October 22, 2019
Nature Medicine

Deep learning-based classification of mesothelioma improves prediction of patient outcome


Malignant mesothelioma (MM) is an aggressive cancer primarily diagnosed on the basis of histological criteria1. The 2015 World Health Organization classification subdivides mesothelioma tumors into three histological types: epithelioid, biphasic and sarcomatoid MM. MM is a highly complex and heterogeneous disease, rendering its diagnosis and histological typing difficult and leading to suboptimal patient care and decisions regarding treatment modalities2. Here we have developed a new approach—based on deep convolutional neural networks—called MesoNet to accurately predict the overall survival of mesothelioma patients from whole-slide digitized images, without any pathologist-provided locally annotated regions. We validated MesoNet on both an internal validation cohort from the French MESOBANK and an independent cohort from The Cancer Genome Atlas (TCGA). We also demonstrated that the model was more accurate in predicting patient survival than using current pathology practices. Furthermore, unlike classical black-box deep learning methods, MesoNet identified regions contributing to patient outcome prediction. Strikingly, we found that these regions are mainly located in the stroma and are histological features associated with inflammation, cellular diversity and vacuolization. These findings suggest that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries.

Pierre Courtiol
Charles Maussion
Matahi Moarii
Elodie Pronier
Samuel Pilcer
Meriem Sefta, PhD
Pierre Manceron
Sylvain Toldo
Mikhail Zaslavskiy
Nolwenn Le Stang
Prof. Olivier Elemento, MD, PhD
Andrew G. Nicholson
Prof. Jean-Yves Blay, MD, PhD
Francoise Galateau Sallé
Gilles Wainrib
Thomas Clozel, MD
Prof. Nicolas Girard, MD, PhD