Zhang Hongxia, Wang Leilei, Lin Yayun, Ha Xiaoming, Huang Chunyan, Han Chao
Department of Ultrasound, Yantaishan Hospital, No. 10087 Keji Avenue, Laishan District, Yantai, 264003, Shandong, P.R. China.
J Imaging Inform Med. 2025 Jan 22. doi: 10.1007/s10278-025-01388-8.
Radiomics has been used as a non-invasive medical image analysis technique for diagnosis and prognosis prediction of breast cancer. This study intended to use radiomics based on preoperative Doppler ultrasound images to classify four molecular subtypes of breast cancer. A total of 565 female breast cancer patients diagnosed by postoperative pathology in a hospital between 2014 and 2022 were included in this study. Radiomic features extracted from preoperative ultrasound images and clinical features were used to construct models for the classification of molecular subtypes of breast cancer. The least absolute shrinkage and selection operator (LASSO) regression was applied for the final screening of radiomic features and clinical features. Three classifiers including Logistic regression, support vector machine (SVM), and XGBoost were utilized to construct model. Model performance was assessed primarily by the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). The mean age of these patients was 54.58 (± 11.27) years. Of these 565 patients, 130 (23.01%) were Luminal A subtype, 329 (58.23%) were Luminal B subtype, 65 (11.50%) were human epidermal growth factor receptor-2 (HER-2) subtype, and 41 (7.26%) were triple negative (TN) subtype. A total of 12 clinical features and 8 radiomic features were selected for model construction. The AUC of the SVM model [0.826 (95%CI 0.808-0.845)] was higher than that of the Logistic regression model [0.776 (95%CI 0.756-0.796)] and the XGB model [0.800 (95%CI 0.779-0.821)] in the multiple classification of breast cancer. For the single classification of breast cancer, the AUC of the SVM model was 0.710 (95%CI 0.660-0.760) for Luminal A subtype, 0.639 (95%CI 0.592-0.685) for Luminal B subtype, 0.754 (95%CI 0.695-0.813) for HER-2 subtype, and 0.832 (95%CI 0.771-0.892) for TN subtype. The SVM model with radiomic features combined with clinical features shows good performance in classifying four molecular subtypes of breast cancer.
放射组学已被用作一种非侵入性医学图像分析技术，用于乳腺癌的诊断和预后预测。本研究旨在基于术前多普勒超声图像，利用放射组学对乳腺癌的四种分子亚型进行分类。本研究纳入了2014年至2022年期间在某医院经术后病理诊断的565例女性乳腺癌患者。从术前超声图像中提取的放射组学特征和临床特征被用于构建乳腺癌分子亚型分类模型。采用最小绝对收缩和选择算子（LASSO）回归对放射组学特征和临床特征进行最终筛选。利用逻辑回归、支持向量机（SVM）和XGBoost三种分类器构建模型。主要通过受试者操作特征曲线（AUC）下面积和95%置信区间（CI）评估模型性能。这些患者的平均年龄为54.58（±11.27）岁。在这565例患者中，130例（23.01%）为Luminal A亚型，329例（58.23%）为Luminal B亚型，65例（11.50%）为人表皮生长因子受体2（HER-2）亚型，41例（7.26%）为三阴性（TN）亚型。共选择12项临床特征和8项放射组学特征用于模型构建。在乳腺癌的多分类中，SVM模型的AUC[0.826（95%CI 0.808-0.845）]高于逻辑回归模型[0.776（95%CI 0.756-0.796）]和XGB模型[0.800（95%CI 0.779-0.821）]。在乳腺癌的单分类中，SVM模型对Luminal A亚型的AUC为0.710（95%CI 0.660-0.760），对Luminal B亚型的AUC为0.639（95%CI 0.592-0.685），对HER-2亚型的AUC为0.754（95%CI 0.695-0.813），对TN亚型的AUC为0.832（95%CI 0.771-0.892）。具有放射组学特征与临床特征相结合的SVM模型在乳腺癌四种分子亚型的分类中表现出良好性能。
