Yan Qinqin, Yan Fuhua, Wang Shengping, Feng Feng, Jia Zhongzheng, Yang Shan, Cheng Zenghui, Zhang Zhiyong, Shan Fei
Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Quant Imaging Med Surg. 2025 Aug 1;15(8):7114-7130. doi: 10.21037/qims-24-1937. Epub 2025 Jul 29.
Preoperative detection of occult nodal metastasis (ONM) is essential for treatment planning and prognostic evaluation in lung adenocarcinoma (LUAD). This study aimed to develop and validate radiomics models capable of predicting ONM in patients with stage cT1a-bN0M0 LUAD.
A total of 1,672 patients from six hospitals were enrolled and stratified into training (n=687), test (n=297) and external validation (n=688) sets. Predictive models including generalized linear model (GLM), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and the combined clinical-radiomics (Clinic-Rad) model were constructed. Diagnostic performance was quantified via the area under receiver operating characteristic curve (AUC), with the De Long test used for comparisons. The Mantel test was employed to assess the correlations between radiomics features and pathological/genetic characteristics.
In external validation, the Clinic-Rad model integrating clinical predictors and radiomics score demonstrated superior diagnostic efficacy (AUC 0.813±0.019) compared to GLM (0.790±0.021), SVM (0.761±0.023), RF (0.708±0.026), GBM (0.769±0.022) (all P values <0.001). However, no significant intermodel differences were observed in test set, with the Clinic-Rad model achieving an AUC of 0.834±0.023, and GLM, SVM, RF, and GBM yielding AUCs of 0.827±0.024, 0.829±0.025, 0.838±0.023, and 0.826±0.024, respectively (all P values >0.05). The Clinic-Rad model exhibited a pooled sensitivity of 75.8-77.2%, a specificity of 72.0-72.7%, and an accuracy of 72.7-74.4%, with pooled AUC values of 0.802-0.820 and 0.797-0.917 for the solid and subsolid LUAD, respectively. Furthermore, radiomics models outperformed clinical predictors comprising solid-component diameter (AUC: 0.669-0.678), consolidation-to-tumor ratio (CTR) (0.542-0.600), carcinoembryonic antigen (CEA) level (0.571-0.613), and their combination (0.683-0.724) (all P values <0.001). The Mantel test indicated correlations between radiomics signatures and , , , and expression, as well as histopathological markers of ONM.
Radiomics-based models demonstrate clinical utility in predicting ONM in patients with stage cT1a-bN0M0 LUAD, with the integrated Clinic-Rad model providing superior diagnostic performance.
术前检测隐匿性淋巴结转移(ONM)对于肺腺癌(LUAD)的治疗规划和预后评估至关重要。本研究旨在开发并验证能够预测cT1a-bN0M0期LUAD患者ONM的放射组学模型。
共纳入来自六家医院的1672例患者,并将其分层为训练集(n = 687)、测试集(n = 297)和外部验证集(n = 688)。构建了包括广义线性模型(GLM)、支持向量机(SVM)、随机森林(RF)、梯度提升机(GBM)以及临床-放射组学联合(Clinic-Rad)模型在内的预测模型。通过受试者操作特征曲线下面积(AUC)对诊断性能进行量化,并使用De Long检验进行比较。采用Mantel检验评估放射组学特征与病理/基因特征之间的相关性。
在外部验证中,整合临床预测指标和放射组学评分的Clinic-Rad模型显示出优于GLM(0.790±0.021)、SVM(0.761±0.023)、RF(0.708±0.026)、GBM(0.769±0.022)的诊断效能(AUC 0.813±0.019)(所有P值<0.001)。然而,在测试集中未观察到模型间的显著差异,Clinic-Rad模型的AUC为0.834±0.023,GLM、SVM、RF和GBM的AUC分别为0.827±0.024、0.829±0.025、0.838±0.023和0.826±0.024(所有P值>0.05)。Clinic-Rad模型的合并敏感度为75.8 - 77.2%,特异度为72.0 - 72.7%,准确度为72.7 - 74.4%,实性和亚实性LUAD的合并AUC值分别为0.802 - 0.820和0.797 - 0.917。此外,放射组学模型优于包括实性成分直径(AUC:0.669 - 0.678)、实变与肿瘤比值(CTR)(0.542 - 0.600)、癌胚抗原(CEA)水平(0.571 - 0.613)及其组合(0.683 - 0.724)在内的临床预测指标(所有P值<0.001)。Mantel检验表明放射组学特征与 、 、 和 表达以及ONM的组织病理学标志物之间存在相关性。
基于放射组学的模型在预测cT1a-bN0M0期LUAD患者的ONM方面具有临床应用价值,整合的Clinic-Rad模型具有更优的诊断性能。