Original articles

Vol. 118: Issue 1 - February 2026

MesoRet: a reticulin stain-based deep learning algorithm to assist diffuse mesothelioma subtyping

Authors

Giulia Orlando , Anna Paola Ferrero , Giorgia Andrea Impalà , Alessandra Pittaro , Luisa Delsedime , Rute Pedrosa , Darshan Kumar , Luisella Righi , Giuseppe Pelosi , Mauro Papotti , Eleonora Duregon

Key words: Computational Pathology, Pleural mesothelioma; Histopathology; Diagnostic practices; Immunohistochemistry; Molecular biomarkers, Deep Learining, histochemical evaluation
DOI: 10.32074/1591-951X-1583
Publication Date: 2026-03-31

Abstract

Objective. To develop and validate a deep learning model trained on reticulin-stained whole slide images (MesoRet) to accurately identify transitional features and assist in the histologic subtyping of diffuse mesothelioma.

Methods. A total of 115 cases of diffuse mesothelioma were collected from two institutions and reviewed by expert thoracic pathologists. Reticulin-stained whole-slide images were used to train a supervised deep learning model on the Aiforia Create platform to distinguish epithelioid, sarcomatoid, and transitional patterns. Model performance was validated on independent slides and compared with expert pathologists’ assessments.

Results. MesoRet accurately identified reticulin patterns across mesothelioma histotypes achieving 96.32% precision and 99.06% sensitivity, excluding artifacts and non-tumour tissue. It outperformed pathologists in identifying transitional patterns, reducing diagnostic time and minimising errors.

Conclusions. MesoRet provides an accurate and objective approach for detecting reticulin patterns in mesothelioma, supporting histological subtyping and contributing to more consistent diagnoses. Although further validation is required, it represents a promising model to improve diagnostic precision and guide therapeutic decision-making.

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Authors

Giulia Orlando - Department of Oncology, University of Turin, Turin, Italy

Anna Paola Ferrero - Department of Medical Science, University of Turin, Turin, Italy

Giorgia Andrea Impalà - Department of Medical Science, University of Turin, Turin, Italy

Alessandra Pittaro - Pathology Unit, Città della Salute e della Scienza Hospital, Turin, Italy

Luisa Delsedime - Pathology Unit, Città della Salute e della Scienza Hospital, Turin, Italy

Rute Pedrosa - Aiforia Technologies Plc, Helsinki, Finland

Darshan Kumar - Aiforia Technologies Plc, Helsinki, Finland

Luisella Righi - Department of Oncology, University of Turin, Turin, Italy

Giuseppe Pelosi - Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy

Mauro Papotti - Department of Oncology, University of Turin, Turin, Italy

Eleonora Duregon - Department of Oncology, University of Turin, Turin, Italy https://orcid.org/0000-0003-4018-3983

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright

Copyright (c) 2026 Società Italiana di Anatomia Patologica e Citopatologia Diagnostica, Divisione Italiana della International Academy of Pathology

How to Cite
Orlando, G., Ferrero, A. P. ., Impalà, G. A. ., Pittaro, A. ., Delsedime, L., Pedrosa, R. ., Kumar, D., Righi, L., Pelosi, G., Papotti, M., & Duregon, E. (2026). MesoRet: a reticulin stain-based deep learning algorithm to assist diffuse mesothelioma subtyping. Pathologica - Journal of the Italian Society of Anatomic Pathology and Diagnostic Cytopathology, 118(1). https://doi.org/10.32074/1591-951X-1583
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