Ma Mengwei, Xu Weimin, Yang Jun, Zheng Bowen, Wen Chanjuan, Wang Sina, Xu Zeyuan, Qin Genggeng, Chen Weiguo
Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
BMC Med Imaging. 2025 Jul 1;25(1):255. doi: 10.1186/s12880-025-01765-3.
This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer.
This retrospective study included women with breast cancer who underwent CEM preoperatively between 2018 and 2021. We included 241 patients, which were randomly assigned to either a training or a test set in a 7:3 ratio. Twenty-one features were visually described, including four clinical features and seventeen radiological features, these radiological features which extracted from the CEM. Three binary classifications of subtypes were performed: Luminal vs. non-Luminal, HER2-enriched vs. non-HER2-enriched, and triple-negative (TNBC) vs. non-triple-negative. A multinomial naive Bayes (MNB) machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method were used to select the most predictive features for the classifiers. The classification performance was evaluated using the area under the receiver operating characteristic curve. We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model.
The model that used a combination of low energy (LE) and dual-energy subtraction (DES) achieved the best performance compared to using either of the two images alone, yielding an area under the receiver operating characteristic curve of 0.798 for Luminal vs. non-Luminal subtypes, 0.695 for TNBC vs. non-TNBC, and 0.773 for HER2-enriched vs. non-HER2-enriched. The SHAP algorithm shows that "LE_mass_margin_spiculated," "DES_mass_enhanced_margin_spiculated," and "DES_mass_internal_enhancement_homogeneous" have the most significant impact on the model's performance in predicting Luminal and non-Luminal breast cancer. "mass_calcification_relationship_no," "calcification_ type_no," and "LE_mass_margin_spiculated" have a considerable impact on the model's performance in predicting HER2 and non-HER2 breast cancer.
The radiological characteristics of breast tumors extracted from CEM were found to be associated with breast cancer subtypes in our study. Future research is needed to validate these findings.
本研究旨在建立一种机器学习预测模型，以探讨乳腺造影增强（CEM）成像特征与肿块型乳腺癌分子亚型之间的相关性。
这项回顾性研究纳入了2018年至2021年间术前接受CEM检查的乳腺癌女性患者。我们纳入了241例患者，按照7:3的比例随机分配到训练集或测试集。通过视觉描述了21个特征，包括4个临床特征和17个放射学特征，这些放射学特征是从CEM中提取的。进行了三种亚型的二元分类：Luminal型与非Luminal型、HER2富集型与非HER2富集型、三阴性（TNBC）与非三阴性。采用多项式朴素贝叶斯（MNB）机器学习方案进行分类，并使用最小绝对收缩和选择算子方法为分类器选择最具预测性的特征。使用受试者操作特征曲线下面积评估分类性能。我们还利用SHapley值加法解释（SHAP）来解释预测模型。
与单独使用两张图像中的任何一张相比，使用低能量（LE）和双能量减影（DES）组合的模型表现最佳，Luminal型与非Luminal型亚型的受试者操作特征曲线下面积为0.798，TNBC与非TNBC为0.695，HER2富集型与非HER2富集型为0.773。SHAP算法表明，“LE_mass_margin_spiculated”“DES_mass_enhanced_margin_spiculated”和“DES_mass_internal_enhancement_homogeneous”对模型预测Luminal型和非Luminal型乳腺癌的性能影响最大。“mass_calcification_relationship_no”“calcification_type_no”和“LE_mass_margin_spiculated”对模型预测HER2型和非HER2型乳腺癌的性能有相当大的影响。
在我们的研究中，发现从CEM中提取的乳腺肿瘤放射学特征与乳腺癌亚型有关。未来需要进一步研究来验证这些发现。
