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Diagnostic Performance of [99mTc]Tc-UBI 29-41 SPECT/CT with a Standardized Semiquantitative Target-to-Liver Ratio Analysis in Four Difficult-to-Diagnose Bacterial Infection Conditions: A Prospective Diagnostic Accuracy Study

Year: 2026

Type: article

Abstract: Objectives: To assess whether [99mTc]Tc-UBI 29-41 SPECT/CT combined with a standardized target-to-liver ratio can reliably support clinical decision-making in patients with difficult-to-diagnose bacterial infections and to establish a reproducible diagnostic threshold applicable across four infectious scenarios. Methods: This prospective diagnostic accuracy study, conducted following STARD guidelines, included 156 consecutive patients (346 lesions) with clinical suspicion of infected arthroplasty, spondylodiscitis, osteomyelitis, or fever of unknown origin. Target-to-liver total count ratios were calculated from SPECT/CT images. Diagnostic performance was assessed using bootstrap-resampled ROC analysis, Fagan nomograms, and penalized logistic regression. Results: All four conditions showed good discriminatory capacity (AUC: 0.965–0.979). At a cutoff ratio ≥ 1.5, specificities exceeded 95% and negative predictive values were ≥97.8% across all subgroups. Post-test probability after a negative scan decreased to 2–3% in every condition, consistent with clinically meaningful rule-out capability across all four scenarios. Penalized logistic regression confirmed significant associations between elevated ratios and confirmed infection (OR: 22.02–245.53; all p ≤ 0.003). Conclusions: [99mTc]Tc-UBI 29-41 SPECT/CT with target-to-liver ratio analysis provides clinically meaningful diagnostic support across four distinct infectious scenarios, with particular strength as a rule-out test. A threshold of ≥1.5 offers a standardized, potentially reproducible criterion that may guide clinical decisions and reduce reliance on invasive confirmatory procedures. Prospective multicentre validation is warranted to establish this bacteria-specific approach as a practical complement to existing diagnostic algorithms.

Source: Journal of Clinical Medicine

DOI: 10.3390/jcm15124665