Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases.

ABSTRACT: The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis r

SUBMITTER: Chen C 

PROVIDER: S-EPMC9355979 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature