Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study

• Published in: European radiology 2023-06, Vol.33 (6), p.3931-3940
• Main Authors: Dong, Hao, Yin, Le-Kang, Qiu, Yong-Gang, Wang, Xin-Bin, Yang, Jun-Jie, Lou, Cun-Cheng, Ye, Xiao-Dan
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Adenocarcinoma - diagnostic imaging; Adenocarcinoma - pathology; Adenocarcinoma of Lung - diagnostic imaging; Adenocarcinoma of Lung - pathology; Cancer; Chest; Computed tomography; Density; Diagnostic Radiology; Diagnostic systems; High resolution; Humans; Imaging; Internal Medicine; Interventional Radiology; Invasiveness; Lesions; Lung - pathology; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Lungs; Medical diagnosis; Medicine; Medicine & Public Health; Multivariate analysis; Neoplasm Invasiveness - pathology; Neuroradiology; Quality; Radiology; Regression analysis; Regression models; Retrospective Studies; Sensitivity; Tomography, X-Ray Computed - methods; Tumors; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-09379-x

Objectives This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features. Methods The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) ( n = 101) and non-high-grade pattern (n-HGP) ( n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results. Results The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size ( p = 0.001; OR = 1.090, 95% CI 1.035–1.148), density ( p < 0.001; OR = 9.454, 95% CI 4.911–18.199), and lobulation ( p = 0.002; OR = 2.722, 95% CI 1.438–5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583–0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806–0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808–0.896) (sensitivity = 74.3%, specificity = 85.8%). Conclusion The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC. Key Points • The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583–0.699), 0.851 (95% CI 0.806–0.896), and 0.852 (95% CI 0.808–0.896) . • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns . • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma .