2023
Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas
Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay E, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo I, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas. Scientific Reports 2023, 13: 22942. PMID: 38135704, PMCID: PMC10746716, DOI: 10.1038/s41598-023-48918-4.Peer-Reviewed Original ResearchConceptsInformatics platformDeep learning algorithmsImaging featuresCDKN2A alterationsLearning algorithmHeterozygous lossHomozygous deletionLarge datasetsDeep white matter invasionGBM molecular subtypesNew informaticsQualitative imaging biomarkersWhole-exome sequencingQualitative imaging featuresGBM resectionRadiographic evidenceWorse prognosisPACSMolecular subtypesPial invasionImaging biomarkersCDKN2A mutationsAllele statusNoninvasive identificationMagnetic resonance imagesP13.02.A APPLICATION OF NOVEL PACS-BASED INFORMATICS PLATFORM TO IDENTIFY IMAGING BASED PREDICTORS OF CDKN2A ALLELIC STATUS IN GLIOBLASTOMAS
Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay Z, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo I, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. P13.02.A APPLICATION OF NOVEL PACS-BASED INFORMATICS PLATFORM TO IDENTIFY IMAGING BASED PREDICTORS OF CDKN2A ALLELIC STATUS IN GLIOBLASTOMAS. Neuro-Oncology 2023, 25: ii100-ii101. PMCID: PMC10489329, DOI: 10.1093/neuonc/noad137.336.Peer-Reviewed Original ResearchImaging featuresPial invasionQualitative imaging biomarkersQualitative imaging featuresWorse prognosisImaging biomarkersCDKN2A mutationsMethods Sixty-nine patientsCDKN2A alterationsHomozygous deletionHeterozygous lossSixty-nine patientsDeep white matterDeep white matter invasionGBM molecular subtypesWhole-exome sequencingNine patientsGBM resectionRadiographic evidenceMolecular subtypesBACKGROUND GliomasWhite matterAllele statusNoninvasive identificationGliomas
2021
NIMG-64. TYPE OF BONY INVOLVEMENT PREDICTS GENOMIC SUBGROUP IN SPHENOID WING MENINGIOMAS
Jin L, Youngblood M, Gupte T, Vetsa S, Nadar A, Barak T, Yalcin K, Aguilera S, Mishra-Gorur K, Blondin N, Omay S, Pointdujour-Lim R, Judson B, Alperovich M, Aboian M, McGuone D, Gunel M, Erson-Omay Z, Fulbright R, Moliterno J. NIMG-64. TYPE OF BONY INVOLVEMENT PREDICTS GENOMIC SUBGROUP IN SPHENOID WING MENINGIOMAS. Neuro-Oncology 2021, 23: vi144-vi144. PMCID: PMC8598770, DOI: 10.1093/neuonc/noab196.562.Peer-Reviewed Original ResearchSphenoid wing meningiomaSpheno-orbital meningiomasBony involvementTRAF7 mutationsTumor invasionGenomic subgroupsPre-operative clinical featuresYale-New Haven HospitalAdditional clinical variablesSubset of tumorsPre-operative predictionLogistic regression modelsWhole-exome sequencingClinical featuresClinical variablesGrade IIPredictive logistic regression modelRecurrence patternsMolecular subtypesClinical implicationsExome sequencingHyperostosisMeningiomasTumorsGenomic drivers