Liu Jie, Yan Caiying, Liu Chenlu, Wang Yanxiao, Chen Qian, Chen Ying, Guo Jianfeng, Chen Shuangqing
Department of Radiology, Nanjing Medical University Affiliated Suzhou Hospital, Suzhou, China.
Department of Ultrasound, Sir Run Run Hospital Nanjing Medical University, Nanjing, China.
Front Oncol. 2024 Jul 11;14:1403522. doi: 10.3389/fonc.2024.1403522. eCollection 2024.
To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer.
149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model's stability was assessed through AUC, calibration curves, and DCA.
Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM ( = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM ( = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net.
The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.
构建并验证利用超声（US）和数字乳腺断层合成（DBT）图像独立及联合进行无创预测乳腺癌Ki-67状态的放射组学模型。
回顾性纳入2018年6月至2023年8月期间接受DBT和US扫描的149例乳腺癌女性患者。从DBT和US图像中获取放射组学特征，然后使用多种筛选方法进行特征选择和降维。分别基于DBT、US以及二者联合建立放射组学模型。利用受试者操作特征曲线下面积（AUC）、准确率、特异性和敏感性来验证模型的预测能力。采用决策曲线分析（DCA）评估模型的临床适用性。将具有最佳AUC性能的分类器输出转换为Rad评分，视为Rad-Score模型。使用逻辑回归方法构建列线图，整合Rad-Score和临床因素。通过AUC、校准曲线和DCA评估模型的稳定性。
使用支持向量机（SVM）、逻辑回归（LR）和随机森林（RF）对所选特征进行训练以建立放射组学模型，其中SVM显示出最佳结果。三种模型（US_SVM、DBT_SVM和merge_SVM）的AUC值分别为0.668、0.704和0.800。DeLong检验表明merge_SVM与US_SVM之间的曲线下面积（AUC）存在显著差异（P = 0.048），而merge_SVM与DBT_SVM之间无显著差异（P = 0.149）。DCA曲线表明，在预测高Ki-67水平方面，merge_SVM优于单模态模型，具有更多临床价值。将Rad-Score与肿瘤大小整合的列线图在测试集中表现更好（AUC：0.818），且具有更多临床净效益。
融合放射组学模型在预测乳腺癌Ki-67表达水平方面表现更好，但增益效果有限；因此，在资源有限时，DBT作为术前诊断模式更受青睐。列线图比其他方法具有预测优势，可成为预测乳腺癌中Ki-67水平的有价值工具。
