UCSF

Glioblastoma (GBM) remains the most aggressive cancer of the brain. Typical survival in patients with GBM is around 12-18 months, due to local or distant recurrence that occurs even after treatment with radiation and chemotherapy. Although there have been improvements in modern imaging, the radiation planning protocols are still based purely on a 2 cm geometric expansion of conventional post-contrast T1-weighted and T2-weighted FLAIR anatomic sequences. As a result, about 60% of tissue within the high dose treatment field can be normal brain tissue, which can damage to healthy brain function, while microscopic disease farther from the primary tumor bed is untreated. The main goal of this project was to develop a pipeline for the integration of probability maps derived from metabolic and diffusion-weighted MRI into the clinical workflow for radiation treatment planning. 24 patients with newly-diagnosed glioblastoma, who had undergone RT and chemotherapy consisting of an anti-angiogenic agent, were scanned at baseline prior to therapy and had serial follow-up imaging every 2 months until progression. Four patients were excluded because of either a poor initial model fit that resulted in inaccurate probability maps or poor image quality at the time of progression, leaving a total of 20 patients for evaluating our automatic contouring algorithm. First, we determined the optimal threshold for each patient’s probability map based on ROC analysis of the overlap of probability map with the progressed lesion. This value was then projected back on the histogram to automatically calculate each patient’s threshold based on the individual patient’s histogram and a maximum distance cutoff of 5 cm based on standard clinical procedures of high dose delivery during RT planning. Our results show that we were able to develop an automated contouring routine for integration of metabolic and physiology imaging into clinical workflow for radiation treatment planning. Incorporating a maximum distance from the original lesion allowed the automatic selection of a threshold from a consistent position on the histogram of the probability maps that optimized the overlap with the progressed lesion.