Minsun Jung, Seung Geun Song, Soo Ick Cho, Wonkyung Jung, Chiyoon Oum, Heon Song, Minuk Ma, Seonwook Park, Sergio Pereira, Sanghoon Song, Kyunghyun Paeng, Donggeun Yoo, Chan-Young Ock, Ji-Youn Sung, So-Woon Kim<\/p>\n
ASCO, 2022<\/strong><\/p>\n Background:<\/strong> Human epidermal growth factor receptor 2 (HER2) expression is a predictive marker for HER2-targeted therapy in breast cancer patients. Interobserver variation in the interpretation of HER2 levels exists among pathologists, thus a method to increase the consistency of evaluation is needed. This study aimed to evaluate the performance of the artificial intelligence (AI)-based Lunit SCOPE HER2 in assisting pathologists to evaluate HER2 expression levels in breast cancer.<\/p>\n Methods:<\/strong> Lunit SCOPE HER2 was developed with a 1.04 x 1010 \u03bcm2 area and 7.31 x 105 tumor cells from 1,133 HER2 immunohistochemistry stained whole-slide images (WSI) of breast cancer, annotated by 113 board-certified pathologists. The AI model was developed based on a semantic segmentation algorithm, which consists of two atrous spatial pyramid pooling blocks for tissue level classification and for tumor cell level classification. To validate the model, a total of 209 HER2 WSIs diagnosed with breast cancer were obtained from Kyung Hee University Hospital in Korea and were assigned as an external validation set. Three board-certified pathologists evaluated slide level HER2 expression (3+, 2+, 1+, and 0) twice, first without AI assistance and second, with it. The second reading was performed for WSIs where the pathologist's reading showed discrepancy with the AI model.<\/p>\n