ArticleBMC medical imaging2025
Computed tomography radiomics of intratumoral and peritumoral microenvironments for identifying the invasiveness of subcentimeter lung adenocarcinomas.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Spectral CT-based intratumoral and peritumoral radiomics for predicting invasiveness of ground-glass nodules in lung adenocarcinoma.La Radiologia medica · 2026Article
- Habitat model based on CEUS for noninvasive prediction of EGFR mutation status in peripheral NSCLC.BMC cancer · 2026Article
- Article
- A radiomics-based nomogram for preoperatively predicting the invasiveness of nodular lung adenocarcinoma: a multicenter study.Translational lung cancer research · 2026Article
- Radiomics-machine learning model for predicting invasiveness of subcentimeter subsolid lung adenocarcinoma: a validation study with external cohort and SHAP interpretability.Frontiers in oncology · 2026Article
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Abstract
backgroundThe invasiveness of nodules plays a crucial role in the management and surgical methods selection of lung adenocarcinoma (LAC); however, the ability of traditional chest computed tomography (CT) imaging to detect the invasiveness of subcentimeter LAC is limited.
objectiveDevelopment and validation of a model based on computed tomography (CT) radiomics of the intratumoral and peritumoral microenvironments were used to identify the invasiveness of lung adenocarcinomas (LACs) appearing as subcentimeter nodules.
methodsA total of 142 consecutive patients with 142 pathologically confirmed subcentimeter LAC nodules were retrospectively studied from January 2020 to December 2023. The demographic data, clinical data, and CT features were retrospectively collected. A total of 2,264 radiomic features were extracted from LAC nodules in the intratumoral and peritumoral microenvironment and then used to construct the radiomic signature with the correlation coefficient and the least absolute shrinkage and selection operator (LASSO) logistic regression and generated radiomic scores (Radscores). A predictive model was constructed based on independent factors selected using a multiple logistic regression model. The performance of the model was evaluated with respect to its discrimination, calibration, and clinical utility.
resultsIn a total 142 LAC nodules, including 53 microinvasive adenocarcinoma (MIA) nodules and 89 invasive adenocarcinoma (IAC) nodules, the maximum diameter of nodules in the IAC group was larger than that of the MIA group. The positive rate of the vessel convergence sign (VCS) and vacuole sign in the IAC group were higher than that of the MIA group showing a statistical difference (p < 0.05). Logistic regression analysis showed that the maximum diameters of nodules and VCS were independent factors of IAC, but the predictive model based on CT features (maximum diameter and VCS) had moderate discriminative ability (area under the curve = 0.72), insufficient for standalone clinical use. The Radscores based on gross tumor volume (GTV), gross peritumoral volume (GPTV), and gross peritumoral region (GPR) in the IAC group were significantly higher than those of the MIA group (all P < 0.05, Mann-Whitney U test). The predictive model based on Radscores demonstrated improved discriminative ability (AUCs > 0.75) and calibration compared to CT features, though their clinical utility requires further validation.
conclusionsThe CT features-based predictive model had limited ability to differentiate the invasiveness in subcentimeter LAC nodules. Models using GTV, GPTV, and GPR Radscores showed improved performance for predicting invasiveness, though further validation is needed, with the GTV-based model performing best. However, this study has limitations, including its retrospective single-center design and potential selection bias due to the small size of subcentimeter lung adenocarcinoma cases. CLINICAL TRIAL NUMBER: Not applicable.
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