Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference.

Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.

18F-fluoro-ethyl-tyrosine positron emission tomography for grading and estimation of prognosis in patients with intracranial gliomas.

INTRODUCTION: Histopathological examination is the standard for grading and determination of diagnosis in intrinsic brain tumors though the possibility of malignization and tumor heterogeneity always bears the possibility of tumor under-grading or misjudgement regarding the estimation of prognosis. The aim of the present study was to evaluate the use of (18)F-FET-PET (FET-PET) for the grading and estimation of prognosis in newly diagnosed patients with intracranial gliomas in a clinical setting. METHODS: Patients who were treated for a newly diagnosed intracranial glioma between January 2007 and May 2012, and had a preoperative FET-PET and MRI scan between were included. The ratio of counts in a tumor VOI (volume of interest) with maximum uptake to the respective counts in a background VOI was calculated to provide the tumor-to-normal (T/N) ratio. The clinical and histopathological data (tumor grading, pre- and postoperative neurological status, Karnofsky Performance Status Scale scores, and overall survival rates) were recorded. RESULTS: One hundred fifty-two patients (39 WHO II, 26 WHO III, 87 WHO IV) were included. The median T/N ratio was 2.81 (1.1-8.1). The median T/N ratio of low-grade glioma patients was 1.65 (1.1-3.7), and 3.14 (1.61-8.1, p<0.001) in high-grade glioma patients. The median survival for patients with WHO III tumors was 22.8 months (95% CI: 15.87%-NA) and 13.23 months (95% CI: 10.83-15.6.%) for patients with WHO IV tumors (p=0.0001). For T/N≤1.6, no deaths were recorded; for 1.63, median survival was 14.0 months (95% CI: 11.7-16.2%, p<0.001). The test of the maximally selected log-rank statistic resulted in a T/N ratio of 1.88 as the cut-off value, with the greatest difference in overall survival between patients with longer and shorter survival. The ROC curve for differentiation of low- vs. high-grade tumors with regard to the T/N ratio showed an area under the curve (AUC) of 0.903. Regarding the prognostic validity for overall survival ROC-curves for 12-month, 24-month and 48-month survival display a higher validity for the WHO-classification than for the imaging modalities though with an AUC of 0.847 for the 48-month survival T/N ratio and MRI contrast-enhancement have a high prognostic value as well. CONCLUSION: Our study suggests that FET-PET can predict prognosis and survival in patients harboring intracranial gliomas and serves as a valuable tool to supplement the established clinical and histopathological parameters.