A Prognostic Nomogram Combining Radiologic Emphysema and Clinical Parameters in Small Cell Lung Cancer

Small cell lung cancer (SCLC) is an aggressive malignancy with poor survival. Existing prognostic models provide limited patient-level risk stratification and often overlook accessible clinical and imaging data. There remains a need for refined tools to support individualized prognostication in SCLC.

We retrospectively analyzed 149 SCLC patients diagnosed between 2010 and 2014, representing a pre-immunotherapy era cohort. Demographics, CT scans, and treatment details were reviewed. Emphysema burden was quantified using an AI-based automated tool that segmented the lungs and calculated the percentage of low-attenuation areas (< -950 Hounsfield Units). Multivariate Cox regression identified predictors for overall survival (OS), informing nomogram construction. Performance was measured by Harrell's C-index and calibration plots. Validation included bootstrap resampling and a 3:1 data split. Decision curve analysis (DCA) evaluated clinical utility.

Age, emphysema, and treatment modality were independently associated with OS. The nomogram demonstrated excellent discrimination (C-index = 0.807; 95% confidence interval [CI], 0.771-0.843) and good calibration for 1- and 3-year survival. Internal validity was high, with a bootstrap-adjusted C-index of 0.805 (95% CI, 0.779-0.845), and performance remained robust in the validation subset (C-index = 0.740; 95% CI, 0.668-0.812). Stratification by nomogram-derived risk quartiles significantly differentiated survival (log-rank p < 0.001). DCA demonstrated superior net clinical benefit compared to treat-all or treat-none strategies across clinically relevant thresholds.

This validated nomogram, based on three readily available variables, provides accurate survival predictions for patients with SCLC. It may assist clinicians in refining treatment strategies and enhancing shared decision-making. External validation is warranted.