MYC Rearrangement Prediction from LYSA Whole Slide Images in Large B-cell Lymphoma: A Multi-centric 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 (FISH) method which is time consuming, expensive and not available in all laboratories. Automating MYC-R detection on hematoxylin and eosin (HE) stained whole slide images (WSI) of LBCL would decrease the need for costly molecular testing and improve pathologists' productivity. We developed an interpretable deep learning (DL) algorithm to detect MYC-R considering recent advances in self-supervised learning and providing an extensive comparison of seven feature extractors and six multiple instance learning models, themselves. Four different multicentric cohorts, including 1 247 LBCL patients, were used for training and validation. The best DL model reached an average ROC AUC score of 81.9% during cross-validation on the largest LBCL cohort, and ROC AUC scores ranging from 62.2% to 74.5% when evaluated on other unseen cohorts. In addition, we demonstrated that using this model as a pre-screening tool (with a false-negative rate of 0%), FISH 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 HE WSI in daily practice.