Yao Ming, Ye Dingli, Wang Yuchong, Shen Tongxu, Yan Jieqiong, Zou Da, Sun Shuangyan
Department of Radiology, Jilin Cancer Hospital, Changchun, China.
Department of Radiology, The First Hospital of Jilin University, Changchun, China.
Front Oncol. 2025 Apr 16;15:1523507. doi: 10.3389/fonc.2025.1523507. eCollection 2025.
The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and heterogeneity analysis in the differentiation of molecular subtypes of luminal and non-luminal breast cancer.
In this retrospective study, 388 female breast cancer patients (48.37 ± 9.41 years) with luminal (n = 190) and non-luminal (n = 198) molecular subtypes who received surgical treatment at Jilin Cancer Hospital were recruited from January 2019 to June 2023. All patients underwent breast MRI scan and DCE scan before surgery. The patients were then divided into a training set (n = 272) and a validation set (n = 116) in a 7:3 ratio. The three-dimensional texture feature parameters of the breast lesion areas were extracted. Four tumor heterogeneity parameters, including type I curve proportion, type II curve proportion, type III curve proportion and tumor heterogeneity values were calculated and normalized. Five machine learning (ML) models, including the logistic regression, naive Bayes algorithm (NB), k-nearest neighbor (KNN), decision tree algorithm (DT) and extreme gradient boosting (XGBoost) model were used to process the training data and were further validated. The best ML model was selected according to the performance in the validation set.
In luminal subtype breast lesions, type III curve proportion and heterogeneity index were significantly lower than the corresponding parameters of the non-luminal subtype lesions both in the training set and validation set. Eight features together with four heterogeneity-related parameters with significant differences between luminal and non-luminal groups were retained as radiomics signatures for constructing the prediction model. The logistic regression ML model achieved the best performance in the validation set with the highest area under the curve value (0.93), highest accuracy (86.94%), sensitivity (87.55%) and specificity (86.25%).
The radiomics and heterogeneity analysis based on the DCE-MRI exhibit good application value in discriminating luminal and non-luminal subtype breast cancer. The logistic regression model demonstrates the best predictive performance among various machine learning models.
本研究旨在探讨动态对比增强磁共振成像（DCE-MRI）影像组学及异质性分析在鉴别管腔型和非管腔型乳腺癌分子亚型中的应用价值。
在这项回顾性研究中，纳入了2019年1月至2023年6月期间在吉林省肿瘤医院接受手术治疗的388例女性乳腺癌患者（年龄48.37±9.41岁），其中管腔型分子亚型患者190例，非管腔型分子亚型患者198例。所有患者在手术前均接受了乳腺MRI扫描和DCE扫描。然后将患者按7:3的比例分为训练集（n = 272）和验证集（n = 116）。提取乳腺病变区域的三维纹理特征参数。计算并归一化包括I型曲线比例、II型曲线比例、III型曲线比例和肿瘤异质性值在内的四个肿瘤异质性参数。使用包括逻辑回归、朴素贝叶斯算法（NB）、k近邻（KNN）、决策树算法（DT）和极端梯度提升（XGBoost）模型在内的五种机器学习（ML）模型对训练数据进行处理，并进一步验证。根据验证集中的表现选择最佳的ML模型。
在训练集和验证集中，管腔型亚型乳腺病变的III型曲线比例和异质性指数均显著低于非管腔型亚型病变的相应参数。保留了在管腔型和非管腔型组之间具有显著差异的八个特征以及四个与异质性相关的参数作为影像组学特征用于构建预测模型。逻辑回归ML模型在验证集中表现最佳，曲线下面积值最高（0.93），准确率最高（86.94%），灵敏度（87.55%）和特异性（86.25%）。
基于DCE-MRI的影像组学及异质性分析在鉴别管腔型和非管腔型亚型乳腺癌中具有良好的应用价值。逻辑回归模型在各种机器学习模型中表现出最佳的预测性能。
