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MesoRet: a reticulin stain-based deep learning algorithm to assist diffuse mesothelioma subtyping
Giulia Orlando1, Anna Paola Ferrero2, Giorgia Andrea Impalà2
1Department of Oncology, University of Turin, Turin, Italy.
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.
