摘要
目的:探讨低剂量CT影像组学列线图鉴别纯磨玻璃样结节(pGGN)中肺微浸润性腺癌(MIA)和肺浸润腺癌(IAC)的价值。方法:回顾性分析2018年1月至2023年4月温州医科大学附属第五医院经手术病理证实且CT表现为pGGN的239例肺腺癌患者的临床和CT影像资料,包括MIA 93例和IAC 146例。采用完全随机法以7:3的比例将患者分为训练集(n=167)和验证集(n=72)。使用Radcloud平台提取低剂量CT图像中病灶的影像组学特征,通过降维保留纳入模型的最佳特征。随后,建立3种机器学习分类器包括逻辑回归(LR)、支持向量机(SVM)和随机森林(RF),以验证集中曲线下面积(AUC)最高的分类器作为最佳影像组学模型,并将其结果输出为影像组学评分(Rad-score)。将P<0.05的临床和CT形态学特征纳入到多因素Logistic回归分析中,筛选出鉴别MIA和IAC的独立危险因素,并建立临床模型。最终,基于Rad-score和临床危险因素构建联合模型,并绘制列线图。采用受试者工作特征(ROC)曲线的AUC、灵敏度、特异度和准确度评价模型的诊断性能。结果:通过降维得到15个与鉴别MIA和IAC显著相关的影像组学特征。在3种机器学习分类器中,RF具有最佳的诊断性能,其在训练集和验证集的AUC分别为0.837、0.788。多因素Logistic回归分析显示,最大径较大、形状不规则和毛刺征是鉴别MIA和IAC的独立危险因素,进一步结合Rad-score建立列线图。ROC曲线结果显示,该列线图呈现出良好的诊断性能,在训练集中的AUC、灵敏度、特异度、准确度分别为0.913、87.25%、81.54%、84.94%;在验证集中的AUC、灵敏度、特异度、准确度分别为0.862、88.63%、75.01%、82.78%。结论:低剂量CT影像组学列线图能够较好地鉴别表现为pGGN的MIA和IAC,可用于指导临床手术计划制定。
Objective:To evaluate the value of low-dose CT image nomogram in the differential diagnosis of pulmonary microinvasive adenocarcinoma(MIA) and pulmonary invasive adenocarcinoma(IAC) in pure ground-glass nodule(pGGN).Methods:A retrospective analysis of the clinical and CT imaging data of 239 lung adenocarcinoma patients with pGGN confirmed by surgery and pathology at the Fifth Affiliated Hospital of Wenzhou Medical University from January 2018 to April 2023,including 93 cases of MIA and 146 cases of IAC.Patients were divided into a training set(n=167) and a validation set(n=72) at a 7:3 ratio using the complete randomization method.The radiomics features of the lesions in low-dose CT images were extracted using Radcloud platform,and the best features were preserved by dimensionality reduction.Subsequently,three machine learning classifiers,including Logistic regression(LR),support vector machine support vector machine(SVM),and random forest(RF),were established to validate the classifier with the highest area under the concentration curve(AUC) as the best radiomics model,with the output results as Rad-score.The clinical and CT features with P<0.05 were included in the multivariate logistic regression analysis to screen out the independent risk factors and establish the clinical model.Finally,a joint model was constructed based on Rad-score and clinical risk factors,and a nomogram was drawn.The area under the receiver operating characteristic(ROC) curve(AUC),sensitivity,specificity and accuracy were used to evaluate the diagnostic performance of the model.Results:Fifteen radiomics features significantly related to the differential diagnosis of MIA and IAC were obtained by dimensionality reduction.Among the three machine learning classifiers,RF had the best diagnostic performance,with AUC being 0.837 in training set and 0.788 in verification set.Multivariate logistic regression analysis showed that the long largest diameter,irregular shape and spiculate sign were independent risk factors for the identification of MI
作者
王海林
林桂涵
陈炜越
应海峰
翁雅琴
付伟东
翁巧优
卢陈英
纪建松
WANG Hailin;LIN Guihan;CHEN Weiyue;YING Haifeng;WENG Yaqin;FU Weidong;WENG Qiaoyou;LU Chenying;JI Jiansong(Department of Radiology,the Fifth Affiliated Hospital of Wenzhou Medical University,Lishui 323000 China;Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research,Lishui 323000 China;Department of Radiology,Liandu District People’s Hospital,Lishui 323000 China)
出处
《温州医科大学学报》
CAS
2024年第1期7-13,19,共8页
Journal of Wenzhou Medical University
基金
浙江省卫生健康科技计划项目(2023KY425)。
关键词
肺浸润性腺癌
肺微浸润性腺癌
影像组学
低剂量CT
pulmonary infiltrate adenocarcinoma
microinvasive adenocarcinoma of the lung
radiomics
low dose CT
