Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects
Tram N, Chou T, Janse S, Bobbey A, Audino A, Onofrey J, Stacy M. Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects. European Radiology 2023, 33: 6599-6607. PMID: 36988714, DOI: 10.1007/s00330-023-09587-z.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentBody CompositionChildDeep LearningDisease ProgressionFemaleHumansLymphomaMalePredictive Value of TestsProportional Hazards ModelsRetrospective StudiesTomography, X-Ray ComputedConceptsProportional hazards regression analysisHazards regression analysisLate effectsBody composition measuresAYA patientsHigh riskBody compositionCox proportional hazards regression analysisTreatment-related late effectsComposition measuresCancer treatmentSerious adverse eventsLate treatment effectsYoung adult patientsSubcutaneous adipose tissueRegression analysisCare CT imagesSingle-site studyMuscle tissueAdult patientsAdverse eventsInitial stagingPediatric patientsAdult lymphomasPrognostic value