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The linking of image analysis, in particular radiomics, with genetic profiles, known as “imaging genomics,” has been successfully applied to many diseases and anatomic regions. The application of imaging genomics in mesothelioma, however, remains limited. Pleural mesothelioma (PM) is a devastating cancer associated with asbestos exposure and has a poor prognosis. The BRCA1-associated protein 1 (BAP1 ) gene is of considerable interest because somatic BAP1 mutations are the most common alteration associated with PM. Further, germline mutation of the BAP1 gene has been linked to PM. This study aims to explore the potential of radiomics to identify somatic BAP1 mutations and assess the feasibility of radiomics in future research in identifying germline mutations. A cohort of 149 patients with PM and known somatic BAP1 mutation status was collected, and a previously published deep learning model was used to automatically segment tumor on three representative sections from each patient’s computed tomography scans. Preprocessing, including gray-level discretization and resampling, was performed, and intensity-based and 2D texture features were extracted from the segmented tumor regions. Synthetic Minority Over-sampling Technique (SMOTE) combined with Tomek links was employed to address data imbalance. The top features were selected and used to train 18 separate machine learning models. The performance of the models in distinguishing between BAP1 -mutated (BAP1+) versus BAP1 wild-type (BAP1 -) tumors were evaluated using area under the receiver operating characteristic curve (ROC AUC) as the figure of merit. This study achieved an AUC value of 0.69 (95% confidence interval: 0.60, 0.77) using a decision tree classifier. Overall, this novel, proof-of-concept work demonstrates the potential of radiomics in differentiating between BAP1+/- in patients with PM. Future work will extend these methods to the assessment of germline BAP1 mutation status through image analysis for improved patient prognostication.
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