[18F]F-FAPI PET/CT outperforms [18F]F-FDG PET/CT in predicting treatment response in lupus nephritis.

Abstract

To identify predictors of complete response (CR) following induction therapy in lupus nephritis (LN) and comparatively evaluate the predictive value of baseline [18 F]F-FAPI PET/CT versus [18 F]F-FDG PET/CT for treatment short-term outcomes. Twenty biopsy-proven LN patients were prospectively enrolled and underwent dual-tracer PET/CT imaging, using both [18 F]F-FAPI and [18 F]F-FDG imaging prior to standardized induction therapy. Treatment response was assessed at 6 months, categorizing into CR, partial response (PR), and non-response (NR) groups. Associations between PET metrics, clinical biomarkers and treatment response were analyzed. Semiquantitative PET parameters including renal standardized uptake values (SUVmax) and target-to-background ratio (TBR) were calculated. Baseline renal FAPI SUVmax was inversely correlated with estimated glomerular filtration rate (eGFR) and positively with erythrocyte sedimentation rate (ESR) (P < 0.05 for both), whereas FDG SUVmax showed no significant correlation with either parameter. At 6 months, 50% of patients achieved CR, 25% PR, and 25% NR. Patients with CR exhibited significantly lower baseline renal FAPI SUVmax and TBR compared to those with non-CR (PR + NR) (P < 0.01). Visual assessment of FAPI uptake outperformed FDG in predicting CR, with higher accuracy (85% vs. 70%), specificity (90% vs. 50%), and positive predictive value (PPV, 89% vs. 64%), while sensitivity and negative predictive value (NPV) were comparable. Univariate logistic regression identified age > 35 years (P < 0.05) and negative baseline FAPI uptake (P < 0.01) as significant predictors of CR. In multivariate analysis, negative FAPI uptake remained the only independent predictor (P < 0.01). [18 F]F-FAPI PET/CT demonstrates superior predictive performance over [18 F]F-FDG PET/CT for short-term treatment response in lupus nephritis. It holds promise as a noninvasive imaging biomarker to guide clinical decision-making and optimize individualized therapy.