Analysis of Imaging Signatures in 18 F-DOPA PET of Glioblastoma Treated with Dose-Escalated Radiotherapy

Jing Qian^1*Deanna H Pafundi²William Breen¹Paul D Brown¹Christopher H Hunt³Mark S Jacobson³Derek Richard Johnson³Timothy J Kaufmann³Bradley J Kemp³Sani Haider Kizilbash⁴Val Lowe³Michael Ruff⁵Jann Sarkaria¹Joon H Uhm^4,6Mark J Zakhary⁷Maasa H Seaberg⁸Hok Wan Chan Tseung¹Elizabeth Yan¹Yan Zhang¹Nadia N Laack¹Debra H Brinkmann^1*

• ¹Department of Radiation Oncology, Mayo Clinic, Rochester, United States
• ²Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, United States
• ³Department of Radiology, Mayo Clinic, Rochester, United States
• ⁴Department of Medical Oncology, Mayo Clinic, Rochester, United States
• ⁵Department of Neurology, Mayo Clinic, Rochester, United States
• ⁶Mayo Clinic, Department of Neurology, Rochester, MN, United States
• ⁷University of Maryland, Department of Radiation Oncology, Baltimore, MD, United States
• ⁸University of California San Francisco Medical Center, San Francisco, CA, United States

Background/Objectives: 18 F-DOPA is an amino acid radiotracer with high uptake in glioblastoma and low uptake in normal brain. Patients underwent pre-radiation and post-radiation 18 F-DOPA PET scans on a prospective clinical trial. This analysis investigates quantitative image features correlated with prognosis and treatment response to identify patients who benefit the most from dose-escalated therapy. Methods: Quantitative image features from 18 F-DOPA PET scans of 58 glioblastoma patients were extracted from the high uptake region (TBR>2.0) in both pre-RT and early post-RT follow-up PET images, which were then refined using Pearson pair correlation. To explore the possibility to identify patients who benefit the most from dose-escalated therapy, pre-irradiation features were identified with univariate Cox regression analysis. Classifications with simple threshold or with Decision Tree models were carried out to categorize patients into distinct survival groups. Additionally, the features with notable changes before and after RT were identified and the temporal patterns of these changes between the survival groups were compared. Multivariates cox analysis was performed to assess the prognostic value of delta features in survival analysis; Results: The pre-irradiation features demonstrated predictive capability in distinguishing survival groups, yielding an accuracy of 0.78 on the reserved test dataset. We also pinpointed eight quantitative features that exhibited a significant difference before and after radiotherapy in patients with MGMT-unmethylated glioblastoma. The change of the features presented different patterns between the survival groups separated by median overall survival and the inclusion of delta features can enhance the accuracy of survival analysis. Conversely, for patients with methylated MGMT, no feature displayed such significant changes between preRT and early postRT. Conclusions: Our study showcased the potential of employing quantitative features derived from 18 F-DOPA images to refine the stratification of patients with unmethylated MGMT for dose escalated therapy. Moreover, the change of these features can serve as valuable tools for monitoring treatment responses following radiotherapy.