Cerebral blood volume and apparent diffusion coefficient - Valuable predictors of non-response to bevacizumab treatment in patients with recurrent glioblastoma
Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články
PubMed
31476621
DOI
10.1016/j.jns.2019.116433
PII: S0022-510X(19)30365-X
Knihovny.cz E-zdroje
- Klíčová slova
- Apparent diffusion coefficient, Bevacizumab, Cerebral blood volume, Glioblastoma multiforme, Glioma therapy response, Machine learning,
- MeSH
- alkylační protinádorové látky terapeutické užití MeSH
- bevacizumab terapeutické užití MeSH
- časové faktory MeSH
- difuzní magnetická rezonance MeSH
- dospělí MeSH
- glioblastom farmakoterapie patologie MeSH
- kombinovaná terapie MeSH
- lidé MeSH
- lokální recidiva nádoru farmakoterapie MeSH
- nádory mozku farmakoterapie patologie MeSH
- objem krve v mozku účinky léků fyziologie MeSH
- prediktivní hodnota testů * MeSH
- přežití bez známek nemoci MeSH
- protinádorové látky imunologicky aktivní terapeutické užití MeSH
- radioterapie MeSH
- retrospektivní studie MeSH
- strojové učení MeSH
- temozolomid terapeutické užití MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- alkylační protinádorové látky MeSH
- bevacizumab MeSH
- protinádorové látky imunologicky aktivní MeSH
- temozolomid 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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