■构建和验证放射组学模型,该模型独立地和组合地利用超声(US)和数字乳腺断层合成(DBT)图像,以无创地预测乳腺癌中的Ki-67状态。
■从2018年6月至2023年8月,共有149名接受DBT和US扫描的乳腺癌女性进行回顾性登记。影像组学特征是从DBT和US图像中获得的,然后使用几种筛选方法进行选择和降维。建立基于DBT的影像组学模型,和美国分开和合并。接收器工作特性曲线下的面积(AUC),准确度,特异性,和灵敏度用于验证模型的预测能力。使用决策曲线分析(DCA)来评估模型的临床适用性。将具有最佳AUC性能的分类器的输出转换为Rad分数,并视为Rad分数模型。使用逻辑回归方法构建了列线图,整合Rad评分和临床因素。通过AUC评估模型的稳定性,校正曲线,DCA。
■支持向量机(SVM),逻辑回归(LR),和随机森林(RF)进行训练,以建立具有选定特征的影像组学模型,支持向量机显示出最佳结果。三个模型的AUC值(US_SVM,DBT_SVM,和merge_SVM)分别为0.668、0.704和0.800。DeLong检验表明,merge_SVM和US_SVM之间的曲线下面积(AUC)存在显着差异(p=0.048),而merge_SVM和DBT_SVM之间没有实质性差异(p=0.149)。DCA曲线表明,merge_SVM在预测高Ki-67水平方面优于单峰模型,显示出更多的临床价值。整合Rad-Score与肿瘤大小的列线图在测试集中获得了更好的表现(AUC:0.818)并且具有更多的临床净。
■融合影像组学模型在预测乳腺癌Ki-67表达水平方面表现更好,但是增益效应是有限的;因此,当资源有限时,DBT是首选的术前诊断模式。与其他方法相比,列线图具有预测优势,并且可以成为预测BC中Ki-67水平的有价值的工具。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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 (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 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.
UNASSIGNED: 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.