基于免疫组化和常规组织学的深度学习黑色素瘤分类评估：一项三项中心研究
Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study
AI总结：
本文评估了基于深度学习的黑色素瘤分类在免疫组化和常规组织学切片上的性能，发现联合使用两种染色方法可提高诊断准确性。
摘要：
病理科医生通常会使用针对MelanA的免疫组化（IHC）染色组织切片，以及苏木精-伊红（H&E）染色切片，以提高对黑色素瘤的诊断准确性。在标准H&E染色的组织切片上，使用诊断深度学习（DL）支持系统进行自动组织形态和细胞组成分析已得到充分研究。然而，对IHC切片使用DL进行分析的研究较少。因此，我们研究了在MelanA和相应H&E染色切片上训练的ResNets的单独和联合性能。MelanA分类器在分布外（OOD）数据集上实现了0.82和0.74的受试者工作特征曲线面积（AUROC），与H&E基准分类的0.81和0.75相似。联合使用MelanA和H&E的分类器在OOD数据集上分别实现了0.85和0.81的AUROC。DL基于MelanA的辅助系统与基准H&E分类表现相同，可能通过多染色分类进一步改进，以帮助病理学家在临床实践中工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
