Cerebral blood volume and apparent diffusion coefficient - Valuable predictors of non-response to bevacizumab treatment in patients with recurrent glioblastoma
Language English Country Netherlands Media print-electronic
Document type Journal Article
PubMed
31476621
DOI
10.1016/j.jns.2019.116433
PII: S0022-510X(19)30365-X
Knihovny.cz E-resources
- Keywords
- Apparent diffusion coefficient, Bevacizumab, Cerebral blood volume, Glioblastoma multiforme, Glioma therapy response, Machine learning,
- MeSH
- Antineoplastic Agents, Alkylating therapeutic use MeSH
- Bevacizumab therapeutic use MeSH
- Time Factors MeSH
- Diffusion Magnetic Resonance Imaging MeSH
- Adult MeSH
- Glioblastoma drug therapy pathology MeSH
- Combined Modality Therapy MeSH
- Humans MeSH
- Neoplasm Recurrence, Local drug therapy MeSH
- Brain Neoplasms drug therapy pathology MeSH
- Cerebral Blood Volume drug effects physiology MeSH
- Predictive Value of Tests * MeSH
- Disease-Free Survival MeSH
- Antineoplastic Agents, Immunological therapeutic use MeSH
- Radiotherapy MeSH
- Retrospective Studies MeSH
- Machine Learning MeSH
- Temozolomide therapeutic use MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Antineoplastic Agents, Alkylating MeSH
- Bevacizumab MeSH
- Antineoplastic Agents, Immunological MeSH
- Temozolomide MeSH
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Department of Neurology Mayo Clinic 200 1st St SW Rochester MN 55905 United States of America
Department of Neurosurgery Mayo Clinic 200 1st St SW Rochester MN 55905 United States of America
Department of Neurosurgery Medical University Innsbruck 6020 Innsbruck Austria
Department of Oncology Mayo Clinic 200 1st St SW Rochester MN 55905 United States of America
Department of Radiology Mayo Clinic 200 1st St SW Rochester MN 55905 United States of America
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