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• W4319222092 abstract "Recent advances in the management of diffuse pleural mesothelioma (DPM) have increased interest in prognostication and risk stratification on the basis that maximum benefit of combination immunotherapy appears to be seen in patients who otherwise would have the worst prognosis. Various grading schemes have been proposed, including the recently published Mesothelioma Weighted Grading Scheme (MWGS). However, predictive modelling using deep learning algorithms is increasingly regarded as the gold standard in prognostication. We therefore sought to develop and validate a prognostic nomogram for DPM. Data from 369 consecutive patients with DPM were used as independent training and validation cohorts to develop a prognostic tool that included the following variables: age, sex, histological type, nuclear atypia, mitotic count, necrosis, and BAP1 immunohistochemistry. Patients were stratified into four risk groups to assess model discrimination and calibration. To assess discrimination, the area-under-the-curve (AUC) of a receiver-operator-curve (ROC), concordance-index (C-index), and dissimilarity index (D-index) were calculated. Based on the 5-year ROC analysis, the AUC for our model was 0.75. Our model had a C-index of 0.67 (95% CI 0.53–0.79) and a D-index of 2.40 (95% CI 1.69–3.43). Our prognostic nomogram for DPM is the first of its kind, incorporates well established prognostic markers, and demonstrates excellent predictive capability. As these factors are routinely assessed in most pathology laboratories, it is hoped that this model will help inform prognostication and difficult management decisions, such as patient selection for novel therapies. This nomogram is now freely available online at: https://nomograms.shinyapps.io/Meso_Cox_ML/. Recent advances in the management of diffuse pleural mesothelioma (DPM) have increased interest in prognostication and risk stratification on the basis that maximum benefit of combination immunotherapy appears to be seen in patients who otherwise would have the worst prognosis. Various grading schemes have been proposed, including the recently published Mesothelioma Weighted Grading Scheme (MWGS). However, predictive modelling using deep learning algorithms is increasingly regarded as the gold standard in prognostication. We therefore sought to develop and validate a prognostic nomogram for DPM. Data from 369 consecutive patients with DPM were used as independent training and validation cohorts to develop a prognostic tool that included the following variables: age, sex, histological type, nuclear atypia, mitotic count, necrosis, and BAP1 immunohistochemistry. Patients were stratified into four risk groups to assess model discrimination and calibration. To assess discrimination, the area-under-the-curve (AUC) of a receiver-operator-curve (ROC), concordance-index (C-index), and dissimilarity index (D-index) were calculated. Based on the 5-year ROC analysis, the AUC for our model was 0.75. Our model had a C-index of 0.67 (95% CI 0.53–0.79) and a D-index of 2.40 (95% CI 1.69–3.43). Our prognostic nomogram for DPM is the first of its kind, incorporates well established prognostic markers, and demonstrates excellent predictive capability. As these factors are routinely assessed in most pathology laboratories, it is hoped that this model will help inform prognostication and difficult management decisions, such as patient selection for novel therapies. This nomogram is now freely available online at: https://nomograms.shinyapps.io/Meso_Cox_ML/." @default.
• W4319222092 created "2023-02-05" @default.
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• W4319222092 date "2023-06-01" @default.
• W4319222092 modified "2023-09-29" @default.
• W4319222092 title "A novel prognostic nomogram for predicting survival in diffuse pleural mesothelioma" @default.
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• W4319222092 doi "https://doi.org/10.1016/j.pathol.2022.11.009" @default.
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