Integrating PET/CT-derived heterogeneity indices and composite risk scores improves prognostic stratification in breast cancer

Abstract

Purpose This study aimed to assess the prognostic significance of novel and established intratumoral heterogeneity indices (HIs) derived from F-18-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) and multiparametric composite risk scores (CRS) combining these indices with PET/CT-derived metrics, clinical parameters, and metastatic variables in breast cancer (BC) patients. Methods We retrospectively evaluated 135 BC patients who underwent [18 F]FDG PET/CT for pretreatment staging. Metabolic and volumetric data of primary tumors obtained from [18 F]FDG PET/CT images, such as the maximum, mean, peak, and minimum standardized uptake values (SUVmax, SUVmean, SUVpeak, and SUVmin), the SUV corrected for lean body mass (LBM) calculated by James's and Janmahasatian's methods (SULmax, SULmean, SULpeak, and SULmin), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), and two predefined and seven novel HIs were compared between molecular subtypes via the Kruskal-Wallis (KW) test. All relevant HI, PET/CT-derived metrics, and clinical, pathological, and metastatic variables were included in the cross-validated LASSO regression models to estimate the overall survival (OS) endpoints for 1, 2, 3, 4, and 5 years. Results Significant differences were observed between molecular subtypes for SUVmax, SUVpeak, TLG_40, and HI5 (Janma/James) (p < 0.05), with the highest values in the HER2-enriched and triple-negative (TNBC) subtypes. CRS, which combines clinical factors, metastatic status, PET/CT-derived metrics, and HI, demonstrated robust discrimination of OS (area under the curve [AUC]: 0.79-0.91) and outperformed single-parameter models. Among the heterogeneity indices, HI2 and HI4 showed the strongest independent predictions of OS at multiple time points, although a combination of multiple parameters was required for optimal prognostic accuracy. Conclusion CRS, which integrates imaging-derived heterogeneity and metabolic and clinical data, offers improved OS prediction and individualized risk stratification in BC patients.