Han Yuxuan, Huang Manxia, Xie Lizhi, Cao Yuhai, Dong Yang
Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
GE Healthcare, MR Research China, Beijing, China.
Front Oncol. 2025 Mar 11;15:1379048. doi: 10.3389/fonc.2025.1379048. eCollection 2025.
A model for preoperative prediction of molecular subtypes of breast cancer using tumor and peritumor radiomics features from multiple magnetic resonance imaging (mMRI) sequences, combined with semantic features.
A total of 254 female patients with pathogically confirmed breast cancer were enrolled in this study. Preoperative mMRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE) sequences, covered the entire breast. To analyze the MRI semantic features of different molecular subtypes of breast cancer and identify independent predictive risk factors. Thirty-three binary classification models were established based on the radiomic features of different sequences and peritumoral ranges. The best radiomics model was selected by comparing the performance of the above radiomics models. At the same time, the best sequence and peritumoral extent were extracted from the target features, the radiomics score was calculated, and independent risk factors were predicted. Finally, a nomogram was established for preoperative prediction of Triple-Negative Breast Cancer (TNBC), Hormone Receptor (HR) positive and HER2 negative (HR+/HER2-), and HER2+ molecular staging types of breast cancer.
Tumor length, edge enhancement, and peritumoral edema were independent risk factors for predicting the different molecular types of breast cancer. The best MRI sequence was DCE and the best peritumoral margin was 6 mm. The AUC of the nomogram based on the optimal sequence(DCE) and optimal peritumoral range (6 mm) combined with independent risk factors were 0.910, 0.909, and 0.845, respectively.
The nomogram based on independent predictors combined with intratumoral and peritumoral radiomics scores can be used as an auxiliary diagnostic tool for molecular subtype prediction in breast cancer.
建立一种利用来自多个磁共振成像（mMRI）序列的肿瘤及瘤周放射组学特征，并结合语义特征对乳腺癌分子亚型进行术前预测的模型。
本研究共纳入254例经病理证实的女性乳腺癌患者。术前mMRI包括T2加权成像（T2WI）、扩散加权成像（DWI）和动态对比增强MRI（DCE）序列，覆盖整个乳房。以分析不同分子亚型乳腺癌的MRI语义特征并识别独立预测风险因素。基于不同序列和瘤周范围的放射组学特征建立了33个二元分类模型。通过比较上述放射组学模型的性能选择最佳放射组学模型。同时，从目标特征中提取最佳序列和瘤周范围，计算放射组学评分，并预测独立风险因素。最后，建立列线图用于术前预测三阴性乳腺癌（TNBC）、激素受体（HR）阳性且人表皮生长因子受体2阴性（HR+/HER2-）以及HER2+分子分期类型的乳腺癌。
肿瘤长度、边缘强化和瘤周水肿是预测不同分子类型乳腺癌的独立风险因素。最佳MRI序列为DCE，最佳瘤周边缘为6mm。基于最佳序列（DCE）和最佳瘤周范围（6mm）并结合独立风险因素的列线图的AUC分别为0.910、0.909和0.845。
基于独立预测因素并结合瘤内和瘤周放射组学评分的列线图可作为乳腺癌分子亚型预测的辅助诊断工具。
