Citations

Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer 18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods: 18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up 18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial 18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU 18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.

PurposeA Positron Emission Tomography/Computed Tomography quality assurance program is necessary to ensure that patients receive optimal imaging and care. We summarize the AAPM Task Group (TG) 126 report on acceptance and quality assurance (QA) testing of PET/CT systems.MethodsTG 126 was charged with developing PET/CT acceptance testing and QA procedures. The TG aimed to develop procedures that would allow for standardized evaluation of existing short‐axis cylindrical‐bore PET/CT systems in the spirit of NEMA NU 2 standards without requiring specialized phantoms or proprietary software tools.ResultsWe outline eight performance evaluations using common phantoms and freely available software whereby the clinical physicist monitors each PET/CT system by comparing periodic Follow‐Up Measurements to Baseline Measurements acquired during acceptance testing. For each of the eight evaluations, we also summarize the expected testing time and materials necessary and the recommended pass/fail criteria.ConclusionOur report provides a guideline for periodic evaluations of most clinical PET/CT systems that simplifies procedures and requirements outlined by other agencies and will facilitate performance comparisons across vendors, models, and institutions.