Peritumoral Radiomic Features on CT for Differential Diagnosis in Small-Cell Lung Cancer: Potential for Surgical Decision-Making.

Small-cell lung cancer (SCLC) is a leading cause of cancer-related mortality worldwide, with limited therapeutic outcomes and poor prognosis. Accurate diagnosis and optimal surgical decision-making remain critical challenges. This study aimed to develop and validate a clinical-radiomics nomogram integrating computed tomography (CT) radiomic features of the peritumoral region and clinical factors to improve SCLC diagnosis and guide surgical planning. A retrospective cohort of 113 patients (54 SCLC, 59 non-small cell lung cancer) was analyzed. CT images were processed to extract 1050 radiomic features from both intratumoral and peritumoral (2-mm expanded) ROIs. Feature selection was performed using t-tests, LASSO regression, and mRMR analysis. Logistic regression models were constructed for original and expanded ROIs, and a clinical-radiomics nomogram was developed by combining significant radiomic features with independent clinical predictors (gender, smoking history, tumor diameter, glitch, and neuron-specific enolase levels). Model performance was evaluated using ROC curves, AUC, sensitivity, specificity, and CIC curves. The expanded ROI radiomics model outperformed the original ROI and clinical models, achieving higher accuracy (0.83 vs 0.76/0.70), sensitivity (0.80 vs 0.74/0.77), specificity (0.85 vs 0.75/0.65), and AUC (0.85 vs 0.76/0.71). The clinical-radiomics nomogram demonstrated superior diagnostic performance, with an AUC of 0.96 (95% CI: 0.88-1.00), accuracy of 0.91, sensitivity of 0.92, and specificity of 0.90. CIC analysis confirmed its clinical utility for surgical decision-making at intermediate-risk thresholds. The integration of peritumoral radiomic features and clinical factors into a nomogram provides a non-invasive tool for SCLC diagnosis and surgical planning. The superiority of the expanded model substantiates the potential presence of SCLC in peri-tumoral tissues that may be imperceptible through conventional imaging, thereby offering guidance for surgical decision-making. This approach has potential for improving treatment outcomes and warrants further validation in multicenter studies.