AI for prognostic assessment of diffuse large B‐cell lymphoma using H&E whole‐slide images

Résumé fourni par la source

Abstract Prediction of the outcome of large B‐cell lymphomas/high‐grade B‐cell lymphomas (DLBCLs/HGBCLs) is based on clinical parameters and molecular testing, for example, for rearrangements of MYC (MYC‐R) and MYC‐R in combination with BCL2 and BCL6 translocations (double/triple hit). However, the group of DLBCL/HGBCL with poor outcome is not confined to MYC‐R lymphomas, and fluorescence in situ hybridization (FISH) testing for MYC‐R misses several high‐risk lymphomas. We aimed to understand if artificial intelligence (AI) trained to identify MYC‐R will delineate a poor prognostic subgroup. We generated a collection of digital hematoxylin and eosin (H&E)‐stained slides of DLBCL/HGBCL ( N = 2018) annotated for MYC, BCL2, and BCL6 translocations. A multiple‐instance deep learning AI model for the identification of MYC‐R alone or as double/triple hit was established using 1035 H&E‐stained slides and evaluated on an external test cohort ( N = 499). A pretrained tumor tissue classifier improved reliability and interpretability by focusing the model on tumor areas. Our model score reflects a morphological “MYCness” in DLBCL/HGBCL and demonstrates a strong association with overall survival (OS) and progression‐free survival (PFS) in the external test cohort. This was confirmed with an additional clinical test cohort ( N = 484) without FISH labels. The AI model scores correlate with various molecular features of DLBCL/HGBCL, including BCL2 and MYC gene expression, and the high‐grade gene expression signature, but also features of the tumor microenvironment. Multivariate analysis, adjusted for International Prognostic Index (IPI) factors, demonstrated the prognostic significance of our model in identifying high‐risk cases for both PFS and OS.

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