2024
RADT-12. DERIVING IMAGING BIOMARKERS FOR PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA USING DEEP LEARNING
Zhu J, Hager T, Chadha S, Sritharan D, Weiss D, Hossain S, Osenberg K, Moore N, Aneja S. RADT-12. DERIVING IMAGING BIOMARKERS FOR PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA USING DEEP LEARNING. Neuro-Oncology 2024, 26: viii74-viii74. PMCID: PMC11553274, DOI: 10.1093/neuonc/noae165.0296.Peer-Reviewed Original ResearchPrimary central nervous system lymphomaWhole-brain radiotherapyTreated with chemotherapyOverall survivalHigh-risk groupPatient phenotypesCentral nervous system lymphomaPCNSL treatmentRisk of neurocognitive side effectsImaging biomarkersC-statisticOne-year OSTwo-year OSNervous system lymphomaAssociated with improved outcomesLog-rank testNeurocognitive side effectsTime-dependent AUCBrain radiotherapySystem lymphomaTumor volumeTumor sizeRisk stratificationAnalyses assessed differencesSub-analysis
2022
Deep learning algorithm to predict pathologic complete response to neoadjuvant chemotherapy for breast cancer prior to treatment.
Choi R, Joel M, Hui M, Aneja S. Deep learning algorithm to predict pathologic complete response to neoadjuvant chemotherapy for breast cancer prior to treatment. Journal Of Clinical Oncology 2022, 40: 600-600. DOI: 10.1200/jco.2022.40.16_suppl.600.Peer-Reviewed Original ResearchPathologic complete responseNeoadjuvant chemotherapyBreast cancerComplete responseBreast MRIImproved disease-free survivalDisease-free survivalStage breast cancerPre-treatment predictionSubsets of ageNAC initiationOverall survivalPCR rateTreatment initiationUnnecessary toxicityTumor sizeSingle institutionDisease groupPatient levelPrognostic dataChemotherapyPatientsDiscordant predictionsCancerTotal test set