Application of deep learning models on whole slide images uncover new histological markers related to high-risk malignant pleural mesothelioma.

e13580 Background: Malignant Pleural mesothelioma (MPM) is a highly aggressive cancer of the pleural surface and represents 80-90% of mesothelioma diagnosis. MPM is broadly subclassified into three histological subgroups (epithelioid, sarcomatoid, biphasic), however tissue heterogeneity has resulted in diagnostic challenges and suboptimal patient care. There are currently no specific histological markers of high/low-risk MPM patients, which is critical in predicting patient prognosis. Methods: Owkin developed MesoNet, a deep learning model that predicts overall survival (OS) of MPM patients from whole slide images (WSI) and trained on the French MESOBANK and TCGA cohorts (Courtiol et al, 2019). In this study, we sought to validate MesoNet’s performance on an independent cohort of 127 WSI stained with haematoxylin/eosin from MPM patients collected at the University of Pittsburgh as part of the National Mesothelioma Virtual Bank (funding by U24OH009077). Patient demographics, survival data, expertly curated pathology annotations were also collected. Results: Our analyses showed that MesoNet predicted OS as risk score based on WSI, which validated high-risk MPM patients exhibited poorer OS, as compared to low-risk patients. Analyses on histological subtypes revealed sarcomatoid and biphasic patients were overrepresented in high-risk groups, as compared to epithelioid patients, which correlates with observed OS data. Notably, histological features associated with high-risk patients revealed tumor pleomorphism and anaplastic nuclear features, whereas low-risk tiles appear to be enriched in tumor infiltrating lymphocytes (TILs) with accompanying stromal proliferation and dense fibrosis. Conclusions: Collectively, our studies validate MesoNet performance on an independent cohort and identify new features related to MPM risk groups, which may inform future treatment stratification and personalization of immunotherapies.