Shen Yunyun, Huang Renjun, Zhang Yinghui, Zhu Jianguo, Li Yonggang
Department of Radiology, Suzhou Industrial Park Xinghai Hospital, Suzhou city, 215522, Jiangsu province, P.R. China.
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou City, 215006, Jiangsu Province, China.
Sci Rep. 2025 Jul 1;15(1):21923. doi: 10.1038/s41598-025-08001-6.
To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective study included TNBC patients from the First Affiliated Hospital of Soochow University and Jiangsu Province Hospital (2016-2023). We analyzed clinical characteristics and radiomic features from T2-weighted MRI. Using LASSO regression for feature selection, we applied Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) to build prediction models. A total of 163 patients, with a median age of 53 years (range: 24-73), were divided into a training group (n = 115) and a validation group (n = 48). Among them, 54 (33.13%) had ALN metastasis, and 109 (66.87%) were non-metastasis. Nottingham grade (P = 0.005), tumor size (P = 0.016) were significant difference between non-metastasis cases and metastasis cases. In the validation set, the LR-based combined model achieved the highest AUC (0.828, 95%CI: 0.706-0.950) with excellent sensitivity (0.813) and accuracy (0.812). Although the RF-based model had the highest AUC in the training set and the highest specificity (0.906) in the validation set, its performance was less consistent compared to the LR model. MRI-T2WI radiomic features predict ALN metastasis in TNBC, with integration into clinical models enhancing preoperative predictions and personalizing management.
开发并验证一种基于机器学习的预测模型，用于利用磁共振成像（MRI）和临床特征预测三阴性乳腺癌（TNBC）患者的腋窝淋巴结（ALN）转移。这项回顾性研究纳入了苏州大学第一附属医院和江苏省医院（2016 - 2023年）的TNBC患者。我们分析了T2加权MRI的临床特征和影像组学特征。使用LASSO回归进行特征选择，我们应用逻辑回归（LR）、随机森林（RF）和支持向量机（SVM）构建预测模型。总共163例患者，中位年龄为53岁（范围：24 - 73岁），被分为训练组（n = 115）和验证组（n = 48）。其中，54例（33.13%）发生ALN转移，109例（66.87%）未转移。非转移病例和转移病例之间，诺丁汉分级（P = 0.005）、肿瘤大小（P = 0.016）存在显著差异。在验证集中，基于LR的联合模型实现了最高的AUC（0.828，95%CI：0.706 - 0.950），具有出色的敏感性（0.813）和准确性（0.812）。尽管基于RF的模型在训练集中具有最高的AUC，在验证集中具有最高的特异性（0.906），但其性能与LR模型相比不太一致。MRI - T2WI影像组学特征可预测TNBC中的ALN转移，将其整合到临床模型中可增强术前预测并实现个性化管理。
