用深度学习自动区分DLBCL两种亚型，助力精准治疗
Classification and Morphological Analysis of DLBCL Subtypes in H\&E-Stained Slides
- 基于H&E染色切片的深度学习模型实现亚型分类
- 交叉验证下AUC达87.4%，正向预测值高
- 揭示亚型间形态差异细微，支持临床辅助诊断
我们针对弥漫大B细胞淋巴瘤（DLBCL）两大主要亚型——活化B细胞样（ABC）和生发中心B细胞样（GCB）的自动化分类挑战提出解决方案。准确区分这两类亚型对制定合适治疗策略至关重要，因其分子特征和治疗反应显著不同。所提出的深度学习模型在交叉验证中平均面积曲线下（AUC）达到(87.4 ± 5.7)%，展现出高阳性预测值（PPV），凸显其在临床应用中的潜力，如用于分子检测的分诊。为获取生物学见解，我们采用预训练深度神经网络分割细胞核，并对比分析了ABC与GCB亚型在几何与颜色特征上的统计差异。结果显示两类亚型在这些特征分布上差异较小，表明其视觉差异更为细微。研究结果强调了该方法在提升亚型分类精度方面的潜力，有助于改善DLBCL患者的治疗管理与预后。
原文摘要 · Abstract (English)
We address the challenge of automated classification of diffuse large B-cell lymphoma (DLBCL) into its two primary subtypes: activated B-cell-like (ABC) and germinal center B-cell-like (GCB). Accurate classification between these subtypes is essential for determining the appropriate therapeutic strategy, given their distinct molecular profiles and treatment responses. Our proposed deep learning model demonstrates robust performance, achieving an average area under the curve (AUC) of (87.4 pm 5.7)\% during cross-validation. It shows a high positive predictive value (PPV), highlighting its potential for clinical application, such as triaging for molecular testing. To gain biological insights, we performed an analysis of morphological features of ABC and GCB subtypes. We segmented cell nuclei using a pre-trained deep neural network and compared the statistics of geometric and color features for ABC and GCB. We found that the distributions of these features were not very different for the two subtypes, which suggests that the visual differences between them are more subtle. These results underscore the potential of our method to assist in more precise subtype classification and can contribute to improved treatment management and outcomes for patients of DLBCL.
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