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Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer.

ABSTRACT: Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancreatic cancer where the mortality remains high despite the current care and intense research. For radiomics, interobserver segmentation variability and its effect on radiomic feature stability is a crucial consideration. While investigations have been reported for high-contrast cancer sites such as lung cancer, no studies to date have investigated it on CT-based radiomics for pancreatic cancer. With three radiation oncology observers and three radiology observers independently contouring on the contrast CT of 21 pancreatic cancer patients, we conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer. Moreover, our novel investigation assessed whether there exists an interdisciplinary difference between the two disciplines. For each patient, a consensus tumor volume was generated using the simultaneous truth and performance level expectation algorithm, using the dice similarity coefficient (DSC) to assess each observer's delineation against the consensus volume. Radiation oncology observers showed a higher average DSC of 0.81 ± 0.06 than the radiology observers at 0.69 ± 0.16 (p = 0.002). On a panel of 1277 radiomic features, the intraclass correlation coefficients (ICC) was calculated for all observers and those of each discipline. Large variations of ICCs were observed for different radiomic features, but ICCs were generally higher for the radiation oncology group than for the radiology group. Applying a threshold of ICC > 0.75 for considering a feature as stable, 448 features (35%) were found stable for the radiation oncology group and 214 features (16%) were stable from the radiology group. Among them, 205 features were found stable for both groups. Our results provide information for interobserver segmentation variability and its effect on CT-based radiomics for pancreatic cancer. An interesting interdisciplinary variability found in this study also introduces new considerations for the deployment of radiomics models.

SUBMITTER: Wong J

PROVIDER: S-EPMC8357939 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: In this study, publicly datasets with organs at risk (OAR) structures were used as reference data to compare the differences of several observers. Convolutional neural network (CNN)-based auto-contouring was also used in the analysis. We evaluated the variations among observers and the effect of CNN-based auto-contouring in clinical applications. Materials and methods: A total of 60 publicly available lung cancer CT with structures were used; 48 cases were used for training, and the other 12 cases were used for testing. The structures of the datasets were used as reference data. Three observers and a CNN-based program performed contouring for 12 testing cases, and the 3D dice similarity coefficient (DSC) and mean surface distance (MSD) were used to evaluate differences from the reference data. The three observers edited the CNN-based contours, and the results were compared to those of manual contouring. A value of P<0.05 was considered statistically significant. Results: Compared to the reference data, no statistically significant differences were observed for the DSCs and MSDs among the manual contouring performed by the three observers at the same institution for the heart, esophagus, spinal cord, and left and right lungs. The 95% confidence interval (CI) and P-values of the CNN-based auto-contouring results comparing to the manual results for the heart, esophagus, spinal cord, and left and right lungs were as follows: the DSCs were CNN vs. A: 0.914~0.939(P = 0.004), 0.746~0.808(P = 0.002), 0.866~0.887(P = 0.136), 0.952~0.966(P = 0.158) and 0.960~0.972 (P = 0.136); CNN vs. B: 0.913~0.936 (P = 0.002), 0.745~0.807 (P = 0.005), 0.864~0.894 (P = 0.239), 0.952~0.964 (P = 0.308), and 0.959~0.971 (P = 0.272); and CNN vs. C: 0.912~0.933 (P = 0.004), 0.748~0.804(P = 0.002), 0.867~0.890 (P = 0.530), 0.952~0.964 (P = 0.308), and 0.958~0.970 (P = 0.480), respectively. The P-values of MSDs are similar to DSCs. The P-values of heart and esophagus is smaller than 0.05. No significant differences were found between the edited CNN-based auto-contouring results and the manual results. Conclusion: For the spinal cord, both lungs, no statistically significant differences were found between CNN-based auto-contouring and manual contouring. Further modifications to contouring of the heart and esophagus are necessary. Overall, editing based on CNN-based auto-contouring can effectively shorten the contouring time without affecting the results. CNNs have considerable potential for automatic contouring applications.

Project description:ObjectivesThis study aims to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas (PDAC) in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans.MethodsIn this IRB-approved and HIPAA-compliant retrospective study, 37 pairs of scans from 37 unique patients who underwent CECTs within a 2-week interval were included in the analysis of the reproducibility of features derived from pancreatic parenchyma, and a subset of 18 pairs of scans were further analyzed for the reproducibility of features derived from PDAC. In each patient, pancreatic parenchyma and pancreatic tumor (when present) were manually segmented by two radiologists independently. A total of 266 radiomic features were extracted from the pancreatic parenchyma and tumor region and also the volume and diameter of the tumor. The concordance correlation coefficient (CCC) was calculated to assess feature reproducibility for each patient in three scenarios: (1) different radiologists, same CECT; (2) same radiologist, different CECTs; and (3) different radiologists, different CECTs.ResultsAmong pancreatic parenchyma-derived features, using a threshold of CCC > 0.90, 58/266 (21.8%) and 48/266 (18.1%) features met the threshold for scenario 1, 14/266 (5.3%) and 15/266 (5.6%) for scenario 2, and 14/266 (5.3%) and 10/266 (3.8%) for scenario 3. Among pancreatic tumor-derived features, 11/268 (4.1%) and 17/268 (6.3%) features met the threshold for scenario 1, 1/268 (0.4%) and 5/268 (1.9%) features met the threshold for scenario 2, and no features for scenario 3 met the threshold, respectively.ConclusionsVariations between CECT scans affected radiomic feature reproducibility to a greater extent than variation in segmentation. A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions.Key points• For pancreatic-derived radiomic features from contrast-enhanced CT (CECT), fewer than 25% are reproducible (with a threshold of CCC < 0.9) in a clinical heterogeneous dataset. • Variations between CECT scans affected the number of reproducible radiomic features to a greater extent than variations in radiologist segmentation. • A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions.

Project description:ObjectivesThe purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.Methods96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.ResultsA total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.ConclusionsThe CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.

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Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer.

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