ArticleEuropean radiology2023
Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Moran's I-driven habitat radiomics: A biologically plausible and temporally robust approach for risk stratification of lung adenocarcinoma invasiveness.European journal of radiology open · 2026Article
- Application of SHAP visualization and nomogram based on PET/CT radiomics combined models for risk stratification in high-grade invasive lung adenocarcinoma.Journal of translational medicine · 2026Article
- Evaluation of high-grade growth patterns in invasive adenocarcinomas manifesting as ground-glass lesions via clinical and qualitative and quantitative computed tomography features.Quantitative imaging in medicine and surgery · 2026Article
- Assessment of high-grade pattern in lung invasive nonmucinous adenocarcinoma based on multiparametric features from dual-layer detector spectral computed tomography combined with extracellular volume fraction.Quantitative imaging in medicine and surgery · 2026Article
- Integrating deep learning and radiomics in the differentiation of major histological subtypes of invasive non-mucinous lung adenocarcinoma using positron emission tomography and computed tomography.Translational lung cancer research · 2025Article
- [Predicting Invasive Non-mucinous Lung Adenocarcinoma IASLC Grading: A Nomogram Based on Dual-energy CT Imaging and Conventional Features].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
- Consensus clustering based on CT radiomics has potential for risk stratification of patients with clinical T1 stage lung adenocarcinoma.BMC medical imaging · 2025Article
- Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.BMC cancer · 2025Article
- Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines.Insights into imaging · 2025Review
- Combining high-resolution CT parameters and inflammatory markers to predict spread through air spaces in lung cancer.American journal of translational research · 2024Article
- The diagnostic value of CT-based radiomics nomogram for solitary indeterminate smoothly marginated solid pulmonary nodules.Frontiers in oncology · 2024Article
- Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier.Technology in cancer research & treatmentArticle
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7 authors.
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Abstract
objectivesThis study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features.
methodsThe 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.
resultsThe 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%).
conclusionThe 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.
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