Prediction of immunochemotherapy response for diffuse large B ‐cell lymphoma using artificial intelligence digital pathology

作者：Jeong Hoon Lee, Ga‐Young Song, Jonghyun Lee, Sae‐Ryung Kang, Kyoung Min Moon, Yoo Duk Choi, Jeanne Shen, Myung‐Giun Noh, Deok‐Hwan Yang · 发表于：The Journal of Pathology Clinical Research · 年份：2024 · DOI：10.1002/2056-4538.12370 · 被引用次数：22 · 研究领域：Lymphoma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Colorectal and Anal Carcinomas

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict surviv...