文献类型：期刊文献

英文题名：Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study

作者：Lin, Guihan[1];Chen, Weiyue[1];Fan, Yingying[1,5];Zhou, Yi[1,3];Li, Xia[1,3];Hu, Xin[1,4];Cheng, Xue[1,3];Chen, Mingzhen[1,2];Kong, Chunli[1,3];Chen, Minjiang[1,3];Xu, Min[1,3,4];Peng, Zhiyi[1,2,3];Ji, Jiansong[1,4]

机构：[1]Wenzhou Med Univ, Zhejiang Engn Res Ctr Intervent Med Engn & Biotech, Key Lab Imaging Diag & Minimally Invas Intervent R, Key Lab Precis Med Lishui City,Affiliated Hosp 1, Lishui 323000, Peoples R China;[2]Zhejiang Univ, Affiliated Hosp 1, Sch Med, Dept Radiol, Hangzhou 310009, Peoples R China;[3]Lishui Univ, Affiliated Cent Hosp, Clin Coll, Sch Med, Lishui 323000, Peoples R China;[4]Shaoxing Univ, Sch Med, Shaoxing 312000, Peoples R China;[5]Wuhan Univ, Zhongnan Hosp, Dept Stomatol, Wuhan 430071, Peoples R China

年份：2024

卷号：31

期号：8

起止页码：3081

外文期刊名：ACADEMIC RADIOLOGY

收录：SCI-EXPANDED(收录号：WOS:001303912600001)、、Scopus(收录号：2-s2.0-85187998621)、WOS

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 82072026 to Jiansong Ji) , Medical and Health General Project of Zhejiang Province (Grant No. 2023KY425 to Guihan Lin) , and Medical and Health Youth Innovation Project of Zhejiang Province (Grant No. 2023RC115 to Weiyue Chen) .

语种：英文

外文关键词：Breast cancer; Non-sentinel lymph node; Machine learning; Radiomics; Axillary management.

外文摘要：Rationale and Objectives: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). Materials and Methods: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n n = 286), internal validation (n n = 122), and external validation (n n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. Results: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. Conclusion: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.