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
| Autor: | Andrew Churg, Jennifer L. Sauter, William D. Travis, Valerie W. Rusch, Aliya N. Husain, Francoise Thivolet, Isabelle Rouquette, Ming-Sound Tsao, Hugues Begueret, David B. Chapel, Daniel Pissaloux, Jean Claude Pairon, Gaétane Planchard, Jean Michel Vignaud, Richard Attanoos, Sophie Giusiano-Courcambeck, Alberto M. Marchevsky, Frederique Capron, Andrew G. Nicholson, Sylvie Lantuejoul, Marie Brevet, Kazuki Nabeshima, Franck Tirode, Francoise Galateau Salle, Nolwenn Le Stang, Philippe Rouvier, Pierre Courtiol, Keith M. Kerr, Sonja Klebe, Diane Damotte, Nicolas Girard, Jean Michel Picquenot, Kenzo Hiroshima, Mary Beth Beasley, Aurélie Cazes, Luka Brcic, Véronique Hofman, Matahi Moarii, Severine Paindavoine, Allen R. Gibbs, Henry D. Tazelaar, Lucian R. Chirieac, Armelle Foulet, Ruth Sequeiros, Lynnette Fernandez-Cuesta, Thomas Clozel, Christine Sagan, Charles Maussion, Sanja Dacic, Victor L. Roggli, Gilles Wainrib, Marie Christine Copin, Birgit Weynand, Francesca Damiola |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Pulmonary and Respiratory Medicine Mesothelioma Pathology medicine.medical_specialty Lung Neoplasms Histology CARCINOMA Biphasic Mesothelioma Respiratory System P16 FISH BENIGN Systemic treatment GUIDELINES 03 medical and health sciences 0302 clinical medicine Deep Learning CDKN2A BAP1 IMMUNOHISTOCHEMISTRY Medicine Humans SARCOMATOID MESOTHELIOMA Sequence Deletion BAP1 Kappa value Science & Technology business.industry Tumor Suppressor Proteins Homozygote Sarcomatoid Mesothelioma medicine.disease MALIGNANT PLEURAL MESOTHELIOMA 030104 developmental biology Oncology 030220 oncology & carcinogenesis Immunohistochemistry Surgery DIFFERENTIAL-DIAGNOSIS EXTRAPLEURAL PNEUMONECTOMY business Ubiquitin Thiolesterase Life Sciences & Biomedicine LUNG |
| Popis: | INTRODUCTION: Histologic 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. METHODS: A 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. RESULTS: The 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. CONCLUSION: These results revealed that the TM pattern should be classified as non-EM or at minimum as a subgroup of the SM type. ispartof: JOURNAL OF THORACIC ONCOLOGY vol:15 issue:6 pages:1037-1053 ispartof: location:United States status: published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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