Aim: The purpose of this study was to develop a radiomic-based machine-learning model to predict triple-negative breast cancer (TNBC) based on the contralateral unaffected breast’s fibroglandular tissue (FGT) in breast cancer patients. Materials and methods: This study retrospectively included 541 patients (mean age, 51 years; range, 26–82) who underwent a screening breast MRI between November 2016 and September 2018 and who were subsequently diagnosed with biopsy-confirmed, treatment-naïve breast cancer. Patients were divided into training (n= 250) and validation (n= 291) sets. In the training set, 132 radiomic features were extracted using the open-source CERR platform. Following feature selection, the final prediction model was created, based on a support vector machine with a polynomial kernel of order 2. Results: In the validation set, the final prediction model, which included four radiomic features, achieved an F1 score of 0.66, an area under the curve of 0.71, a sensitivity of 54% [47–60%], a specificity of 74% [65–84%], a positive predictive value of 84% [78–90%], and a negative predictive value of 39% [31–47%]. Conclusions: TNBC can be predicted based on radiomic features extracted from the FGT of the contralateral unaffected breast of patients, suggesting the potential for risk prediction specific to TNBC.
目的：本研究旨在开发一种基于影像组学的机器学习模型，通过乳腺癌患者对侧未受累乳房的纤维腺体组织（FGT）来预测三阴性乳腺癌（TNBC）。材料与方法：本研究回顾性纳入了541名患者（平均年龄51岁，范围26-82岁），这些患者在2016年11月至2018年9月期间接受了筛查性乳腺MRI检查，随后被诊断为经活检证实且未经治疗的乳腺癌。患者被分为训练集（n=250）和验证集（n=291）。在训练集中，使用开源CERR平台提取了132个影像组学特征。经过特征选择后，基于二阶多项式核的支持向量机构建了最终的预测模型。结果：在验证集中，包含四个影像组学特征的最终预测模型取得了F1分数0.66、曲线下面积0.71、敏感性54% [47–60%]、特异性74% [65–84%]、阳性预测值84% [78–90%]和阴性预测值39% [31–47%]。结论：基于患者对侧未受累乳房FGT提取的影像组学特征可以预测TNBC，这表明了针对TNBC进行风险预测的潜力。
肿瘤(癌症)患者之家