2023
Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management
Taouli B, Ba-Ssalamah A, Chapiro J, Chhatwal J, Fowler K, Kang T, Knobloch G, Koh D, Kudo M, Lee J, Murakami T, Pinato D, Ringe K, Song B, Tabrizian P, Wang J, Yoon J, Zeng M, Zhou J, Vilgrain V. Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management. European Radiology 2023, 33: 9152-9166. PMID: 37500964, PMCID: PMC10730664, DOI: 10.1007/s00330-023-09928-y.Peer-Reviewed Original ResearchConceptsGadoxetic acid-enhanced magnetic resonance imagingLiver magnetic resonance imagingMagnetic resonance imagingHCC managementResonance imagingOutcome of HCCScreening/surveillanceLiver cancer screeningPrediction of prognosisWestern guidelinesCancer screeningLiver surgeonsLiver surgeryConsensus statementHCC screeningTreatment responseConsensus reportLiver cancerInterventional radiologistsClinical relevanceClinical practiceGadoxetic acidEmerging DataHCCDiagnosisConsensus report from the 10th global forum for liver magnetic resonance imaging: multidisciplinary team discussion
Taouli B, Ba-Ssalamah A, Chapiro J, Chhatwal J, Fowler K, Kang T, Knobloch G, Koh D, Kudo M, Lee J, Murakami T, Pinato D, Ringe K, Song B, Tabrizian P, Wang J, Yoon J, Zeng M, Zhou J, Vilgrain V. Consensus report from the 10th global forum for liver magnetic resonance imaging: multidisciplinary team discussion. European Radiology 2023, 33: 9167-9181. PMID: 37439935, PMCID: PMC10667403, DOI: 10.1007/s00330-023-09919-z.Peer-Reviewed Original ResearchConceptsLiver magnetic resonance imagingLiver surgeonsMagnetic resonance imagingHCC managementInterventional radiologistsResonance imagingLate-stage HCCMultidisciplinary team discussionMultidisciplinary tumor boardMultidisciplinary team approachHepatocellular carcinoma managementLiver cancer patientsCancer patientsTumor boardConsensus reportClinical relevanceTeam approachMultidisciplinary expertsOncologistsSurgeonsTeam discussionRadiologistsImagingMultidisciplinary facultyManagementPredicting 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 ResearchExplainable AI for Prostate MRI: Don't Trust, Verify.
Chapiro J. Explainable AI for Prostate MRI: Don't Trust, Verify. Radiology 2023, 307: e230574. PMID: 37039689, PMCID: PMC10323286, DOI: 10.1148/radiol.230574.Peer-Reviewed Original Research
2022
Defining textbook outcome for selective internal radiation therapy of hepatocellular carcinoma: an international expert study
Gregory J, Tselikas L, Allimant C, de Baere T, Bargellini I, Bell J, Bilbao JI, Bouvier A, Chapiro J, Chiesa C, Decaens T, Denys A, Duran R, Edeline J, Garin E, Ghelfi J, Helmberger T, Irani F, Lam M, Lewandowski R, Liu D, Loffroy R, Madoff DC, Mastier C, Salem R, Sangro B, Sze D, Vilgrain V, Vouche M, Guiu B, Ronot M. Defining textbook outcome for selective internal radiation therapy of hepatocellular carcinoma: an international expert study. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 50: 921-928. PMID: 36282299, DOI: 10.1007/s00259-022-06002-5.Peer-Reviewed Original ResearchConceptsSelective internal radiation therapyTextbook outcomeInternal radiation therapyHepatocellular carcinomaRadiation therapyPatient-important outcomesLarge observational studiesNuclear medicine physiciansEntire intervention processRandomized trialsRoutine careResultsA totalObservational studyMultistep interventionMedicine physiciansInterventional radiologistsSenior authorClinical roundsCarcinomaTherapyInterventionOutcomesMachine 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 MRIAnalysis of Tumor Burden as a Biomarker for Patient Survival with Neuroendocrine Tumor Liver Metastases Undergoing Intra-Arterial Therapies: A Single-Center Retrospective Analysis
