2019
Defining an Intermediate-risk Group for Low-grade Glioma: A National Cancer Database Analysis
JAIRAM V, KANN BH, PARK HS, MICCIO JA, BECKTA JM, YU JB, PRABHU RS, GAO SJ, MEHTA MP, CURRAN WJ, BINDRA RS, CONTESSA JN, PATEL KR. Defining an Intermediate-risk Group for Low-grade Glioma: A National Cancer Database Analysis. Anticancer Research 2019, 39: 2911-2918. PMID: 31177129, DOI: 10.21873/anticanres.13420.Peer-Reviewed Original ResearchConceptsIntermediate-risk groupInferior overall survivalOverall survivalAdjuvant therapyLow-grade gliomasTumor sizePrognostic featuresMultivariate analysisPre-operative tumor sizeNational Cancer Database AnalysisNational Cancer DatabaseLow-risk patientsCohort of patientsKaplan-Meier methodPoor prognostic featuresGross total resectionHigh-risk groupPatterns of careAdditional prognostic featuresRTOG 9802Clinical factorsTotal resectionCancer DatabaseRisk groupsClinical classification
2018
Residual Convolutional Neural Network for Determination of IDH Status in Low- and High-grade Gliomas from MR Imaging
Chang K, Bai HX, Zhou H, Su C, Bi WL, Agbodza E, Kavouridis VK, Senders JT, Boaro A, Beers A, Zhang B, Capellini A, Liao W, Shen Q, Li X, Xiao B, Cryan J, Ramkissoon S, Ramkissoon L, Ligon K, Wen PY, Bindra RS, Woo J, Arnaout O, Gerstner ER, Zhang PJ, Rosen BR, Yang L, Huang RY, Kalpathy-Cramer J. Residual Convolutional Neural Network for Determination of IDH Status in Low- and High-grade Gliomas from MR Imaging. Clinical Cancer Research 2018, 24: clincanres.2236.2017. PMID: 29167275, PMCID: PMC6051535, DOI: 10.1158/1078-0432.ccr-17-2236.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBrainBrain NeoplasmsDatasets as TopicFemaleGliomaHumansImage Processing, Computer-AssistedIsocitrate DehydrogenaseMagnetic Resonance ImagingMaleMiddle AgedMutationNeoplasm GradingNeural Networks, ComputerPredictive Value of TestsPreoperative PeriodRetrospective StudiesYoung AdultConceptsResidual convolutional neural networkConvolutional neural networkNeural networkDeep learning techniquesTesting setNeural network modelMulti-institutional data setCancer Imaging ArchiveLearning techniquesTesting accuracyNetwork modelTraining setPrediction accuracyPreoperative radiographic dataClin Cancer ResData setsConventional MR imagingHospital of UniversityIsocitrate dehydrogenase (IDH) mutationPreoperative imagingLonger survivalWomen's HospitalGrade IINetworkTreatment decisions
2017
Pediatric high-grade glioma: current molecular landscape and therapeutic approaches
Braunstein S, Raleigh D, Bindra R, Mueller S, Haas-Kogan D. Pediatric high-grade glioma: current molecular landscape and therapeutic approaches. Journal Of Neuro-Oncology 2017, 134: 541-549. PMID: 28357536, DOI: 10.1007/s11060-017-2393-0.Peer-Reviewed Original ResearchConceptsHigh-grade gliomasPediatric populationLow-grade malignancyCurrent molecular landscapeGlial tumorsAnaplastic astrocytomaTherapeutic approachesAdult populationAdult counterpartsPrimary settingGliomasMolecular landscapeChildrenPopulationMalignancyAstrocytomasMolecular geneticsTumorsGlioblastoma