protein fold space according to the CATH classifiThese 284 proteins correspond to 257 enzymes and 27 non-enzymes. According to the official Enzyme Nomenclature, they caThe full list of PDB names is available . For each individual BlastP result, the percentage of sequences in each interval was calculated and we thus obtained for each BlastP, a list of 10 values ranging from 0 to 100% characterising the E-value distribution for this BlastP search.All the 153128 sequences detected with E-value ≤ 0.001 by BlastP for the 284 protein dataset were pooled and sorted according to their respective E-values. This list was then divided into 10 equally populated intervals and the E-values corresponding to the boundaries of each interval were defined from this cutting . A threshold is defined as the mean difference between successive E-value logarithms. Let n bi) and log(Ei+1) is greater than or equal to this threshold  = Ei+1 - Ei. A sequence is selected if f" (Vi) < 0 . The second derivative of the E-value as a function of rank is computed and the sequences corresponding to its inflexion points are selected. Let V be the variation function of the E-value curve, i.e. V0 Figure .These two methods depend entirely on the BlastP E-values and are representative of the E-value dispersion. No limit is given for the number of sequences to be selected.- customisable methods:1 be the smallest E-value from the Blast search results and En the highest one (En ≤ 0.001).- the strips method (sm), for which the maximal number of sequences to be selected is fixed. The logarithmic graph of the Blast E-values is divided into a preset number x of strips of equal width (x = 100 in this study). Let EE1) - log(En))Width = Inside each non-empty strip, the sequence associated with the smallest E-value is selected Figure .- the random method (rm), for which the associated reduction rate was defined as 70%, after initial analysis of the mean reduction rates of the 3 other sampling methods. Sequences are randomly selected on this basis. This method is a control in our study, used to estimate the relevance of a selection according to the sequence dispersion.Two different tests were designed to evaluate and compare the set of sequences selected by each of the four different sampling methods.The reduction rate estimates the relationship between the total number of sequences detected by a BlastP search with an E-value ≤ 0.001 and the reduced number of sequences after sampling. For each query protein X of the 284 protein dataset:For a given sampling method, the mean reduction rate is the average of the 284 individual reduction rates:The sampling method coverage at the sequence level (coverage rate) corresponds to the number of sequences selected in common by the sampling methods. The sequence coverage was calculated by considering the number of sequences jointly selected by 2, 3 or 4 methods compared to one method chosen as the reference.Three tests were designed to evaluate the potential information content associated with a MACS.When more than 500 sequences with an E-value ≤ 0.001 are detected by Blast searches, the sampling methods may select sequences that are not present in the original MACS_init. These sequences located after the top 500 Figure  are defiThe quality of the test alignments used in this study was evaluated using the norMD  are identical at this position.The sensitivity (Se) and the specificity (Sp) of each sampling method are defined as:Where:- TP (True Positive) = number of active site residues in conserved columns,- FP  = number of non-active site residues in conserved columns,- TN (True Negative) = number of non-active site residues in non-conserved columns,- FN  = number of active site residues in non-conserved columns.A Receiver Operating Characteristic curve (ROC curThe most suitable conservation threshold for the discrimination of active site residues was determined by computing ROC curves while varying the classification threshold from 60 to 100% in the context of the MACS_init.To assess the relevance of the sampling methods in terms of MACS information content, we compared the sensitivity and specificity results obtained with and without sampling. We studied the so-called confusion matrix, which iMACS, multiple alignment of complete sequences; PDB, protein data bank; SDS, sampled distant sequences; ROC, receiver operating characteristic; AUC, area under the curve; TP, true positives; FP, false positives; TN, true negatives; FN, false negatives.AF performed the computational experiments, analyzed them and wrote the manuscript. RR and LM developed respectively the strips and the second derivative methods, and contributed to the experimental work. LM also edited the manuscript and supervised the project. NG, EB and GD provided the mean method and suggested ideas. OP initiated the project, suggested ideas and edited the manuscript. All authors read and approved the final manuscript.Sequences selected in the case of the 1QJ4 protein. Sequences detected by BlastP searches  are represented by their E-values on the graph. Sequences selected by each sampling method are schematized by their projection on horizontal axes above the graph.Click here for fileProportion of good quality MACS and mean norMD for the 284 protein dataset and restricted to the single domain proteins. These tables summarize the proportion of good quality MACS and mean norMD obtained: – for the 284 protein dataset. – for the 212 single PFAM domain proteins.Click here for fileG-mean results associated with detailed sensitivity and specificity when considering all proteins in subset+500Click here for fileList of the PDB identifier constituting the 284 protein dataset. In this list, the chain identifier corresponds to the last character, with "_" denoting no chain.Click here for file