Miszczuk M, Chapiro J, Do Minh D, van Breugel JMM, Smolka S, Rexha I, Tegel B, Lin M, Savic LJ, Hong K, Georgiades C, Nezami N. Analysis of Tumor Burden as a Biomarker for Patient Survival with Neuroendocrine Tumor Liver Metastases Undergoing Intra-Arterial Therapies: A Single-Center Retrospective Analysis. CardioVascular And Interventional Radiology 2022, 45: 1494-1502. PMID: 35941241, PMCID: PMC9587516, DOI: 10.1007/s00270-022-03209-9.Peer-Reviewed Original ResearchConceptsNeuroendocrine tumor liver metastasesMedian overall survivalIntra-arterial therapyLow tumor burdenTumor burdenOverall survivalLiver metastasesPrognostic factorsTumor diameterTB groupLonger median overall survivalRetrospective single-center analysisSingle-center retrospective analysisHigh TB groupLow TB groupRespective hazard ratiosHigh tumor burdenSingle-center analysisIndependent prognostic factorStrong prognostic factorDrug-eluting beadsLargest liver lesionPrediction of survivalHazard ratioPatient survivalProceedings 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 imagingResponse assessment methods for patients with hepatic metastasis from rare tumor primaries undergoing transarterial chemoembolization
Adam LC, Savic LJ, Chapiro J, Letzen B, Lin M, Georgiades C, Hong KK, Nezami N. Response assessment methods for patients with hepatic metastasis from rare tumor primaries undergoing transarterial chemoembolization. Clinical Imaging 2022, 89: 112-119. PMID: 35777239, PMCID: PMC9470015, DOI: 10.1016/j.clinimag.2022.06.013.Peer-Reviewed Original ResearchConceptsConventional transarterial chemoembolizationLipiodol depositionHepatic metastasesResponse assessment methodsPartial responseTransarterial chemoembolizationResponse assessmentTumor primaryStratification of responseRare primary tumorResponse Evaluation CriteriaRetrospective bicentric studyAssessment of responseQuantitative European AssociationLiver metastasesMajor complicationsBicentric studyPrimary tumorRare tumorEarly surrogateTreatment responseCT scanPatientsRECISTSolid tumorsMR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features
Petukhova-Greenstein A, Zeevi T, Yang J, Chai N, DiDomenico P, Deng Y, Ciarleglio M, Haider SP, Onyiuke I, Malpani R, Lin M, Kucukkaya AS, Gottwald LA, Gebauer B, Revzin M, Onofrey J, Staib L, Gunabushanam G, Taddei T, Chapiro J. MR Imaging Biomarkers for the Prediction of Outcome after Radiofrequency Ablation of Hepatocellular Carcinoma: Qualitative and Quantitative Assessments of the Liver Imaging Reporting and Data System and Radiomic Features. Journal Of Vascular And Interventional Radiology 2022, 33: 814-824.e3. PMID: 35460887, PMCID: PMC9335926, DOI: 10.1016/j.jvir.2022.04.006.Peer-Reviewed Original ResearchConceptsProgression-free survivalPoor progression-free survivalLiver Imaging ReportingHepatocellular carcinomaMR imaging biomarkersRadiomics signatureRadiofrequency ablationRadiomic featuresImaging biomarkersImaging ReportingFirst follow-up imagingMedian progression-free survivalRF ablationEarly-stage hepatocellular carcinomaPretreatment magnetic resonanceFirst-line treatmentMultifocal hepatocellular carcinomaSelection operator Cox regression modelTherapy-naïve patientsEarly-stage diseaseKaplan-Meier analysisCox regression modelLog-rank testFollow-up imagingPrediction of outcomeOptimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI.
