Ren Wanqing, Xi Xiaoming, Zhang Xiaodong, Wang Kesong, Liu Menghan, Wang Dawei, Du Yanan, Sun Jingxiang, Zhang Guang
Department of Radiology, Jinan Third People's Hospital, Jinan, China.
School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China.
Magn Reson Imaging. 2025 Apr;117:110305. doi: 10.1016/j.mri.2024.110305. Epub 2024 Dec 14.
To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.
In this study, we retrospectively analyzed clinical data and five types of MRI images (FS-T1WI, T2WI, Contrast-enhanced T1-weighted imaging (T1-C), DWI, and ADC) from 325 patients with pathologically confirmed breast cancer. Using the five types of MRI images as inputs to the ResNeXt50 model respectively, five base models were constructed, and then the outputs of the five base models were fused using an ensemble learning approach to develop a multi-parametric MRI model. Breast cancer was classified into four molecular subtypes based on immunohistochemical results: luminal A, luminal B, human epidermal growth factor receptor 2-positive (HER2-positive), and triple-negative (TN). The whole dataset was randomly divided into a training set (n = 260; 76 luminal A, 80 luminal B, 50 HER2-positive, 54 TN) and a testing set (n = 65; 20 luminal A, 20 luminal B, 12 HER2-positive, 13 TN). Accuracy, sensitivity, specificity, receiver operating characteristic curve (ROC) and area under the curve (AUC) were calculated to assess the predictive performance of the models.
In the testing set, for the assessment of the four molecular subtypes of breast cancer, the multi-parametric MRI model yielded an AUC of 0.859-0.912; the AUCs based on the FS-T1WI, T2WI, T1-C, DWI, and ADC models achieved respectively 0.632-0. 814, 0.641-0.788, 0.621-0.709, 0.620-0.701and 0.611-0.785.
The multi-parametric MRI model we developed outperformed the base models in predicting breast cancer molecular subtypes. Our study also showed the potential of FS-T1WI base model in predicting breast cancer molecular subtypes.
利用五种类型的乳腺癌术前MRI图像，开发一种用于预测乳腺癌分子亚型的多参数MRI模型。
在本研究中，我们回顾性分析了325例经病理证实的乳腺癌患者的临床数据和五种类型的MRI图像（脂肪抑制T1加权成像（FS-T1WI）、T2加权成像（T2WI）、对比增强T1加权成像（T1-C）、扩散加权成像（DWI）和表观扩散系数（ADC））。分别将五种类型的MRI图像作为输入导入ResNeXt50模型，构建五个基础模型，然后使用集成学习方法融合五个基础模型的输出，以开发一种多参数MRI模型。根据免疫组化结果，将乳腺癌分为四种分子亚型：腔面A型、腔面B型、人表皮生长因子受体2阳性（HER2阳性）和三阴性（TN）。将整个数据集随机分为训练集（n = 260；76例腔面A型、80例腔面B型、50例HER2阳性、54例TN）和测试集（n = 65；20例腔面A型、20例腔面B型、12例HER2阳性、13例TN）。计算准确率、灵敏度、特异性、受试者工作特征曲线（ROC）和曲线下面积（AUC），以评估模型的预测性能。
在测试集中，对于乳腺癌四种分子亚型的评估，多参数MRI模型的AUC为0.859 - 0.912；基于FS-T1WI、T2WI、T1-C、DWI和ADC模型的AUC分别为0.632 - 0.814、0.641 - 0.788、0.621 - 0.709、0.620 - 0.701和0.611 - 0.785。
我们开发的多参数MRI模型在预测乳腺癌分子亚型方面优于基础模型。我们的研究还显示了FS-T1WI基础模型在预测乳腺癌分子亚型方面的潜力。
