Intratumoral and peritumoral ultrasound-based radiomics for preoperative prediction of HER2-low breast cancer: a multicenter retrospective study

作者：Siwei Luo, Xiaobo Chen, Mengxia Yao, Yuanlin Ying, Zena Huang, Xiaoya Zhou, Zuwei Liao, Lijie Zhang, Na Hu, Chunwang Huang · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-01934-6 · 被引用次数：11 · 研究领域：Radiomics and Machine Learning in Medical Imaging、HER2/EGFR in Cancer Research、AI in cancer detection

OBJECTIVES: Recent advances in human epidermal growth factor receptor 2 (HER2)-targeted therapies have opened up new therapeutic options for HER2-low cancers. This study aimed to establish an ultrasound-based radiomics model to identify three different HER2 states noninvasively. METHODS: Between May 2018 and December 2023, a total of 1257 invasive breast cancer patients were enrolled from three hospitals. The HER2 status was divided into three classes: positive, low, and zero. Four peritumoral regions of interest (ROI) were auto-generated by dilating the manually segmented intratumoral ROI to thicknesses of 5 mm, 10 mm, 15 mm, and 20 mm. After image preprocessing, 4720 radiomics features were extracted from each image of every patient. The least absolute shrinkage and selection operator and LightBoost algorithm were utilized to construct single- and multi-region radiomics signatures (RS). A clinical-radiomics combined model was developed by integrating discriminative clinical-sonographic factors with the optimal RS. A data stitching strategy was used to build patient-level models. The Shapley additive explanations (SHAP) approach was used to explain the contribution of internal prediction. RESULTS: The optimal RS was constructed by integrating 12 tumor features and 9 peritumoral-15mm features. Age, tumor size, and seven qualitative ultrasound features were retained to construct the clinical-radiomics combined model with the optimal RS. In the training, validation, and test co...