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.

翻译：我们致力于解决弥漫性大B细胞淋巴瘤（DLBCL）自动分类为两种主要亚型——活化B细胞样（ABC）和生发中心B细胞样（GCB）——的挑战。鉴于这两种亚型具有不同的分子特征和治疗反应，对其进行准确分类对于确定合适的治疗策略至关重要。我们提出的深度学习模型展现出稳健的性能，在交叉验证期间取得了平均曲线下面积（AUC）为（87.4 ± 5.7）%的结果。该模型显示出较高的阳性预测值（PPV），突显了其在临床应用（例如分子检测分诊）中的潜力。为了获得生物学见解，我们对ABC和GCB亚型的形态学特征进行了分析。我们使用预训练的深度神经网络分割了细胞核，并比较了ABC和GCB的几何与颜色特征的统计量。我们发现，这两种亚型的这些特征分布差异不大，这表明它们之间的视觉差异更为细微。这些结果强调了我们的方法在辅助更精确的亚型分类方面的潜力，并有助于改善DLBCL患者的治疗管理和预后。