Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study
Cet article présente une nouvelle approche, basée sur l'utilisation d'un transformeur (modèle d'apprentissage profond constitué d'un réseau de neurones capables de traiter des données de manière séquentielle), pour prédire, à partir d'images histologiques de tumeurs colorectales, le statut des microsatellites et la présence de mutations au niveau des gènes BRAF et KRAS
Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.
Cancer Cell , article en libre accès, 2022