Borde T, Nezami N, Laage Gaupp F, Savic LJ, Taddei T, Jaffe A, Strazzabosco M, Lin M, Duran R, Georgiades C, Hong K, Chapiro J. Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI. Radiology 2022, 304: 228-237. PMID: 35412368, PMCID: PMC9270683, DOI: 10.1148/radiol.212426.Peer-Reviewed Original ResearchConceptsMedian overall survivalAdvanced-stage hepatocellular carcinomaTransarterial chemoembolizationHepatocellular carcinomaBCLC BBCLC COverall survivalTumor burdenBarcelona Clinic Liver Cancer (BCLC) staging systemLiver Cancer staging systemCancer (AJCC) staging systemConventional transarterial chemoembolizationDrug-eluting beadsAllocation of patientsContrast-enhanced MRIBackground PatientsSurvival benefitRetrospective studyStaging systemC tumorsTumor volumePatientsHeterogeneous patientsMonthsChemoembolizationTranslating artificial intelligence from code to bedside: The road towards AI-driven predictive biomarkers for immunotherapy of hepatocellular carcinoma
Chapiro J. Translating artificial intelligence from code to bedside: The road towards AI-driven predictive biomarkers for immunotherapy of hepatocellular carcinoma. Journal Of Hepatology 2022, 77: 6-8. PMID: 35417743, DOI: 10.1016/j.jhep.2022.03.035.Peer-Reviewed Original Research
2021
Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach
Malpani R, Petty CW, Yang J, Bhatt N, Zeevi T, Chockalingam V, Raju R, Petukhova-Greenstein A, Santana JG, Schlachter TR, Madoff DC, Chapiro J, Duncan J, Lin M. Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach. Journal Of Vascular And Interventional Radiology 2021, 33: 324-332.e2. PMID: 34923098, PMCID: PMC8972393, DOI: 10.1016/j.jvir.2021.12.017.Peer-Reviewed Original ResearchImproved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging
Gross M, Spektor M, Jaffe A, Kucukkaya AS, Iseke S, Haider SP, Strazzabosco M, Chapiro J, Onofrey JA. Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging. PLOS ONE 2021, 16: e0260630. PMID: 34852007, PMCID: PMC8635384, DOI: 10.1371/journal.pone.0260630.Peer-Reviewed Original ResearchMachine Learning–Based Surveillance Strategy after Complete Ablation of Initially Recurrent Hepatocellular Carcinoma: Worth the Risk?
Nam D, Chapiro J. Machine Learning–Based Surveillance Strategy after Complete Ablation of Initially Recurrent Hepatocellular Carcinoma: Worth the Risk? Journal Of Vascular And Interventional Radiology 2021, 32: 1558-1559. PMID: 34717834, DOI: 10.1016/j.jvir.2021.08.014.Peer-Reviewed Original ResearchLipiodol Deposition and Washout in Primary and Metastatic Liver Tumors After Chemoembolization
Nezami N, VAN Breugel JMM, Konstantinidis M, Chapiro J, Savic LJ, Miszczuk MA, Rexha I, Lin M, Hong K, Georgiades C. Lipiodol Deposition and Washout in Primary and Metastatic Liver Tumors After Chemoembolization. In Vivo 2021, 35: 3261-3270. PMID: 34697157, PMCID: PMC8627740, DOI: 10.21873/invivo.12621.Peer-Reviewed Original ResearchConceptsConventional trans-arterial chemoembolizationTrans-arterial chemoembolizationNeuroendocrine tumorsColorectal carcinomaIntrahepatic cholangiocarcinomaLipiodol depositionWashout rateContrast-enhanced magnetic resonanceMetastatic liver tumorsColorectal carcinoma tumorsLiver metastasesHepatic metastasesTumor responseTarget lesionsRetrospective analysisLiver tumorsSmall tumorsChemoembolizationCarcinoma tumorsTumorsExponential washoutCarcinomaTomography imagingWashoutCholangiocarcinomaIdentifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy
