⁶⁸Ga-FAPI and ¹⁸F-FDG PET/CT for predicting pathologic response and progression-free survival in locally advanced...

To evaluate the predictive value of dual-tracer ⁶⁸Ga-FAPI and ¹⁸F-FDG PET/CT for pathologic tumor regression grade (TRG) and progression-free survival (PFS) in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT).

This retrospective analysis of a prospective trial (ChiCTR2100051599) included patients with LA-ESCC enrolled from February 2022 to August 2024. Patients received nCRT and underwent dual-tracer PET/CT at baseline (S1) and post-treatment (before esophagectomy, S2). Intensity-, volume-, and distribution-based PET features, including total lesion glycolysis, were extracted. LASSO regression was employed for feature selection. Eight models with different variable combinations were established by logistic regression and cox regression. Associations with TRG and PFS were evaluated using receiver operating characteristic (ROC) curves, Harrell's C-index and Kaplan-Meier estimates. The models were compared pair-to-pair to identify the added value of imaging parameters.

Forty-nine patients (mean age 65.8 ± 5.7 years) were included. TRG 0 was achieved in 29/49 (59.2%) patients. Eleven patients (11/48, 22.9%) experienced a PFS event within the cohort. The combined model incorporating Clinical features with (S1 + S2) FAPI parameters achieved the highest AUC of 0.95 (95% confidence interval (CI): 0.84-1.00) for TRG prediction, while the Clinical + S2 FDG model demonstrated the best PFS prediction with C-index of 0.93 (95% CI: 0.82-0.97). Pairwise comparisons revealed that FAPI-based models significantly outperformed clinical-only models for TRG prediction (p = 0.003). BMI and FAPI heterogeneity parameters were independent predictors of TRG, while clinical staging and FDG distribution features predicted PFS.

Baseline FAPI PET provides superior value for predicting pathological response, while post-treatment FDG PET offers better prognostic stratification. The combination of imaging biomarkers with clinical factors might enable robust prediction of treatment outcomes in LA-ESCC.