AUTHOR=Wang Jing , Gao Weiwei , Lu Min , Yao Xiaohua , Yang Debin 

TITLE=Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1290313

DOI=10.3389/fonc.2023.1290313

ISSN=2234-943X

ABSTRACT=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.