ArticleJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer2020
Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center.
Article in Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- MesoRet: a reticulin stain-based deep learning algorithm to assist diffuse mesothelioma subtyping.Pathologica · 2026Article
- The genetics of cancer heterogeneity and mesothelioma.Frontiers in oncology · 2026Review
- Advances in the Basic Sciences in Thoracic Oncology in the Last 20 Years and Their Translational Impact.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2026Review
- Review
- Molecular expression of glycosaminoglycans modifies the plasticity of biphasic mesothelioma in favor of tumor progression.Glycoconjugate journal · 2025Article
- A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images.Nature communications · 2025Article
- CDKN2A deletion is associated with immune desertification in diffuse pleural mesothelioma.Journal of experimental & clinical cancer research : CR · 2025Article
- Review
- The role of pathologists in the diagnosis of occupational lung diseases: an expert opinion of the European Society of Pathology Pulmonary Pathology Working Group.Virchows Archiv : an international journal of pathology · 2024Review
- Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images.Journal of thoracic disease · 2024Article
- From data strategy to implementation to advance cancer research and cancer care: A French comprehensive cancer center experience.PLOS digital health · 2023Article
- Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.The Journal of pathology · 2023Review
- Spatially resolved, high-dimensional transcriptomics sorts out the evolution of biphasic malignant pleural mesothelioma: new paradigms for immunotherapy.Molecular cancer · 2023Article
- Immunotherapy approaches for malignant pleural mesothelioma.Nature reviews. Clinical oncology · 2022Review
- Micro-RNA-215 and -375 regulate thymidylate synthase protein expression in pleural mesothelioma and mediate epithelial to mesenchymal transition.Virchows Archiv : an international journal of pathology · 2022Article
- ERS International Congress 2021: highlights from the Thoracic Surgery and Lung Transplantation Assembly.ERJ open research · 2022Article
- Pleural mesothelioma classification-update and challenges.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
- Molecular characterization of pleomorphic mesothelioma: a multi-institutional study.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- Solid papillary mesothelial tumor.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- Deep-learning based classification distinguishes sarcomatoid malignant mesotheliomas from benign spindle cell mesothelial proliferations.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021Article
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Abstract
introductionHistologic subtypes of malignant pleural mesothelioma are a major prognostic indicator and decision denominator for all therapeutic strategies. In an ambiguous case, a rare transitional mesothelioma (TM) pattern may be diagnosed by pathologists either as epithelioid mesothelioma (EM), biphasic mesothelioma (BM), or sarcomatoid mesothelioma (SM). This study aimed to better characterize the TM subtype from a histological, immunohistochemical, and molecular standpoint. Deep learning of pathologic slides was applied to this cohort.
methodsA random selection of 49 representative digitalized sections from surgical biopsies of TM was reviewed by 16 panelists. We evaluated BAP1 expression and CDKN2A (p16) homozygous deletion. We conducted a comprehensive, integrated, transcriptomic analysis. An unsupervised deep learning algorithm was trained to classify tumors.
resultsThe 16 panelists recorded 784 diagnoses on the 49 cases. Even though a Kappa value of 0.42 is moderate, the presence of a TM component was diagnosed in 51%. In 49% of the histological evaluation, the reviewers classified the lesion as EM in 53%, SM in 33%, or BM in 14%. Median survival was 6.7 months. Loss of BAP1 observed in 44% was less frequent in TM than in EM and BM. p16 homozygous deletion was higher in TM (73%), followed by BM (63%) and SM (46%). RNA sequencing unsupervised clustering analysis revealed that TM grouped together and were closer to SM than to EM. Deep learning analysis achieved 94% accuracy for TM identification.
conclusionThese results revealed that the TM pattern should be classified as non-EM or at minimum as a subgroup of the SM type.
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