Beyond a Universal Cut-Off: [18F]FET-PET Metrics for Distinguishing High-Grade Glioma Recurrence from Treatment-Related Changes

Background/Objectives: Differentiating tumour recurrence from treatment-related changes (TRC) after multimodal treatment of high-grade gliomas (HGGs) remains challenging because of the overlap in imaging findings on conventional magnetic resonance. This study compared the diagnostic performance of the main static semiquantitative O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography ([18F]FET-PET) parameters and assessed the diagnostic contribution of qualitative tracer uptake. Methods: This retrospective single-centre study included 77 scans from 61 adult patients with previously resected and chemoradiotherapy-treated HGGs undergoing [18F]FET-PET for suspected recurrence. Standardised uptake values (SUVs), tumour-to-background ratios (TBRs), and metabolic tumour volume (MTV) were analysed. Recurrence was determined using a composite histopathological and clinical–radiological reference standard. Diagnostic performance was assessed using receiver operating characteristic analysis, Youden-derived cut-offs and DeLong comparisons. Internal validation was performed by using patient-grouped five-fold cross-validation, in which cut-offs were re-derived within each training partition and applied to the held-out partition. Results: Forty-nine scans were classified as recurrence and 28 as TRC. All semiquantitative parameters except MTV were significantly higher in recurrent disease after false-discovery-rate correction. TBR mean ratio showed the highest accuracy (AUC, 0.80; cut-off, 1.85; sensitivity, 77.6%; specificity, 75.0%), followed by SUV max (AUC, 0.793). Neither prespecified comparison among TBR parameters was significant. The presence of pathological uptake achieved 100% sensitivity, 42.9% specificity, 100% NPV, and 79.2% accuracy. MTV was non-discriminative (AUC, 0.499). SUV max differed significantly between scanners (p = 0.008). Conclusions: Static semiquantitative [18F]FET-PET parameters, except MTV, showed comparable moderate-to-good performance for distinguishing recurrence from TRC. TBRmean may represent a pragmatic primary marker due to its background normalisation reduction of scanner-related variability. Guideline-recommended thresholds should be regarded as reference values requiring local validation and, when appropriate, centre- and protocol-specific calibration.

Authors

Institutions

Publication Details

Journal
Brain Sciences
Published
2026-10-09
DOI
https://doi.org/10.3390/brainsci16101085
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond a Universal Cut-Off: [18F]FET-PET Metrics for Distinguishing High-Grade Glioma Recurrence from Treatment-Related Changes

Nadia Oberhofer, Matteo Bonatti, Alessandro Spimpolo, Davide Costazza et al.
Brain Sciences
Glioma Diagnosis and Treatment
article

Beyond a Universal Cut-Off: [18F]FET-PET Metrics for Distinguishing High-Grade Glioma Recurrence from Treatment-Related Changes

Nadia Oberhofer, Matteo Bonatti, Alessandro Spimpolo, Davide Costazza, Pier Paolo Berti, Luca Zavatto, Francesco Erdini, Francesco Doglietto, Davide Drusiani, Paolo Cecchi, Mohsen Farsad, Giuseppe Maria Della Pepa, Vania Pirillo, Andreas Schwarz, Matteo Presa, Leda Lorenzon
article en

Abstract

Background/Objectives: Differentiating tumour recurrence from treatment-related changes (TRC) after multimodal treatment of high-grade gliomas (HGGs) remains challenging because of the overlap in imaging findings on conventional magnetic resonance. This study compared the diagnostic performance of the main static semiquantitative O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography ([18F]FET-PET) parameters and assessed the diagnostic contribution of qualitative tracer uptake. Methods: This retrospective single-centre study included 77 scans from 61 adult patients with previously resected and chemoradiotherapy-treated HGGs undergoing [18F]FET-PET for suspected recurrence. Standardised uptake values (SUVs), tumour-to-background ratios (TBRs), and metabolic tumour volume (MTV) were analysed. Recurrence was determined using a composite histopathological and clinical–radiological reference standard. Diagnostic performance was assessed using receiver operating characteristic analysis, Youden-derived cut-offs and DeLong comparisons. Internal validation was performed by using patient-grouped five-fold cross-validation, in which cut-offs were re-derived within each training partition and applied to the held-out partition. Results: Forty-nine scans were classified as recurrence and 28 as TRC. All semiquantitative parameters except MTV were significantly higher in recurrent disease after false-discovery-rate correction. TBR mean ratio showed the highest accuracy (AUC, 0.80; cut-off, 1.85; sensitivity, 77.6%; specificity, 75.0%), followed by SUV max (AUC, 0.793). Neither prespecified comparison among TBR parameters was significant. The presence of pathological uptake achieved 100% sensitivity, 42.9% specificity, 100% NPV, and 79.2% accuracy. MTV was non-discriminative (AUC, 0.499). SUV max differed significantly between scanners (p = 0.008). Conclusions: Static semiquantitative [18F]FET-PET parameters, except MTV, showed comparable moderate-to-good performance for distinguishing recurrence from TRC. TBRmean may represent a pragmatic primary marker due to its background normalisation reduction of scanner-related variability. Guideline-recommended thresholds should be regarded as reference values requiring local validation and, when appropriate, centre- and protocol-specific calibration.

Brain SciencesVol. 16(10)
Free University of Bozen-Bolzano (IT), Agostino Gemelli University Polyclinic (IT), Ospedale di Bolzano (IT), Azienda Sanitaria dell'Alto Adige (IT)
Openalex Percentile: Top 13%
Glioma Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.