文章：

对比增强计算机断层扫描影像组学特征、基因组改变与晚期肺腺癌患者预后的关联性研究

Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients

原文发布日期：14 September 2023

DOI: 10.3390/cancers15184553

类型: Article

开放获取: 是

英文摘要：

Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR,KRAS,ALKalterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95–0.98); validation performance was good forEGFR(AUC 0.86), moderate forKRASandALK(AUC 0.61–0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.

摘要翻译： 

为促进晚期非小细胞肺癌（NSCLC）患者的个体化治疗，亟需开发评估突变状态的无创方法及新型预后生物标志物。本研究探讨了肺腺癌病灶的增强计算机断层扫描（CT）影像组学特征（单独或联合临床参数）与肿瘤突变状态（EGFR、KRAS、ALK基因变异）及总生存期（OS）的关联性。共纳入261例回顾性病例与48例前瞻性病例。通过LASSO-Logistic回归模型构建影像组学评分（RS）以预测突变状态。基于回顾性数据训练影像组学模型、临床模型及临床-影像组学联合模型，并采用前瞻性数据进行验证（曲线下面积AUC评估）。OS预测模型通过回顾性数据训练并采用内部交叉验证进行测试（C指数评估）。结果显示：RS在训练集中对各基因变异均具有显著预测能力（影像组学及临床-影像组学模型AUC 0.95–0.98）；验证集中对EGFR突变预测效能良好（AUC 0.86），对KRAS与ALK变异预测效能中等（AUC 0.61–0.65）。单因素及多因素分析均证实RS与OS显著相关，其中多因素分析显示其预测价值独立于肿瘤分期与治疗方案。临床模型、影像组学模型及临床-影像组学模型验证C指数分别为0.63、0.79和0.80。本研究证实CT影像组学在晚期肺腺癌基因变异无创识别及预后预测中具有潜在应用价值，该结论尚需独立研究进一步验证。

原文链接：

Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients