In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slides of diffuse large B-cell lymphoma. The H&E-stained slides of 287 cases from 11 hospitals were used for training and evaluation. The overall sensitivity to detect MYC rearrangement was 0.93 and the specificity 0.52, showing that prediction of MYC translocation based on morphology alone was possible in 93% of MYC-rearranged cases. This would allow a simple and fast prescreening, saving approximately 34% of genetic tests with the current algorithm.

在疑似淋巴瘤患者中，组织活检可提供淋巴瘤的确认、分类和预后因素，包括遗传变化。我们开发了一种深度学习算法来检测弥漫性大 B 细胞淋巴瘤的扫描组织学切片中的 MYC 重排。来自 11 家医院的 287 例 H&E 染色载玻片用于培训和评估。检测 MYC 重排的总体敏感性为 0.93，特异性为 0.52，表明仅基于形态学的 MYC 易位预测在 93% 的 MYC 重排病例中是可能的。这将允许简单快速的预筛选，使用当前算法节省大约 34% 的基因测试。