Ultrasound‐based radiomics‐clinical nomogram for noninvasive prediction of residual cancer burden grading in breast cancer

作者：Zhiyong Li, Sheng‐Nan Wu, Zhen‐Hu Lin, Meichen Jiang, Cong Chen, Rongxi Liang, Wenjin Lin, Ensheng Xue · 发表于：Journal of Clinical Ultrasound · 年份：2024 · DOI：10.1002/jcu.23666 · 被引用次数：4 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Breast Cancer Treatment Studies、MRI in cancer diagnosis

PURPOSE: To assess the predictive value of an ultrasound-based radiomics-clinical nomogram for grading residual cancer burden (RCB) in breast cancer patients. METHODS: This retrospective study of breast cancer patients who underwent neoadjuvant therapy (NAC) and ultrasound scanning between November 2020 and July 2023. First, a radiomics model was established based on ultrasound images. Subsequently, multivariate LR (logistic regression) analysis incorporating both radiomic scores and clinical factors was performed to construct a nomogram. Finally, Receiver operating characteristics (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate and validate the diagnostic accuracy and effectiveness of the nomogram. RESULTS: A total of 1122 patients were included in this study. Among them, 427 patients exhibited a favorable response to NAC chemotherapy, while 695 patients demonstrated a poor response to NAC therapy. The radiomics model achieved an AUC value of 0.84 in the training cohort and 0.83 in the validation cohort. The ultrasound-based radiomics-clinical nomogram achieved an AUC value of 0.90 in the training cohort and 0.91 in the validation cohort. CONCLUSIONS: Ultrasound-based radiomics-clinical nomogram can accurately predict the effectiveness of NAC therapy by predicting RCB grading in breast cancer patients.