中国肺癌杂志2025，Vol.28Issue(8)：585-596,12.DOI:10.3779/j.issn.1009-3419.2025.106.24
预测肺浸润性非黏液腺癌IASLC分级:基于双能CT成像及传统特征的列线图
Predicting Invasive Non-mucinous Lung Adenocarcinoma IASLC Grading:A Nomogram Based on Dual-energy CT Imaging and Conventional Features
摘要
Abstract
Background and objective Lung adenocarcinoma is an important pathohistologic subtype of non-small cell lung cancer(NSCLC).Invasive non-mucinous pulmonary adenocarcinomas(INMA)tend to have a poor prognosis due to their significant heterogeneity and diverse histologic components.Establishing a histologic grading system for INMA is crucial for evaluating its malignancy.In 2021,the International Association for the Study of Lung Cancer(IASLC)proposed that a new histological grading system could better stratify the prognosis of INMA patients.The aim of this study was to es-tablish a visualized nomogram model to predict INMA IASLC grading preoperatively by means of dual-energy computed tomography(DECT),fractal dimension(FD),clinical features and conventional CT parameters.Methods A total of 112 patients with INMA who underwent preoperative DECT were retrospectively enrolled from March 2021 to January 2025.Pa-tients were categorized into low-intermediate grade and high grade groups based on IASLC grading.The clinical characteristics and conventional CT parameters,including baseline features,biochemical markers,and serum tumor markers,were collected.DECT-derived parameters,including iodine concentration(IC),effective atomic number(eff-Z),and normalized IC(NIC),were collected and determined as NIC ratio(NICr)and fractal dimension(FD).Univariate analysis was employed to compare differences in conventional characteristics and DECT parameters between the two groups.Variables demonstrating statistical significance were subsequently incorporated into a multivariate Logistic regression analysis.A nomogram model integrating clinical data,conventional CT parameters,and DECT parameters was developed to identify independent predictors for IASLC grading of INMA.The discriminatory performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Results Multivariate analysis identified smoking history[odds ratio(OR)=2.848,P=0.041],lobulation sign(OR=2.163,P=0.004),air bronchogram(OR=7.833,P=0.005),eff-Z in arterial phase(OR=4.266,P＜0.001),and IC in arterial phase(OR=1.290,P=0.012)as independent and significant predictors for IASLC grading of INMA.The nomogram model constructed based on these indicators demonstrated optimal predictive performance,achieving an area under the curve(AUC)of 0.804(95％CI:0.725-0.883),with specificity and sensitivity of 85.3％and 65.7％,respectively.Conclusion The nomogram model based on clinical features,imaging features and spectral CT parameters have a large potential for application in the pre-operative noninvasive assessment of INMA IASLC grading.关键词
肺肿瘤/浸润性非黏液腺癌/能谱计算机断层扫描/国际肺癌研究协会分级/列线图Key words
Lung neoplasms/Invasive non-mucinous pulmonary adenocarcinomas/Spectral computed tomography/International Association for the Study of Lung Cancer classification/Nomogram引用本文复制引用
朱凯博,邓靓娜,侯悦,熊璐璐,朱彩霞,王海升,周俊林..预测肺浸润性非黏液腺癌IASLC分级:基于双能CT成像及传统特征的列线图[J].中国肺癌杂志,2025,28(8):585-596,12.基金项目
本研究受国家自然科学基金项目(No.82371914)资助 This study was supported by the grant from National Natural Science Foundation of China(No.82371914)(to Junlin ZHOU). （No.82371914）
