Hunt, Christopher H

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.

A 23-year-old gentleman presented to the emergency department with new onset neurological dysfunction and painful oral and genital ulcers. Biopsies of the lesions noted non-specific inflammatory changes. MRI of the brain revealed a large mass-like lesion involving the pons, medulla, midbrain and surrounding cerebral tissue with T2 signal enhancement that was not amenable for biopsy. Both the ulcer biopsy results and the MRI findings could be attributed to either central nervous system (CNS) lymphoma or CNS Behçet's disease. A diagnosis of CNS Behçet's disease was thought to be most likely based on the clinical presentation and MRI findings. He was treated with high-dose immunosuppression and made a significant recovery.