2022
Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside
Chapiro J, Allen B, Abajian A, Wood B, Kothary N, Daye D, Bai H, Sedrakyan A, Diamond M, Simonyan V, McLennan G, Abi-Jaoudeh N, Pua B. Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside. Journal Of Vascular And Interventional Radiology 2022, 33: 1113-1120. PMID: 35871021, DOI: 10.1016/j.jvir.2022.06.003.Peer-Reviewed Original ResearchConceptsArtificial intelligenceGrowth of AIApplicability of AIClinical use casesDevelopment of AIIR research communityUse casesCutting-edge technologiesResearch communityAIInterventional radiologyIntelligenceHealth informationConsensus panelTechnologyPatient careCurrent needsApplicationsConsensus statementResearch collaborationResearch prioritiesImage guidance modalitySubstantial improvementClinical expertiseDiagnostic imaging
2019
Interventional oncology: aiming globally to be the 4 th pillar of cancer care
Madoff DC, Chapiro J, Pua U. Interventional oncology: aiming globally to be the 4 th pillar of cancer care. Chinese Clinical Oncology 2019, 8: 56. PMID: 31968980, DOI: 10.21037/cco.2019.12.12.Peer-Reviewed Original ResearchConceptsIO therapyInterventional oncologyClinical OncologyNational Comprehensive Cancer NetworkComprehensive Cancer NetworkDisease treatment guidelinesOncologic patient managementOncologic communityPalliative settingTreatment guidelinesOncologic careInterventional oncologistsMedical oncologyCancer careClinical trialsTreatment strategiesPatient managementPatient allocationCancer NetworkInterventional radiologyOncologyTherapyEuropean SocietyRadiation oncologyAmerican SocietyImmunotherapy and the Interventional Oncologist: Challenges and Opportunities—A Society of Interventional Oncology White Paper
Erinjeri JP, Fine GC, Adema GJ, Ahmed M, Chapiro J, den Brok M, Duran R, Hunt SJ, Johnson DT, Ricke J, Sze DY, Toskich BB, Wood BJ, Woodrum D, Goldberg SN. Immunotherapy and the Interventional Oncologist: Challenges and Opportunities—A Society of Interventional Oncology White Paper. Radiology 2019, 292: 25-34. PMID: 31012818, PMCID: PMC6604797, DOI: 10.1148/radiol.2019182326.Peer-Reviewed Original ResearchConceptsInterventional oncologyCancer management planCancer-related problemsInterventional oncologistsTreatment of cancerCancer patientsImmuno-oncologyInvasive proceduresInterventional radiologyRadiation oncologyOncologyImage guidanceCancerCancer researchSubspecialty fieldsPivotal roleImmunotherapyPatientsOncologistsDiagnosisCare
2018
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma.
Abajian A, Murali N, Savic LJ, Laage-Gaupp FM, Nezami N, Duncan JS, Schlachter T, Lin M, Geschwind JF, Chapiro J. Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma. Journal Of Visualized Experiments 2018 PMID: 30371657, PMCID: PMC6235502, DOI: 10.3791/58382.Peer-Reviewed Original ResearchConceptsIntra-arterial therapyN patientsHepatocellular carcinomaTrans-arterial therapiesIntra-arterial treatmentCohort of patientsStandard of careLikelihood of responseClinical research questionsSurgical resectionNew patientsTreatment responseUnivariate associationsPatientsTraining patientsInterventional radiologyTherapyCarcinomaTreatmentImage-guided therapyOutcomesFinal modelImaging dataResectionResponse