Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features - Wang, Jing; Gao, Weiwei; Lu, Min; Yao, Xiaohua; Yang, Debin (Frontiers in Oncology, 2023)
Background Traditional immunohistochemistry assessment of Ki-67 in breast cancer (BC) via core needle biopsy is invasive, inaccurate, and nonrepeatable. While machine learning (ML) provides a promising alternative, its effectiveness depends on SHAP analysis identified five influential features from intra-and peritumoral ROIs, offering insight into individual prediction. Conclusion This study emphasized the potential of SVM model using radiomics features from both intra-and peritumoral US images, for predicting elevated Ki-67 levels in BC patients. The model exhibited strong performance in validations, indicating its promise as a noninvasive tool to enable personalized decision-making in BC care.