Ghani MA, Fereydooni A, Chen E, Letzen B, Laage-Gaupp F, Nezami N, Deng Y, Gan G, Thakur V, Lin M, Papademetris X, Schernthaner RE, Huber S, Chapiro J, Hong K, Georgiades C. Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy. European Radiology 2021, 31: 8858-8867. PMID: 34061209, PMCID: PMC8848338, DOI: 10.1007/s00330-021-08058-7.Peer-Reviewed Original ResearchConceptsColorectal cancer liver metastasesCancer liver metastasesTotal tumor volumeIntra-arterial therapyTotal liver volumeLiver metastasesTumor volumeTumor burdenTumor diameterPatient survivalBaseline MRILiver volumeMultivariable Cox proportional hazards modelsKaplan-Meier survival curvesWhole liverCox proportional hazards modelKaplan-Meier methodPrognostic staging systemSurvival of patientsColorectal cancer metastasisMethodsThis retrospective studyPre-treatment MRIProportional hazards modelAppropriate cutoff valueHR 1.7Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial
Letzen BS, Malpani R, Miszczuk M, de Ruiter QMB, Petty CW, Rexha I, Nezami N, Laage-Gaupp F, Lin M, Schlachter TR, Chapiro J. Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial. Clinical Imaging 2021, 78: 194-200. PMID: 34022765, PMCID: PMC8364875, DOI: 10.1016/j.clinimag.2021.05.007.Peer-Reviewed Original ResearchConceptsConventional trans-arterial chemoembolizationMedian overall survivalProspective clinical trialsLipiodol depositionTumor responsePredictive biomarkersClinical trialsModified Response Evaluation CriteriaPost-TACE CTResponse Evaluation CriteriaMetastatic liver cancerKaplan-Meier analysisTrans-arterial chemoembolizationTumor response criteriaLiver tumor responsePrediction of survivalSelective drug targetingArterial embolizationLiver metastasesOverall survivalBland-Altman plotsTransarterial chemoembolizationPortal veinTumor respondersHepatocellular carcinomaRole of 3D quantitative tumor analysis for predicting overall survival after conventional chemoembolization of intrahepatic cholangiocarcinoma
Rexha I, Laage-Gaupp F, Chapiro J, Miszczuk MA, van Breugel JMM, Lin M, Konstantinidis M, Duran R, Gebauer B, Georgiades C, Hong K, Nezami N. Role of 3D quantitative tumor analysis for predicting overall survival after conventional chemoembolization of intrahepatic cholangiocarcinoma. Scientific Reports 2021, 11: 9337. PMID: 33927226, PMCID: PMC8085245, DOI: 10.1038/s41598-021-88426-x.Peer-Reviewed Original ResearchConceptsTotal tumor volumeConventional transarterial chemoembolizationTumor diameterIntrahepatic cholangiocarcinomaOverall survivalTumor areaICC patientsTumor volumeHigh tumor burden groupTumor analysisOS of patientsHazard ratioTransarterial chemoembolizationTumor burdenBurden groupConventional chemoembolizationHTB groupRetrospective analysisPatientsSurvival curvesMultivariate analysisChemoembolizationCholangiocarcinomaETVBaseline imagesElastin-specific MRI of extracellular matrix-remodelling following hepatic radiofrequency-ablation in a VX2 liver tumor model
Collettini F, Reimann C, Brangsch J, Chapiro J, Savic LJ, Onthank DC, Robinson SP, Karst U, Buchholz R, Keller S, Hamm B, Goldberg SN, Makowski MR. Elastin-specific MRI of extracellular matrix-remodelling following hepatic radiofrequency-ablation in a VX2 liver tumor model. Scientific Reports 2021, 11: 6814. PMID: 33767303, PMCID: PMC7994448, DOI: 10.1038/s41598-021-86417-6.Peer-Reviewed Original Research