摘要
目的 探讨基于CT影像组学联合临床参数构建的列线图模型在鉴别非小细胞肺癌与肺良性病变中的价值。方法 对蚌埠医科大学第一附属医院2020年12月~2023年12月经病理证实的177例肺良性病变和非小细胞肺癌患者进行回顾性研究。按照8∶2的比例将病例随机分为训练组和验证组。从CT增强图像中提取影像组学特征,通过Relief-LASSO逐级降维,最终从2264个影像组学特征中成功筛选出了5个最佳特征。采用单-多因素logistic回归筛选临床独立危险因素。分别构建临床、影像组学以及列线图模型。采用ROC曲线下面积校准曲线和决策曲线分析等方法多维度评估列线图模型的性能表现。结果 列线图模型展现出了卓越的预测性能,其曲线下面积在训练集和验证集中分别为0.872(95%CI:0.817~0.928)和0.788(95%CI:0.627~0.948),高于单独影像组学模型(0.811、0.722)和临床模型(0.797、0.734)。结论 本研究建立的列线图模型是一种可用于非小细胞肺癌与肺良性病变鉴别诊断的无创术前预测工具,表现出了优异的鉴别和校准能力,表明其在肺癌早期筛查中的临床效用,可以在术前为临床决策提供重要指导。
Objective To explore the value of a Nomogram model based on CT radiomics with clinical parameters in differentiating non-small cell lung cancer from benign pulmonary lesions.Methods A retrospective study was conducted on 177 patients with benign pulmonary lesions and non-small cell lung cancer,confirmed by pathology,at the First Affiliated Hospital of Bengbu Medical Unversity from December 2020 to December 2023.The cases were randomly divided into a training group and a validation group in an 8:2 ratio.Radiomic features were extracted from contrast-enhanced CT images,and a stepwise dimensionality reduction was performed using the Relief-LASSO algorithm,ultimately selecting five optimal features from a total of 2264 radiomic features.Single and multiple factor Logistic regression was employed to screen independent clinical risk factors.Clinical,radiomics,and Nomogram models were constructed respectively.The performance of the Nomogram model was comprehensively evaluated using multiple metrics,including the area under the ROC curve(AUC),calibration curves,and decision curve analysis.Results The results indicated that the Nomogram model exhibited excellent predictive performance,with AUC values of 0.872(95%CI:0.817-0.928)in the training set and 0.788(95%CI:0.627-0.948)in the validation set.These values were significantly higher than those of the individual imaging model(0.811,0.722)and the clinical model(0.797,0.734).Conclusion The established Nomogram model serves as a non-surgical predictive tool for the differential diagnosis of non-small cell lung cancer and benign pulmonary lesions.Validation demonstrated that the Nomogram model exhibited excellent differentiation and calibration abilities,indicating its clinical utility in the early screening of lung cancer and providing important guidance for clinical decision-making prior to surgery.
作者
胡尹笛
陈艾琪
文欣园
王凯
邹文涛
李轶涵
游昕楠
谢波
王月燕
马宜传
HU Yindi;CHEN Aiqi;WEN Xinyuan;WANG Kai;ZOU Wentao;LI Yihan;YOU Xinnan;XIE Bo;WANG Yueyan;MA Yichuan(Graduate School of Bengbu Medical University,Bengbu 233000,China;Department of Radiology,The First Affiliated Hospital of Bengbu Medical University,Bengbu 233004,China)
出处
《分子影像学杂志》
2025年第9期1071-1077,共7页
Journal of Molecular Imaging
基金
安徽省临床医学研究转化专项立项项目(20230429-5107020072)。
关键词
肺癌
列线图
肺结节
影像组学
lung cancer
Nomogram
pulmonary nodules
radiomics
