Cerebral blood volume and apparent diffusion coefficient – Valuable predictors of non-response to bevacizumab treatment in patients with recurrent glioblastoma

• Published in: Journal of the neurological sciences Vol. 405; p. 116433
• Main Authors: Petrova, Lucie, Korfiatis, Panagiotis, Petr, Ondra, LaChance, Daniel H., Parney, Ian, Buckner, Jan C., Erickson, Bradley J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 15.10.2019
• Subjects: Apparent diffusion coefficient; Bevacizumab; Cerebral blood volume; Glioblastoma multiforme; Glioma therapy response; Machine learning; Neurology; Glioma therapy response; Cerebral blood volume; Glioblastoma multiforme; Machine learning; Bevacizumab; Apparent diffusion coefficient
• ISSN: 0022-510X; 1878-5883; 1878-5883
• DOI: 10.1016/j.jns.2019.116433

Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. The core of standard of care for newly diagnosed GBM was established in 2005 and includes maximum feasible surgical resection followed by radiation and temozolomide, with subsequent temozolomide with or without tumor-treating fields. Unfortunately, nearly all patients experience a recurrence. Bevacizumab (BV) is a commonly used second-line agent for such recurrences, but it has not been shown to impact overall survival, and short-term response is variable. We collected MRI perfusion and diffusion images from 54 subjects with recurrent GBM treated only with radiation and temozolomide. They were subsequently treated with BV. Using machine learning, we created a model to predict short term response (6 months) and overall survival. We set time thresholds to maximize the separation of responders/survivors versus non-responders/short survivors. We were able to segregate 21 (68%) of 31 subjects into unlikely to respond categories based on Progression Free Survival at 6 months (PFS6) criteria. Twenty-two (69%) of 32 subjects could similarly be identified as unlikely to survive long using the machine learning algorithm. With the use of machine learning techniques to evaluate imaging features derived from pre- and post-treatment multimodal MRI, it is possible to identify an important fraction of patients who are either highly unlikely to respond, or highly likely to respond. This can be helpful is selecting patients that either should or should not be treated with BV. •Subjects with recurrent GBM likely or not likely to have improved overall survival to bevacizumab can be idenitfied with MRI.•The prediction of Progression Free Survival at 6 months for recurrent GBM patients treated with bevacizumab was relatively poor•Functional MRI of GBM patients can help guide whether patients should be treated with bevacizumab