The accurate classification of lymphoma subtypes using hematoxylin and eosin
(H&E)-stained tissue is complicated by the wide range of morphological features
these cancers can exhibit. We present LymphoML - an interpretable machine
learning method that identifies morphologic features that correlate with
lymphoma subtypes. Our method applies steps to process H&E-stained tissue
microarray cores, segment nuclei and cells, compute features encompassing
morphology, texture, and architecture, and train gradient-boosted models to
make diagnostic predictions. LymphoML's interpretable models, developed on a
limited volume of H&E-stained tissue, achieve non-inferior diagnostic accuracy
to pathologists using whole-slide images and outperform black box deep-learning
on a dataset of 670 cases from Guatemala spanning 8 lymphoma subtypes. Using
SHapley Additive exPlanation (SHAP) analysis, we assess the impact of each
feature on model prediction and find that nuclear shape features are most
discriminative for DLBCL (F1-score: 78.7%) and classical Hodgkin lymphoma
(F1-score: 74.5%). Finally, we provide the first demonstration that a model
combining features from H&E-stained tissue with features from a standardized
panel of 6 immunostains results in a similar diagnostic accuracy (85.3%) to a
46-stain panel (86.1%).

利用 H&E 染色组织精确分类淋巴瘤亚型的方法是复杂的，本研究提出了 LymphoML - 一种可解释的机器学习方法，它识别与淋巴瘤亚型相关的形态特征，通过处理 H&E 染色组织微阵列核心、分割细胞核和细胞、计算包括形态学、纹理和结构的特征，并训练梯度增强模型进行诊断预测。LymphoML 的可解释模型在有限量的 H&E 染色组织上达到了与病理学家使用全层切片图像相当的诊断准确性，并在跨越 8 个淋巴瘤亚型的 670 个病例的危地马拉数据集上胜过黑匣子深度学习。通过 SHapley Additive exPlanation (SHAP) 分析，我们评估了每个特征对模型预测的影响，并发现核形状特征对 DLBCL（F1 得分：78.7%）和典型霍奇金淋巴瘤（F1 得分：74.5%）最具鉴别性。最后，我们首次证明了将 H&E 染色组织特征与标准化的 6 种免疫染色特征相结合的模型具有类似的诊断准确性（85.3%）与 46 种免疫染色特征的模型（86.1%）。