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

Tytuł:: Deep learning-based classification of mesothelioma improves prediction of patient outcome.
Autorzy:: Courtiol P; Owkin Lab, Owkin, Inc., New York, NY, USA.
Maussion C; Owkin Lab, Owkin, Inc., New York, NY, USA.
Moarii M; Owkin Lab, Owkin, Inc., New York, NY, USA.
Pronier E; Owkin Lab, Owkin, Inc., New York, NY, USA.
Pilcer S; Owkin Lab, Owkin, Inc., New York, NY, USA.
Sefta M; Owkin Lab, Owkin, Inc., New York, NY, USA.
Manceron P; Owkin Lab, Owkin, Inc., New York, NY, USA.
Toldo S; Owkin Lab, Owkin, Inc., New York, NY, USA.
Zaslavskiy M; Owkin Lab, Owkin, Inc., New York, NY, USA.
Le Stang N; Department of Biopathology, MESOPATH/MESOBANK Cancer Center Léon Bérard, Lyon, France.
Girard N; Université de Lyon, Université Claud Bernard Lyon 1, Lyon, France.; Institut du Thorax Curie-Montsouris, Institut Curie, Paris, France.
Elemento O; Department of Physiology and Biophysics, Institute for Computational Biomedicine and Caryl and Israel Englander Institute for Precision Medicine, WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
Nicholson AG; Department of Histopathology, Royal Brompton and Harefield Hospitals NHS Foundation Trust, and National Heart and Lung Institute, Imperial College, London, UK.
Blay JY; Department of Medical Oncology, Centre Léon Bérard, Lyon, France.
Galateau-Sallé F; Department of Biopathology, MESOPATH/MESOBANK Cancer Center Léon Bérard, Lyon, France.
Wainrib G; Owkin Lab, Owkin, Inc., New York, NY, USA.
Clozel T; Owkin Lab, Owkin, Inc., New York, NY, USA. .
Źródło:: Nature medicine [Nat Med] 2019 Oct; Vol. 25 (10), pp. 1519-1525. Date of Electronic Publication: 2019 Oct 07.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Publication: New York Ny : Nature Publishing Company
Original Publication: New York, NY : Nature Pub. Co., [1995-
MeSH Terms:: Prognosis*
Lung Neoplasms/*diagnosis
Lung Neoplasms/*pathology
Mesothelioma/*diagnosis
Mesothelioma/*pathology
Deep Learning ; Female ; Humans ; Lung Neoplasms/classification ; Male ; Mesothelioma/classification ; Mesothelioma, Malignant ; Neoplasm Grading ; Neural Networks, Computer
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Entry Date(s):: Date Created: 20191009 Date Completed: 20200121 Latest Revision: 20210525
Update Code:: 20240104
DOI:: 10.1038/s41591-019-0583-3
PMID:: 31591589
: Czasopismo naukowe

Malignant mesothelioma (MM) is an aggressive cancer primarily diagnosed on the basis of histological criteria 1 . 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 modalities 2 . 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.

Comment in: Nat Rev Clin Oncol. 2019 Dec;16(12):722. (PMID: 31649354)

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