Zhang Qing, Gao Juan, Agyekum Enock Adjei, Zhu Linna, Jiang Chao, Du Suping, Yin Liang
Department of Ultrasound, Jiangsu University Affiliated People's Hospital Zhenjiang, Jiangsu, China.
School of Medicine, Jiangsu University Zhenjiang, Jiangsu, China.
Am J Transl Res. 2025 Aug 15;17(8):6370-6380. doi: 10.62347/SBKU2090. eCollection 2025.
To evaluate the diagnostic performance of a model combining gray-scale ultrasound (US) radiomic features and clinical data in distinguishing benign from malignant breast masses classified as Breast Imaging Reporting and Data System (BI-RADS) category 4.
In this retrospective study, 149 women with pathologically confirmed breast masses were included and randomly divided into a training cohort (n=104) and a validation cohort (n=45). A total of 1,046 radiomic features were extracted from US images. Feature selection was performed using Pearson correlation analysis followed by least absolute shrinkage and selection operator (LASSO) regression. Three K-nearest neighbor (KNN) classifiers were developed: a clinical model, an ultrasound radiomics (USR) model, and a combined clinical-USR model. Model performance was assessed using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).
Seven radiomic features and two clinical variables were selected for model construction. In the training cohort, the combined clinical-USR model achieved an AUC of 0.927, with an accuracy of 89.0%, sensitivity of 88.9%, and specificity of 89.8%. In the validation cohort, the AUC of 0.826, with an accuracy of 80.0%, sensitivity of 83.3%, and specificity of 66.7%. The standalone USR model yielded AUCs of 0.902 and 0.883 in the training and validation cohorts, respectively, while the clinical model showed lower AUCs of 0.876 and 0.794. Decision curve analysis (DCA) indicated that the combined model provided a greater net clinical benefit than the clinical model alone.
The integration of ultrasound radiomic features with clinical data improves diagnostic performance in differentiating benign from malignant BI-RADS 4 breast masses. The combined model holds potential for aiding clinical decision-making but requires further validation in larger, independent datasets.
评估一种结合灰阶超声（US）影像组学特征和临床数据的模型在鉴别乳腺影像报告和数据系统（BI-RADS）4类乳腺肿块良恶性方面的诊断性能。
在这项回顾性研究中，纳入了149例经病理证实的乳腺肿块女性患者，并将其随机分为训练队列（n = 104）和验证队列（n = 45）。从US图像中提取了总共1046个影像组学特征。使用Pearson相关分析进行特征选择，随后进行最小绝对收缩和选择算子（LASSO）回归。开发了三种K近邻（KNN）分类器：临床模型、超声影像组学（USR）模型和临床-USR联合模型。使用准确度、灵敏度、特异度和受试者操作特征曲线下面积（AUC）评估模型性能。
选择了七个影像组学特征和两个临床变量用于模型构建。在训练队列中，临床-USR联合模型的AUC为0.927，准确度为89.0%，灵敏度为88.9%，特异度为89.8%。在验证队列中，AUC为0.826，准确度为80.0%，灵敏度为83.3%，特异度为66.7%。独立的USR模型在训练和验证队列中的AUC分别为0.902和0.883，而临床模型的AUC较低，分别为0.876和0.794。决策曲线分析（DCA）表明，联合模型比单独的临床模型提供了更大的净临床效益。
超声影像组学特征与临床数据的整合提高了鉴别BI-RADS 4类乳腺肿块良恶性的诊断性能。联合模型具有辅助临床决策的潜力，但需要在更大的独立数据集中进行进一步验证。
