Biomarcadores cuantitativos de imagen como indicadores pronósticos en el carcinoma cervical de células escamosas: un...

To evaluate quantitative imaging biomarkers, including MRI tumor size, apparent diffusion coefficient (ADC), arterial-phase enhancement, and PET maximum standardized uptake value (SUVmax), as prognostic indicators of overall survival (OS), recurrence-free survival (RFS), and treatment response in cervical squamous cell carcinoma (SCC).

Fifty patients with biopsy-proven SCC who underwent pre- and post-treatment MRI and FDG-PET/CT were retrospectively analyzed. Tumor dimensions (axial, sagittal), ADC, SUVmax, and arterial enhancement were assessed. Survival was estimated by Kaplan-Meier, and associations with OS and RFS were tested using Cox regression. Logistic regression was used to determine predictors of treatment response according to RECIST and PERCIST criteria.

Tumor size, SUVmax, and ADC changed significantly post-treatment (p < 0.001). Persisting arterial enhancement was seen in 35% of tumors. Higher post-treatment SUVmax predicted worse OS (HR = 1.078, p = 0.008) and RFS (HR = 1.049, p = 0.046). Larger residual sagittal tumor size showed borderline associations with inferior OS (HR = 1.38, p = 0.089) and RFS (HR = 1.31, p = 0.057). Increases in tumor size significantly correlated with persistent arterial enhancement (axial OR = 1.58, p = 0.025; sagittal OR = 1.93, p = 0.002). Persistent enhancement trended toward worse RFS (HR ≈ 2.24, p = 0.107). In multivariable analysis, post-treatment pelvic lymph node positivity remained independently associated with poorer RFS. Baseline ADC predicted metabolic response: a higher pre-treatment ADC was associated with a failure to achieve a partial metabolic response per PERCIST (OR = 1.007, p = 0.015).

Post-treatment SUVmax, residual tumor size, arterial enhancement, and pelvic lymph node involvement are key prognostic indicators in cervical SCC. Persistent enhancement may reflect treatment-resistant vascularity. Integrating these biomarkers into clinical workflows may improve risk stratification and guide personalized management.