Bao Yongyang, Zhang Jiayi, Zhao Xingyu, Zhou Henghua, Chen Ying, Jian Junming, Shi Tianlei, Gao Xin
Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China.
J Dermatolog Treat. 2022 Aug;33(5):2571-2577. doi: 10.1080/09546634.2022.2038772. Epub 2022 Feb 10.
Erroneous diagnoses of melanocytic lesions (benign, atypical, and malignant types) result in inappropriate surgical treatment plans.
To propose a deep learning (DL)-based fully automated diagnostic method using whole slide images (WSIs) for melanocytic lesions.
The method consisted of patch prediction using a DL model and patient diagnosis using an aggregation module. The method was developed with 745 WSIs and evaluated using internal and external testing sets comprising 182 WSIs and 54 WSIs, respectively. The results were compared with those of the classification by one junior and two senior pathologists. Furthermore, we compared the performance of the three pathologists in the classification of melanocytic lesions with and without the assistance of our method.
The method achieved an accuracy of 0.963 and 0.930 on the internal and external testing sets, respectively, which was significantly higher than that of the junior pathologist (0.419 and 0.535). With assistance from the method, all three pathologists achieved higher accuracy on the internal and external testing sets; the accuracy of the junior pathologist increased by 39.0% and 30.2%, respectively ( < .05).
This generalizable method can accurately classify melanocytic lesions and effectively improve the diagnostic accuracy of pathologists.
黑素细胞性病变（良性、非典型性和恶性类型）的错误诊断会导致不适当的手术治疗方案。
提出一种基于深度学习（DL）的利用全切片图像（WSIs）对黑素细胞性病变进行全自动诊断的方法。
该方法包括使用DL模型进行图像块预测以及使用聚合模块进行患者诊断。该方法基于745张WSIs开发，并分别使用包含182张WSIs和54张WSIs的内部和外部测试集进行评估。将结果与一名初级病理学家和两名高级病理学家的分类结果进行比较。此外，我们比较了三名病理学家在有无我们方法辅助的情况下对黑素细胞性病变进行分类的表现。
该方法在内部和外部测试集上的准确率分别达到0.963和0.930，显著高于初级病理学家的准确率（0.419和0.535）。在该方法的辅助下，所有三名病理学家在内部和外部测试集上都取得了更高的准确率；初级病理学家的准确率分别提高了39.0%和30.2%（P<0.05）。
这种可推广的方法能够准确地对黑素细胞性病变进行分类，并有效提高病理学家的诊断准确率。
