The deep learning (DL)-based approaches in tumor pathology help to overcome the limitations of subjective visual examination from pathologists and improve diagnostic accuracy and objectivity. However, it is unclear how a DL system trained to discriminate normal/tumor tissues in a specific cancer could perform on other tumor types. Herein, we cross-validated the DL-based normal/tumor classifiers separately trained on the tissue slides of cancers from bladder, lung, colon and rectum, stomach, bile duct, and liver. Furthermore, we compared the differences between the classifiers trained on the frozen or formalin-fixed paraffin-embedded (FFPE) tissues. The Area under the curve (AUC) for the receiver operating characteristic (ROC) curve ranged from 0.982 to 0.999 when the tissues were analyzed by the classifiers trained on the same tissue preparation modalities and cancer types. However, the AUCs could drop to 0.476 and 0.439 when the classifiers trained for different tissue modalities and cancer types were applied. Overall, the optimal performance could be achieved only when the tissue slides were analyzed by the classifiers trained on the same preparation modalities and cancer types.

中文翻译：

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深度学习系统在各种癌症病理诊断中的通用性

基于深度学习（DL）的肿瘤病理学方法有助于克服病理学家进行主观视觉检查的局限性，并提高诊断准确性和客观性。但是，尚不清楚经过训练以区分特定癌症中正常/肿瘤组织的DL系统如何在其他肿瘤类型上发挥作用。在本文中，我们交叉验证了分别在来自膀胱癌，肺癌，结肠癌和直肠癌，胃癌，胆管癌和肝癌的组织切片上训练的基于DL的正常/肿瘤分类器。此外，我们比较了在冷冻或福尔马林固定石蜡包埋（FFPE）组织上训练的分类器之间的差异。接收器工作特性（ROC）曲线的曲线下面积（AUC）为0.982至0。999时，由在相同的组织准备方式和癌症类型上受过训练的分类器对组织进行了分析。但是，当使用针对不同组织模式和癌症类型训练的分类器时，AUC可能降至0.476和0.439。总体而言，只有通过在相同制备方式和癌症类型下训练的分类器对组织玻片进行分析，才能实现最佳性能。