ArticleJournal of thoracic disease2025
Quantitative analysis for identifying molecular subtypes of small cell lung cancer via two-dimensional and three-dimensional contrast-enhanced computed tomography images: a preliminary study.
Article in Journal of thoracic disease, 2025. 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: Small cell lung cancer (SCLC) comprises distinct molecular subtypes [neuroendocrine (NE) Methods: In this single-center retrospective study, we included 51 patients with resected SCLC (NE subtype n=39, non-NE n=12) between 2005 and 2016, all with preoperative CECT scans and known molecular subtype confirmed by immunohistochemistry. Radiomics features were extracted from arterial-phase CECT images using both a 2D (single largest cross-sectional slice) and 3D (whole tumor volume) segmentation of the primary tumor. Radiomics-based logistic regression models were trained to classify NE Results: The 2D radiomics model achieved an AUC of 0.806 (95% CI: 0.666-0.945) for distinguishing NE Conclusions: Quantitative analysis of CECT images via radiomics can noninvasively distinguish NE and non-NE molecular subtypes of SCLC. A simplified 2D radiomics approach performed comparably to 3D volumetric analysis for subtype classification and also demonstrated prognostic relevance. Radiomics could serve as a valuable adjunct for SCLC subtype identification and risk stratification, potentially guiding more personalized treatment decisions.
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