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
Patterns of care and outcomes for use of concurrent chemoradiotherapy over radiotherapy alone for anaplastic gliomas
Yeboa DN, Rutter CE, Park HS, Lester-Coll NH, Corso CD, Mancini BR, Bindra RS, Contessa J, Yu JB. Patterns of care and outcomes for use of concurrent chemoradiotherapy over radiotherapy alone for anaplastic gliomas. Radiotherapy And Oncology 2017, 125: 258-265. PMID: 29054377, DOI: 10.1016/j.radonc.2017.09.027.Peer-Reviewed Original ResearchConceptsUse of CCRTConcurrent chemoradiotherapyPatterns of careAnaplastic gliomasOverall survivalCox proportional hazards regression modelingProportional hazards regression modelingMultivariable logistic regression analysisConcurrent CRTNational Cancer DatabaseKaplan-Meier analysisLog-rank testLogistic regression analysisGrade III gliomasAdjusted hazardAdult patientsImproved survivalCancer DatabaseDesign cohortRadiotherapyPropensity scorePatientsGliomasChemoradiotherapyRegression modelingBi‐allelic alterations in DNA repair genes underpin homologous recombination DNA repair defects in breast cancer
Mutter RW, Riaz N, Ng CK, Delsite R, Piscuoglio S, Edelweiss M, Martelotto LG, Sakr RA, King TA, Giri DD, Drobnjak M, Brogi E, Bindra R, Bernheim G, Lim RS, Blecua P, Desrichard A, Higginson D, Towers R, Jiang R, Lee W, Weigelt B, Reis‐Filho J, Powell SN. Bi‐allelic alterations in DNA repair genes underpin homologous recombination DNA repair defects in breast cancer. The Journal Of Pathology 2017, 242: 165-177. PMID: 28299801, PMCID: PMC5516531, DOI: 10.1002/path.4890.Peer-Reviewed Original ResearchConceptsBreast cancerGermline BRCA1/BRCA2 mutationsBRCA1/BRCA2 mutationsPrecision medicine-based approachPrimary breast cancerTumour-specific DNA repair defectsSporadic breast cancerGermline genetic alterationsBi-allelic lossWhole-exome sequencingSpecific mutational signaturesComprehensive genetic assessmentBRCA2 mutationsLarge-scale state transitionsBi-allelic alterationsCancerGenetic alterationsDNA repair defectsMutational signaturesTherapyAlterationsRepair defectsGene expressionGenetic assessmentHR genes
2016
Adjuvant chemotherapy and overall survival in adult medulloblastoma
Kann BH, Lester-Coll NH, Park HS, Yeboa DN, Kelly JR, Baehring JM, Becker KP, Yu JB, Bindra RS, Roberts KB. Adjuvant chemotherapy and overall survival in adult medulloblastoma. Neuro-Oncology 2016, 19: 259-269. PMID: 27540083, PMCID: PMC5464064, DOI: 10.1093/neuonc/now150.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overAntineoplastic Combined Chemotherapy ProtocolsCerebellar NeoplasmsChemoradiotherapy, AdjuvantChemotherapy, AdjuvantCraniospinal IrradiationFemaleFollow-Up StudiesHumansMaleMedulloblastomaMiddle AgedNeoplasm StagingPrognosisRadiotherapy, AdjuvantSurvival RateYoung AdultConceptsGy craniospinal irradiationCraniospinal irradiationOverall survivalM0 patientsAdjuvant chemotherapyAdult MBMultivariable Cox proportional hazard modelingHigh-dose craniospinal irradiationNational Cancer Data BaseCox proportional hazard modelingSuperior overall survivalPlanned subgroup analysisMultivariable logistic regressionNational database analysisLog-rank testProportional hazard modelingPediatric medulloblastoma patientsCSI dosesPostoperative chemotherapySurgical resectionSurvival impactYear OSMultivariable analysisSubgroup analysisRisk factors