Cai Yuxin, Li Yanbo, Wang Wenqi, Zhou Yaqiu, Wang Jingbo, Zhang Lina, Lu Hong
Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin, People's Republic of China.
Second Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, People's Republic of China.
Breast Cancer (Dove Med Press). 2025 Jul 15;17:611-625. doi: 10.2147/BCTT.S513779. eCollection 2025.
To explore the difference between triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC) based on multi-parametric MRI imaging features and construct a prediction model to identify TNBC.
A retrospective study enrolled 1353 women with 1376 malignant lesions who had no additional therapy before surgery between January 2019 and December 2020 in a single center. The images were accessed according to BI-RADS-MR (fifth ed.) atlas. The lesions were classified as TNBC group and non-TNBC and then randomly divided into a primary cohort (n = 963) and a validation cohort (n = 413) at a ratio of 7:3. In the primary cohort, univariate analysis, logistic regression analysis and Boruta algorithm were used to determine the independent predictors for TNBC and non-TNBC. The machine learning classifier XGboost was developed based on the features to predict TNBC. The area under the receiver operating characteristic (ROC) curve (AUC) was applied to evaluate the model prediction ability. The diagnostic performances of the model were evaluated in the validation cohort.
Necrosis, edema, the maximum diameter of lesions, enhancement ratio in each phase, time to peak, gland enhancement ratio, wash-in slope and the number and diameter of the vessels were independent predictors predicting TNBC. The AUCs of the model were 0.795 (95% CI: 0.758-0.832) and 0.705 (95% CI: 0.640-0.770) in the primary cohort and validation cohort, respectively.
The model based on multiparameter MRI has good predictive ability and can be used to predict the probability of TNBC.
基于多参数MRI成像特征探讨三阴性乳腺癌（TNBC）与非三阴性乳腺癌（non-TNBC）之间的差异，并构建识别TNBC的预测模型。
一项回顾性研究纳入了2019年1月至2020年12月在单中心接受手术前未接受额外治疗的1353例患有1376个恶性病变的女性。图像根据BI-RADS-MR（第五版）图谱进行评估。病变被分为TNBC组和non-TNBC组，然后以7:3的比例随机分为初级队列（n = 963）和验证队列（n = 413）。在初级队列中，采用单因素分析、逻辑回归分析和Boruta算法确定TNBC和non-TNBC的独立预测因素。基于这些特征开发机器学习分类器XGboost以预测TNBC。应用受试者操作特征（ROC）曲线下面积（AUC）评估模型预测能力。在验证队列中评估模型的诊断性能。
坏死、水肿、病变最大直径、各期增强率、达峰时间、腺体增强率、流入斜率以及血管数量和直径是预测TNBC的独立预测因素。该模型在初级队列和验证队列中的AUC分别为0.795（95%CI：0.758 -
