Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Kucukkaya A, Zeevi T, Chai N, Raju R, Haider S, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Scientific Reports 2023, 13: 7579. PMID: 37165035, PMCID: PMC10172370, DOI: 10.1038/s41598-023-34439-7.Peer-Reviewed Original ResearchMeSH KeywordsCarcinoma, HepatocellularHumansLiver NeoplasmsMachine LearningMagnetic Resonance ImagingNeoplasm Recurrence, LocalRetrospective StudiesDeep 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 ResearchConceptsProportional 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