Baseline ¹⁸F-FDG PET/CT radiomics predict early progression and survival in small cell lung cancer

To determine whether baseline ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomic markers of thoracic tumor burden and metabolic heterogeneity predict early progression and overall survival (OS) in patients with small cell lung cancer (SCLC) receiving first-line platinum-etoposide chemotherapy.

We retrospectively analyzed 45 patients with SCLC who underwent baseline ¹⁸F-FDG PET/CT before chemotherapy. Radiomic features were extracted from thoracic tumor volumes, including metabolic tumor volume (MTV), total lesion glycolysis (TLG), standardized uptake value (SUV) histogram parameters, and gray-level co-occurrence matrix (GLCM) metrics. Univariable and multivariable Cox regression assessed associations with progression-free survival (PFS) and OS. Receiver operating characteristic (ROC) analysis, with area under the ROC curve (AUC), assessed discriminatory ability for early progression (PFS < 6 months) and short-term mortality (OS < 12 months).

Higher log-transformed TLG (logTLG) was correlated with shorter PFS and OS in univariable analysis. In multivariable models, logTLG independently predicted PFS (hazard ratio [HR] 2.20, 95% confidence interval [CI] 1.12-4.30; p = 0.021) and OS (HR 2.54, 95% CI 1.24-5.20; p = 0.011). Age (HR 1.07 per year; 95% CI 1.02-1.12, p = 0.007) and stage IV vs. III (HR 2.46, 95% CI 1.06-5.71; p = 0.037) were additional OS predictors. GLCM texture features were not significant. ROC analysis showed MTV (AUC 0.88) and serum lactate dehydrogenase (LDH) (AUC 0.74) best predicted early progression, while age (AUC 0.79), stage (AUC 0.73), and SUV entropy (AUC 0.75) predicted short-term mortality. Kaplan-Meier curves confirmed poorer survival with high logTLG, advanced stage, and older age.

Baseline thoracic PET/CT radiomics, particularly logTLG, independently predict survival in SCLC. MTV and LDH identify early progression, while age, stage, and SUV entropy predict short-term mortality, supporting integrated imaging-clinical risk stratification.