Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study

作者：Yunfang Yu, Zifan He, Jie Ouyang, Yujie Tan, Yongjian Chen, Yang Gu, Luhui Mao, Wei Ren, Jue Wang, Lili Lin, Zhuo Wu, Jingwen Liu, Qiyun Ou, Qiugen Hu, Anlin Li, Kai Chen, Chenchen Li, Nian Lu, Xiaohong Li, Fengxi Su, Qiang Liu, Chuanmiao Xie, Herui Yao · 发表于：EBioMedicine · 年份：2021 · DOI：10.1016/j.ebiom.2021.103460 · 被引用次数：287 · 研究领域：Breast Cancer Treatment Studies、Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis

BACKGROUND: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer. METHODS: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n ...