Contrast-enhanced CT Radiomics for High-Grade Pattern Identification and Prognostic Stratification in Lung Adenocarcinoma with Consolidation-to-Tumor Ratio of 25% or More.

Authors

Jiahui E, Liuqing Kang, Fan Liu, Dianzhe Wang, Jingyi Yang, Xiaoyan Lu, Yong Huang, Jing Li, Yicai Zhang, Qiliang Wang, Xiaoting Cai, Bole Gao, Zhuo Ning, Ying Liu

Published in

Radiology. Volume 320. Issue 2. Pages e260264.

Abstract

Background Radiomics may preoperatively identify high-grade patterns (HGPs) in lung adenocarcinoma (ADC) and assist in clinical decision-making. Purpose To develop and evaluate a machine learning model based on preoperative contrast-enhanced CT images to predict HGPs and explore the model's prognostic value. Materials and Methods Patients with clinical stage I invasive ADC who underwent surgery (January 2017 to May 2025) were retrospectively enrolled from three centers. Binary (low risk: HGPs < 20%; high risk: HGPs ≥ 20%) and ternary (HGP0: HGPs = 0; HGP1: 0 < HGPs < 20%; HGP2: HGPs ≥ 20%) classification analyses were performed based on the proportion of HGPs. Multivariable logistic regression analysis was used to determine independent predictors of HGPs. XGBoost classifier-based radiomic models and combined models (radiomics-predicted probabilities plus clinical variables plus CT semantic features) were constructed and evaluated for discriminability, calibration ability, and clinical utility. Kaplan-Meier and Cox regression analyses were conducted to identify prognostic factors for overall survival (OS) and recurrence-free survival (RFS). Results A total of 1181 patients (median age, 61 years [IQR, 54-66 years]; 694 female) were allocated to the training (n = 667), internal test (n = 279), and external test (n = 235) sets. The combined model achieved the best discrimination in both binary (training: area under the receiver operating characteristic curve [AUC], 0.87 [95% CI: 0.84, 0.90]; internal test: AUC, 0.80 [95% CI: 0.74, 0.85]; external test: AUC, 0.84 [95% CI: 0.78, 0.90]) and ternary (training: microaverage AUC, 0.80 [95% CI: 0.78, 0.82]; internal test: microaverage AUC, 0.74 [95% CI: 0.70, 0.77]; external test: microaverage AUC, 0.72 [95% CI: 0.68, 0.76]) classification analyses. Model-predicted high-risk group was an independent prognostic factor for both OS (binary: hazard ratio [HR] = 1.98, P =.04; ternary: HR = 2.93, P =.03) and RFS (binary: HR = 3.33, P < .001; ternary: HR = 5.10, P < .001) and was consistently confirmed across subgroup analyses. Conclusion The combined model, integrating clinical variables, CT semantic features, and radiomics-predicted probabilities, effectively predicted high-grade patterns in lung ADC and showed strong potential for prognostic risk stratification. © RSNA, 2026 Supplemental material is available for this article. See also the editorial by Arita and Kocak in this issue.

PMID:
42578790
Bibliographic data and abstract were imported from PubMed on 11 Aug 2026.

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