分子影像学杂志2025，Vol.48Issue(9)：1071-1077,7.DOI:10.12122/j.issn.1674-4500.2025.09.03
基于CT影像组学联合临床参数构建的列线图模型可有效鉴别非小细胞肺癌与肺良性病变
The construction of a Nomogram based on CT radiomics combined with clinical parameters can effectively differentiate non-small cell lung cancer from benign pulmonary lesions
摘要
Abstract
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.关键词
肺癌/列线图/肺结节/影像组学Key words
lung cancer/Nomogram/pulmonary nodules/radiomics引用本文复制引用
胡尹笛,马宜传,陈艾琪,文欣园,王凯,邹文涛,李轶涵,游昕楠,谢波,王月燕..基于CT影像组学联合临床参数构建的列线图模型可有效鉴别非小细胞肺癌与肺良性病变[J].分子影像学杂志,2025,48(9):1071-1077,7.基金项目
安徽省临床医学研究转化专项立项项目(20230429-5107020072) （20230429-5107020072）
