S4 Dataset -

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine.

病理学家日常诊疗中，通常会在苏木精-伊红（hematoxylin and eosin, H&E）染色组织玻片的基础上，额外使用针对黑素A（MelanA）的免疫组化（immunohistochemical, IHC）染色玻片，以提升黑色素瘤的诊断准确率。基于诊断级深度学习（Deep Learning, DL）的支持系统用于自动化分析组织形态与细胞组成的研究，在标准H&E染色玻片场景中已得到充分探索；与之相对，利用深度学习分析IHC染色玻片的相关研究却较为稀缺。为此，本研究探究了分别基于MelanA染色玻片与对应H&E染色玻片训练的残差网络（ResNets）的单独性能与联合性能。MelanA分类器在分布外（out of distribution, OOD）数据集上的受试者工作特征曲线下面积（area under receiver operating characteristics curve, AUROC）分别为0.82与0.74，与基于H&E的基准分类模型的0.81、0.75表现相近。联合使用MelanA与H&E数据的分类器，在上述OOD数据集上的AUROC分别达到0.85与0.81。基于MelanA的深度学习辅助系统性能可媲美基准H&E分类模型，且可通过多染色分类技术进一步优化，从而辅助病理学家开展临床日常工作。