Amino-acid PET as a prognostic tool after post Stupp protocol temozolomide therapy in high-grade glioma patients.

PURPOSE: This study aimed to evaluate the prognostic performance of amino-acid PET in high-grade gliomas (HGG) patients at the time of temozolomide (TMZ) treatment discontinuation, after the Stupp protocol. METHODS: The analysis included consecutive HGG patients with dynamic [18F]FDOPA PET imaging within 3 months of the end of TMZ therapy, post-Stupp protocol. Static and dynamic PET parameters, responses to RANO criteria for MRI and clinical and histo-molecular factors were correlated to progression-free (PFS). RESULTS: Thirty-two patients (59.4 [54.0;67.6] years old, 13 (41%) women) were included. Static PET parameters peak tumor-to-background ratio and metabolic tumor volume (respective thresholds of 1.9 and 1.5 mL) showed the best 84% accuracies for predicting PFS at 6 months (p = 0.02). These static PET parameters were also independent predictor of PFS in multivariate analysis (p ≤ 0.05). CONCLUSION: In HGG patients having undergone a Stupp protocol, the absence of significant PET uptake after TMZ constitutes a favorable prognostic factor.

The role of [18 F]FDOPA PET as an adjunct to conventional MRI in the diagnosis of aggressive glial lesions.

BACKGROUND: Amino acid PET is recommended for the initial diagnosis of brain lesions, but its value for identifying aggressive lesions remains to be established. The current study therefore evaluates the added-value of dynamic [18 F]FDOPA PET as an adjunct to conventional MRI for determining the aggressiveness of presumed glial lesions at diagnosis. METHODS: Consecutive patients, with a minimal 1 year-follow-up, underwent contrast-enhanced MRI (CE MRI) and dynamic [18 F]FDOPA PET to characterize their suspected glial lesion. Lesions were classified semi-automatically by their CE MRI (MRI-/+), and PET parameters (static tumor-to-background ratio, TBR; dynamic time-to-peak ratio, TTPratio). Diagnostic accuracies of MRI and PET parameters for the differentiation of tumor aggressiveness were evaluated by chi-square test or receiver operating characteristic analyses. Aggressive lesions were either defined as lesions with dismal molecular characteristics based on the WHO 2021 classification of brain tumors or with compatible clinico-radiological profiles. Time-to-treatment failure (TTF) and overall survival (OS) were evaluated. RESULTS: Of the 109 patients included, 46 had aggressive lesions (45 confirmed by histo-molecular analyses). CE MRI identified aggressive lesions with an accuracy of 73%. TBRmax (threshold of 3.2), and TTPratio (threshold of 5.4 min) respectively identified aggressive lesions with an accuracy of 83% and 76% and were independent of CE MRI and clinical factors in the multivariate analysis. Among the MRI-lesions, 11/56 (20%) were aggressive and respectively 55% and 50% of these aggressive lesions showed high TBRmax and short TTPratio in PET. High TBRmax and short TTPratio in PET were significantly associated to poorer survivals (p ≤ 0.009). CONCLUSION: Dynamic [18 F]FDOPA PET provides a similar diagnostic accuracy as contrast enhancement in MRI to identify the aggressiveness of suspected glial lesions at diagnosis. Both methods, however, are complementary and [18 F]FDOPA PET may be a useful additional tool in equivocal cases.

Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma.

This study assesses the feasibility of using a sample-efficient model to investigate radiomics changes over time for predicting progression-free survival in rare diseases. Eighteen high-grade glioma patients underwent two L-3,4-dihydroxy-6-[18F]-fluoro-phenylalanine positron emission tomography (PET) dynamic scans: the first during treatment and the second at temozolomide chemotherapy discontinuation. Radiomics features from static/dynamic parametric images, alongside conventional features, were extracted. After excluding highly correlated features, 16 different models were trained by combining various feature selection methods and time-to-event survival algorithms. Performance was assessed using cross-validation. To evaluate model robustness, an additional dataset including 35 patients with a single PET scan at therapy discontinuation was used. Model performance was compared with a strategy extracting informative features from the set of 35 patients and applying them to the 18 patients with 2 PET scans. Delta-absolute radiomics achieved the highest performance when the pipeline was directly applied to the 18-patient subset (support vector machine (SVM) and recursive feature elimination (RFE): C-index = 0.783 [0.744-0.818]). This result remained consistent when transferring informative features from 35 patients (SVM + RFE: C-index = 0.751 [0.716-0.784], p = 0.06). In addition, it significantly outperformed delta-absolute conventional (C-index = 0.584 [0.548-0.620], p < 0.001) and single-time-point radiomics features (C-index = 0.546 [0.512-0.580], p < 0.001), highlighting the considerable potential of delta radiomics in rare cancer cohorts.