Background/Objectives:To evaluate the ability of radiomics analysis of QUS spectral parametric imaging to non-invasively differentiate intermediate-to-high-risk from low-risk Oncotype DXTMRecurrence Score (ODXRS).Methods:This prospective study included 31 participants (21 intermediate-to-high-risk ODXRS (median age, 56 years [IQR: 49–68 years]) and 10 low-risk ODXRS (median age, 52 years [IQR: 48–58 years])) presenting with ER+ HER2− invasive breast masses acquired between September 2015 and August 2024. Quantitative ultrasound (QUS) spectroscopy produced five spectral maps, from which radiomics features (including statistical, texture, and morphological measures) were extracted from the tumor core and a 5 mm margin. The ground truth label was determined from thresholding the ODXRS. A multivariate predictive model was developed to differentiate intermediate-to-high-risk ODXRS from low-risk ODXRS, with performance assessed via nested leave-one-out cross-validation (LOOCV).Results:A nested leave-one-out cross-validation (LOOCV) analysis demonstrated the generalization performance of a four-feature model. The support vector machine (SVM-RBF) classifier achieved 86% recall, 100% specificity, 93% balanced accuracy, and an area under the receiver operating characteristic curve (AUROC) of 0.95 (CI = 0.88–1.00) in identifying intermediate-to-high-risk versus low-risk ODXRS.Conclusions:The preliminary results suggest the potential radiomics-based model of ODXRS in predicting the risks of recurrence. The results warrant further investigation on a larger cohort. This framework can be a useful surrogate for participants for whom ODX testing is neither affordable nor available.
背景/目的：评估定量超声（QUS）光谱参数成像的影像组学分析在无创区分中高风险与低风险Oncotype DX™复发评分（ODXRS）方面的能力。 方法：这项前瞻性研究纳入了31名参与者（21例中高风险ODXRS[中位年龄56岁，四分位距：49-68岁]和10例低风险ODXRS[中位年龄52岁，四分位距：48-58岁]），其ER阳性、HER2阴性的浸润性乳腺肿块数据采集于2015年9月至2024年8月期间。定量超声（QUS）光谱分析生成了五幅光谱图，从肿瘤核心及5毫米边缘区域提取了影像组学特征（包括统计、纹理和形态学指标）。金标准标签通过ODXRS阈值划分确定。研究构建了一个多变量预测模型以区分中高风险与低风险ODXRS，并通过嵌套留一交叉验证（LOOCV）评估模型性能。 结果：嵌套留一交叉验证（LOOCV）分析显示，一个包含四个特征的模型具有良好的泛化性能。支持向量机（SVM-RBF）分类器在识别中高风险与低风险ODXRS时，达到了86%的召回率、100%的特异性、93%的平衡准确率，以及接收者操作特征曲线下面积（AUROC）为0.95（置信区间 = 0.88–1.00）。 结论：初步结果表明，基于影像组学的ODXRS模型在预测复发风险方面具有潜力。该结果支持在更大规模队列中进行进一步研究。对于无法负担或获得ODX检测的参与者，此框架可作为一个有用的替代评估工具。
肿瘤(癌症)患者之家