Abstract 5058: Recurrence risk prediction based on automatic histopathologic analysis of breast cancer using whole slide images

Abstract

Abstract The RS (Recurrence Score) is the results of a 21-gene test assessing the likelihood of breast cancer recurrence for hormone-positive, HER2-negative, and node-negative breast cancer patients. We conducted an experiment to find out whether there is a correlation between the RS value derived through genetic testing and specific morphological characteristics of breast cancer tissues with the expectation that these morphological features could predict RS values. Deep learning was utilized to assess the morphology of the hematoxylin-eosin (H&E) stained whole slide images (WSIs) of breast cancer. In this study, we used 125 breast cancer cases associated with H&E stained WSIs and RS scores which were divided into 3 risk groups based on the RS values: the low-risk group with 49 cases, the intermediate-risk group with 59 cases, and the high-risk group with 17 cases (low-risk for RS < 18, high-risk for RS > 31, and intermediate-risk for RS in between). On each WSI, an expert pathologist has annotated invasive breast tumor regions. We divided WSIs into 512x512 patches and trained a deep CNN model to classify the patches as benign (including DCIS), low-risk, intermediate-risk, or high-risk. In determining the risk group of each WSI, all patches from the WSI were classified by the model first, and then their majority class was assigned to the WSI. A nested cross validation was adopted to evaluate how well the model generalizes. The performance of the algorithm is shown in Table 1. According to the results, our model was able to distinguish between the low-risk group and the high-risk group to some extent. In particular, there was no case of misclassifying the low-risk group as high-risk. Since these high-risk patients require radiological examination, there is an advantage that the 21-gene test is not required. Although our model has yielded modest results, we believe that future studies through the integration of additional data with other clinical and pathological data will yield better results. Result Table 21-gene test result (*ground truth) Low Intermediate High Prediction Low 42 14 0 Intermediate 5 44 8 High 2 1 9 Accuracy 0.932 0.776 0.912 Sensitivity 0.857 0.746 0.529 Specificity 0.816 0.803 0.972 PPV 0.851 0.772 0.750 Citation Format: Geongyu Lee, Chungyeul Kim, Tae-Yeong Kwak, Sun Woo Kim, Hyeyoon Chang. Recurrence risk prediction based on automatic histopathologic analysis of breast cancer using whole slide images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5058.