ArticleJournal of thoracic disease2026
Prediction of microinvasive spread in stage T1 non-small cell lung cancer based on a preoperative tumor margin CT radiomics model.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Accurately predicting subclinical invasion and spread is critical for screening patients with early-stage non-small cell lung cancer (NSCLC) who receive preoperative neoadjuvant chemotherapy or develop individualized surgical plans. These subclinical invasions cannot be visualized by existing imaging systems, and relevant information cannot be obtained before surgery for clinical decision-making. This study aimed to assess the preoperative status of microinvasion-including vascular invasion (VI), lymphatic invasion (LI), visceral pleural invasion (VPI), and spread through air spaces (STAS)-and to evaluate the predictive value of tumor characteristics, tumor margins, peritumoral radiomic features, clinical variables, and computed tomography (CT)-based fusion models in patients with stage T1 NSCLC. Methods: One hundred and seventy-seven patients with stage T1 NSCLC in The First Hospital of Jilin University were randomly divided into a test group (microinvasion: non-invasion group =50:74) and a validation group (microinvasion: non-invasion group =21:32) in a 7:3 ratio. The study extracted radiomics features from the tumor interior, margins, and periphery. A total of three radiomics models were constructed, and the optimal model was selected to construct a fusion model with clinically independent predictors. Results: Compared with the tumor radiomics model and the combined model integrating tumor peripheral radiomics, the combined model integrating tumor margin radiomics demonstrated better predictivity, with higher area under the curve (AUC) values. In the clinical model, tumor characteristics_solid nodule, long axis direction_Z, and pleural indentation were independent predictors. The combined model's efficacy was enhanced by integrating the clinical independent predictors and the combined tumor margin model. The AUCs for the three cohorts were 0.759, 0.804, and 0.818, respectively. Conclusions: A fusion model based on independent predictors of clinical CT morphology and combined tumor margin radiomic features can help predict microinvasion in patients with early-stage lung cancer, offering the possibility of preoperative, non-invasive risk stratification.
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