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Case study: MesoNet

A biomarker of outcome prediction in Mesothelioma

Prognostic biomarkers
Risk Score Biomarker
Histology Data
Prognostic biomarkers
Risk Score Biomarker
Histology Data
MesoNet prognosis predictions can be used for patient stratification to reduce sample size of phase 3 trials by 6-13%.

Malignant mesothelioma patients are diagnosed and classified by pathologists via tissue biopsy. Only three FDA-approved drugs exist for mesothelioma with varying response. Novel biomarkers are needed to improve clinical care and offer possible targets for new drugs.


Owkin built MesoNet, a machine learning model that predicts the overall survival of malignant mesothelioma patients from digital pathology images better than existing subtype classification used by pathologists.


MesoNet highlighted new biomarkers predictive of prognosis in collaboration with expert mesothelioma pathologists: stroma, tumor cell localization, inflammation, cellular diversity & vascularization.


MesoNet can be used in clinical routine to aid patient prognosis, providing important input for patient management.

The novel morphological biomarkers identified in the tumour microenvironment could lead to the discovery of new druggable targets.

Published in Nature medicine Courtiol et al. 2019

"Owkin were able to identify details from the histology slides that we knew about but had never previously recognized as significant prognostic indicators.”
Professor Galateau Salle
Centre Leon Berard