Guo Fuyu, Sun Shiwei, Deng Xiaoqian, Wang Yue, Yao Wei, Yue Peng, Wu Shaoduo, Yan Junrong, Zhang Xiaojun, Zhang Yangang
Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Immunol. 2024 Dec 13;15:1482020. doi: 10.3389/fimmu.2024.1482020. eCollection 2024.
To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). The objective is to provide guidance for developing scientifically individualized treatment plans, assessing prognosis, and planning preoperative interventions.
A retrospective analysis was conducted on clinical and imaging data from 270 patients with BC confirmed by surgical pathology at the Third Hospital of Shanxi Medical University between November 2022 and April 2024. Multiple sequence images from MG and MRI were selected, and regions of interest in the lesions were delineated. Radiomics and deep learning (3D-Resnet18) features were extracted and fused. The samples were randomly divided into training and test sets in a 7:3 ratio. Dimensionality reduction and feature selection were performed using the least absolute shrinkage and selection operator (LASSO) regression model, and other methods. Various machine learning algorithms were used to construct radiomics, deep learning, and combined models. These models were visualized and evaluated for performance using receiver operating characteristic curves, area under the curve (AUC), calibration curves, and decision curves.
The highest AUCs in the test set were achieved using radiomics-logistic regression (AUC = 0.759), deep learning-multilayer perceptron (MLP) (AUC = 0.712), and combined-MLP models (AUC = 0.846). The MLP model demonstrated strong classification performance, with the combined model (AUC = 0.846) outperforming both the radiomics (AUC = 0.756) and deep learning (AUC = 0.712) models.
The multimodal radiomics and deep learning models developed in this study, incorporating various machine learning algorithms, offer significant value for the preoperative prediction of ALNM in BC.
探讨基于乳腺钼靶（MG）和磁共振成像（MRI），使用不同机器学习算法的联合放射组学和深度学习模型对乳腺癌（BC）腋窝淋巴结转移（ALNM）的预测价值。目的是为制定科学的个体化治疗方案、评估预后和规划术前干预提供指导。
对2022年11月至2024年4月在山西医科大学第三医院经手术病理确诊的270例BC患者的临床和影像数据进行回顾性分析。选取MG和MRI的多序列图像，勾勒出病变的感兴趣区域。提取并融合放射组学和深度学习（3D-Resnet18）特征。样本以7:3的比例随机分为训练集和测试集。使用最小绝对收缩和选择算子（LASSO）回归模型等方法进行降维和特征选择。使用各种机器学习算法构建放射组学、深度学习和联合模型。使用受试者工作特征曲线、曲线下面积（AUC）、校准曲线和决策曲线对这些模型进行可视化和性能评估。
在测试集中，放射组学-逻辑回归（AUC = 0.759）、深度学习-多层感知器（MLP）（AUC = 0.712）和联合-MLP模型（AUC = 0.846）的AUC最高。MLP模型表现出强大的分类性能，联合模型（AUC = 0.846）优于放射组学（AUC = 0.756）和深度学习（AUC = 0.712）模型。
本研究开发的多模态放射组学和深度学习模型，结合了各种机器学习算法，为BC中ALNM的术前预测提供了重要价值。
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