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

Background: Small cell lung cancer (SCLC) comprises distinct molecular subtypes [neuroendocrine (NE) vs. non-NE] that have different prognoses, with NE tumors generally exhibiting a more aggressive clinical course. However, identifying these subtypes usually requires invasive tissue sampling. Radiomics—the extraction of quantitative features from medical images—offers a potential noninvasive alternative. This study aimed to predict the NE subtype of SCLC using radiomics analysis of contrast-enhanced computed tomography (CECT) images, and to compare a two-dimensional (2D) radiomics approach with a three-dimensional (3D) approach. 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 vs. non-NE subtypes. Model performance was evaluated using receiver operating characteristic analysis [area under the curve (AUC)] with bootstrap 95% confidence intervals (CIs). A combined model incorporating radiomics and clinical factors was also tested. Additionally, we explored the association of the radiomics signature with recurrence-free survival (RFS) via Kaplan-Meier curves and Cox proportional-hazards analysis. Results: The 2D radiomics model achieved an AUC of 0.806 (95% CI: 0.666–0.945) for distinguishing NE vs. non-NE subtypes, comparable to the 3D model (AUC 0.784, 95% CI: 0.634–0.934; P=0.75 or 2D vs. 3D). At the optimal cutoff, the 2D model yielded 64.1% sensitivity and 83.3% specificity. The radiomics signature remained an independent predictor of NE subtype in a combined model [adjusted odds ratio (OR) 6.22, P=0.005], and the addition of radiomics improved the combined model’s AUC to 0.861 (vs. 0.673 for clinical factors alone). No conventional clinical or CT features alone were significant predictors. Notably, the 2D radiomics score also stratified patients’ outcomes: those predicted as NE subtype had a 5-year RFS of 48.1%, compared to 62.5% for non-NE (log-rank P=0.03). In multivariable Cox analysis, a higher radiomics score showed a trend toward shorter RFS [hazard ratios (HRs) 1.46 per SD increase, P=0.08]. 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.