■探讨基于乳腺癌肿瘤内和瘤周动态对比增强MRI(DCE-MRI)影像组学和临床影像学特征预测腋窝淋巴结(ALN)转移的价值。
■从2017年1月至2020年12月,共473例接受术前DCE-MRI检查的乳腺癌患者纳入研究。这些患者以8:2的比例随机分为训练组(n=378)和测试组(n=95)。手动划定感兴趣的肿瘤内区域(ITR),通过形态学扩张ITR自动获得3mm的肿瘤周围区域(3mmPTR)。提取了影像组学特征,和ALN转移相关的影像组学特征通过Mann-WhitneyU检验选择,Z分数归一化,方差阈值,K-best算法和最小绝对收缩和选择算子(LASSO)算法。通过逻辑回归选择临床放射学危险因素,并结合影像组学特征构建预测模型。然后,构建了5个模型,包括ITR,3mmPTR,ITR+3mmPTR,临床放射学和联合(ITR+3mmPTR+临床放射学)模型。通过灵敏度评估模型的性能,特异性,准确度,受试者工作特征(ROC)的F1评分和曲线下面积(AUC),校准曲线和判定曲线分析(DCA)。
■从每个感兴趣区域(ROI)中总共提取了2264个影像组学特征,为ITR和3mmPTR选择了3和10个影像组学特征,分别。选择了5个临床放射学危险因素,包括病变大小,人表皮生长因子受体2(HER2)的表达,血管癌血栓状态,MR报告的ALN状态,和时间-信号强度曲线(TIC)类型。在测试集中,联合模型显示最高的AUC(0.839),特异性(74.2%),5个模型的准确率(75.8%)和F1评分(69.3%)。DCA显示,与其他模型相比,它具有最大的净临床效益。
基于DCE-MRI的肿瘤内和肿瘤周围影像组学模型可用于预测乳腺癌的ALN转移,特别是对于具有临床放射学特征的组合模型,显示出有希望的临床应用价值。
UNASSIGNED: To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.
UNASSIGNED: A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA).
UNASSIGNED: A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models.
UNASSIGNED: The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.