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.

Journal Article (Journal Article;Multicenter Study)

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.

Full Text

Duke Authors

Cited Authors

  • Galateau Salle, F; Le Stang, N; Tirode, F; Courtiol, P; Nicholson, AG; Tsao, M-S; Tazelaar, HD; Churg, A; Dacic, S; Roggli, V; Pissaloux, D; Maussion, C; Moarii, M; Beasley, MB; Begueret, H; Chapel, DB; Copin, MC; Gibbs, AR; Klebe, S; Lantuejoul, S; Nabeshima, K; Vignaud, J-M; Attanoos, R; Brcic, L; Capron, F; Chirieac, LR; Damiola, F; Sequeiros, R; Cazes, A; Damotte, D; Foulet, A; Giusiano-Courcambeck, S; Hiroshima, K; Hofman, V; Husain, AN; Kerr, K; Marchevsky, A; Paindavoine, S; Picquenot, JM; Rouquette, I; Sagan, C; Sauter, J; Thivolet, F; Brevet, M; Rouvier, P; Travis, WD; Planchard, G; Weynand, B; Clozel, T; Wainrib, G; Fernandez-Cuesta, L; Pairon, J-C; Rusch, V; Girard, N

Published Date

  • June 2020

Published In

Volume / Issue

  • 15 / 6

Start / End Page

  • 1037 - 1053

PubMed ID

  • 32165206

Electronic International Standard Serial Number (EISSN)

  • 1556-1380

Digital Object Identifier (DOI)

  • 10.1016/j.jtho.2020.01.025


  • eng

Conference Location

  • United States