MYC Rearrangement Prediction From LYSA Whole Slide Images in Large B-Cell Lymphoma: A Multicentric Validation of Self-supervised Deep Learning Models

Abstract

Large B-cell lymphoma (LBCL) is a heterogeneous lymphoid malignancy in which MYC gene rearrangement (MYC-R) is associated with a poor prognosis, prompting the recommendation for more intensive treatment. MYC-R detection relies on fluorescence in situ hybridization method which is time consuming, expensive, and not available in all laboratories. Automating MYC-R detection on hematoxylin-and-eosin–stained whole slide images of LBCL would decrease the need for costly molecular testing and improve pathologists’ productivity. We developed an interpretable deep learning algorithm to detect MYC-R considering recent advances in self-supervised learning and providing an extensive comparison of 7 feature extractors and 6 multiple instance learning models, themselves. Four different multicentric cohorts, including 1247 patients with LBCL, were used for training and validation. The best deep learning model reached an average area under the receiver operating characteristic curve score of 81.9% during crossvalidation on the largest LBCL cohort, and area under the receiver operating characteristic curve scores ranging from 62.2% to 74.5% when evaluated on other unseen cohorts. In addition, we demonstrated that using this model as a prescreening tool (with a false-negative rate of 0%), fluorescence in situ hybridization testing would be avoided in 35% of cases. This work demonstrates the feasibility of developing a medical device to efficiently detect MYC gene rearrangement on hematoxylin-and-eosin–stained whole slide images in daily practice.