Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study.
Iseke S, Zeevi T, Kucukkaya AS, Raju R, Gross M, Haider SP, Petukhova-Greenstein A, Kuhn TN, Lin M, Nowak M, Cooper K, Thomas E, Weber MA, Madoff DC, Staib L, Batra R, Chapiro J. Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study. American Journal Of Roentgenology 2022, 220: 245-255. PMID: 35975886, PMCID: PMC10015590, DOI: 10.2214/ajr.22.28077.Peer-Reviewed Original ResearchConceptsEarly-stage hepatocellular carcinomaLiver transplantHepatocellular carcinomaImaging featuresPosttreatment recurrenceOrgan allocationMean AUCLiver transplant eligibilityPretreatment clinical characteristicsPretreatment MRI examinationsKaplan-Meier analysisKaplan-Meier curvesClinical characteristicsImaging surveillanceTherapy allocationTransplant eligibilityUnderwent treatmentClinical parametersRetrospective studyUnpredictable complicationMRI dataConcept studyPoor survivalClinical impactPretreatment MRILearning Correspondences of Cardiac Motion from Images Using Biomechanics-Informed Modeling
Zhang X, You C, Ahn S, Zhuang J, Staib L, Duncan J. Learning Correspondences of Cardiac Motion from Images Using Biomechanics-Informed Modeling. Lecture Notes In Computer Science 2022, 13593: 13-25. DOI: 10.1007/978-3-031-23443-9_2.Peer-Reviewed Original ResearchDisplacement vector fieldQuantitative evaluation metricsCardiac anatomical structuresTraining complexityExtensive experimentsSegmentation performanceBiomechanical feasibilityEvaluation metricsAvailable datasetsCardiac motionSmoothness constraintGeometric constraintsBiomechanical propertiesDatasetMRI dataPlausible transformationsMotionConstraintsAnatomical structuresRegularizerRegularization schemeMethodIncompressibilityImagesMetrics