Neuro-oncology
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The Macdonald criteria and the Response Assessment in Neuro-Oncology (RANO) criteria define radiologic parameters to classify therapeutic outcome among patients with malignant glioma and specify that clinical status must be incorporated and prioritized for overall assessment. But neither provides specific parameters to do so. We hypothesized that a standardized metric to measure neurologic function will permit more effective overall response assessment in neuro-oncology. ⋯ The NANO scale provides an objective clinician-reported outcome of neurologic function with high inter-observer agreement. It is designed to combine with radiographic assessment to provide an overall assessment of outcome for neuro-oncology patients in clinical trials and in daily practice. Furthermore, it complements existing patient-reported outcomes and cognition testing to combine for a global clinical outcome assessment of well-being among brain tumor patients.
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The management of patients with brain metastases has become a major issue due to the increasing frequency and complexity of the diagnostic and therapeutic approaches. In 2014, the European Association of Neuro-Oncology (EANO) created a multidisciplinary Task Force to draw evidence-based guidelines for patients with brain metastases from solid tumors. Here, we present these guidelines, which provide a consensus review of evidence and recommendations for diagnosis by neuroimaging and neuropathology, staging, prognostic factors, and different treatment options. Specifically, we addressed options such as surgery, stereotactic radiosurgery/stereotactic fractionated radiotherapy, whole-brain radiotherapy, chemotherapy and targeted therapy (with particular attention to brain metastases from non-small cell lung cancer, melanoma and breast and renal cancer), and supportive care.
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Glioblastoma (GBM) is the deadliest primary brain cancer in adults. Emerging innovative therapies hold promise for personalized cancer treatment. Improving therapeutic options depends on research relying on relevant preclinical models. In this line we have established in the setting of the GlioTex project (GBM and Experimental Therapeutics) a GBM patient-derived cell line (GBM-PDCL) library. A multi-omic approach was used to determine the molecular landscape of PDCL and the extent to which they represent GBM tumors. ⋯ Overall, PDCLs recapitulate pivotal molecular alterations of paired-parental tumors supporting their use as a preclinical model of GBM. However, some driver aberrations are lost or gained in the passage from tumor to PDCL. Our results support using PDCL as a relevant preclinical model of GBM. Further investigations of changes between PDCLs and their parental tumor may provide insights into GBM biology.
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Randomized Controlled Trial Multicenter Study
Baseline pretreatment contrast enhancing tumor volume including central necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials.
The prognostic significance of baseline contrast enhancing tumor prior to second- or third-line therapy in recurrent glioblastoma (GBM) for overall survival (OS) remains controversial, particularly in the context of repeated surgical resection and/or use of anti-angiogenic therapy. In the current study, we examined recurrent GBM patients from both single and multicenter clinical trials to test whether baseline enhancing tumor volume, including central necrosis, is a significant prognostic factor for OS in recurrent GBM. ⋯ Baseline tumor volume is a significant prognostic factor in recurrent GBM. Clinical trial treatment arms must have a balanced distribution of tumor size, and tumor size should be considered when interpreting therapeutic efficacy.
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High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate determination of the IDH genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI. ⋯ Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.