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Wang Q, Yu G, Qiu J, Lu W. Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review. J Magn Reson Imaging 2024; 60:417-440. [PMID: 37908165 DOI: 10.1002/jmri.29086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Intravoxel incoherent motion (IVIM) modeling is a widely used double-exponential model for describing diffusion-weighted imaging (DWI) signal, with a slow component related to pure molecular diffusion and a fast component associated with microcirculatory perfusion, which compensates for the limitations of traditional DWI. IVIM is a noninvasive technique for obtaining liver pathological information and characterizing liver lesions, and has potential applications in the initial diagnosis and treatment monitoring of liver diseases. Recent studies have demonstrated that IVIM-derived parameters are useful for evaluating liver lesions, including nonalcoholic fatty liver disease (NAFLD), liver fibrosis and liver tumors. However, the results are not stable. Therefore, it is necessary to summarize the current applications of IVIM in liver disease research, identify existing shortcomings, and point out the future development direction. In this review, we searched for studies related to hepatic IVIM-DWI applications over the past two decades in the PubMed database. We first introduce the fundamental principles and influential factors of IVIM, and then discuss its application in NAFLD, liver fibrosis, and focal hepatic lesions. It has been found that IVIM is still unstable in ensuring the robustness and reproducibility of measurements in the assessment of liver fibrosis grade and liver tumors differentiation, due to inconsistent and substantial overlap in the range of IVIM-derived parameters for different fibrotic stages. In the end, the future direction of IVIM-DWI in the assessment of liver diseases is discussed, emphasizing the need for further research on the stability of IVIM-derived parameters, particularly perfusion-related parameters, in order to promote the clinical practice of IVIM-DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Qi Wang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, China
| | - Guanghui Yu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Jianfeng Qiu
- Department of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Weizhao Lu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, China
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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3
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Stocker D, King MJ, Homsi ME, Gnerre J, Marinelli B, Wurnig M, Schwartz M, Kim E, Taouli B. Early post-treatment MRI predicts long-term hepatocellular carcinoma response to radiation segmentectomy. Eur Radiol 2024; 34:475-484. [PMID: 37540318 PMCID: PMC10791774 DOI: 10.1007/s00330-023-10045-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 05/29/2023] [Accepted: 06/20/2023] [Indexed: 08/05/2023]
Abstract
OBJECTIVES Radiation segmentectomy using yttrium-90 plays an emerging role in the management of early-stage HCC. However, the value of early post-treatment MRI for response assessment is uncertain. We assessed the value of response criteria obtained early after radiation segmentectomy in predicting long-term response in patients with HCC. MATERIALS AND METHODS Patients with HCC who underwent contrast-enhanced MRI before, early, and 12 months after radiation segmentectomy were included in this retrospective single-center study. Three independent radiologists reviewed images at baseline and 1st follow-up after radiation segmentectomy and assessed lesion-based response according to mRECIST, LI-RADS treatment response algorithm (TRA), and image subtraction. The endpoint was response at 12 months based on consensus readout of two separate radiologists. Diagnostic accuracy for predicting complete response (CR) at 12 months based on the 1st post-treatment MRI was calculated. RESULTS Eighty patients (M/F 60/20, mean age 67.7 years) with 80 HCCs were assessed (median size baseline, 1.8 cm [IQR, 1.4-2.9 cm]). At 12 months, 74 patients were classified as CR (92.5%), 5 as partial response (6.3%), and 1 as progressive disease (1.2%). Diagnostic accuracy for predicting CR was fair to good for all readers with excellent positive predictive value (PPV): mRECIST (range between 3 readers, accuracy: 0.763-0.825, PPV: 0.966-1), LI-RADS TRA (accuracy: 0.700-0.825, PPV: 0.983-1), and subtraction (accuracy: 0.775-0.825, PPV: 0.967-1), with no difference in accuracy between criteria (p range 0.053 to > 0.9). CONCLUSION mRECIST, LI-RADS TRA, and subtraction obtained on early post-treatment MRI show similar performance for predicting long-term response in patients with HCC treated with radiation segmentectomy. CLINICAL RELEVANCE STATEMENT Response assessment extracted from early post-treatment MRI after radiation segmentectomy predicts complete response in patients with HCC with high PPV (≥ 0.96). KEY POINTS • Early post-treatment response assessment on MRI predicts response in patients with HCC treated with radiation segmentectomy with fair to good accuracy and excellent positive predictive value. • There was no difference in diagnostic accuracy between mRECIST, LI-RADS, and subtraction for predicting HCC response to radiation segmentectomy.
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Affiliation(s)
- Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | - Michael J King
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria El Homsi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jeffrey Gnerre
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett Marinelli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Interventional Radiology, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Moritz Wurnig
- Institute of Radiology, Spital Lachen AG, Lachen, Switzerland
| | - Myron Schwartz
- Recanati Miller Transplantation Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edward Kim
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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4
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Hsieh C, Laguna A, Ikeda I, Maxwell AWP, Chapiro J, Nadolski G, Jiao Z, Bai HX. Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma. Radiology 2023; 309:e222891. [PMID: 37934098 DOI: 10.1148/radiol.222891] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.
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Affiliation(s)
- Celina Hsieh
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Amanda Laguna
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Ian Ikeda
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Aaron W P Maxwell
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Julius Chapiro
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Gregory Nadolski
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Zhicheng Jiao
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Harrison X Bai
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
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Marinelli B, Chen M, Stocker D, Charles D, Radell J, Lee JY, Fauveau V, Bello-Martinez R, Kim E, Taouli B. Early Prediction of Response of Hepatocellular Carcinoma to Yttrium-90 Radiation Segmentectomy Using a Machine Learning MR Imaging Radiomic Approach. J Vasc Interv Radiol 2023; 34:1794-1801.e2. [PMID: 37364730 DOI: 10.1016/j.jvir.2023.06.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/05/2023] [Accepted: 06/16/2023] [Indexed: 06/28/2023] Open
Abstract
PURPOSE To assess the accuracy of a machine learning (ML) approach based on magnetic resonance (MR) imaging radiomic quantification obtained before treatment and early after treatment for prediction of early hepatocellular carcinoma (HCC) response to yttrium-90 transarterial radioembolization (TARE). MATERIALS AND METHODS In this retrospective single-center study of 76 patients with HCC, baseline and early (1-2 months) post-TARE MR images were collected. Semiautomated tumor segmentation facilitated extraction of shape, first-order histogram, and custom signal intensity-based radiomic features, which were then trained (n = 46) using a ML XGBoost model and validated on a separate cohort (n = 30) not used in training to predict treatment response assessed at 4-6 months (based on modified Response and Evaluation Criteria in Solid Tumors criteria). Performance of this ML radiomic model was compared with those of models comprising clinical parameters and standard imaging characteristics using area under the receiver operating curve (AUROC) analysis for prediction of complete response (CR). RESULTS Seventy-six tumors with a mean (±SD) diameter of 2.6 cm ± 1.6 were included. Sixty, 12, 1, and 3 patients were classified as having CR, partial response, stable disease, and progressive disease, respectively, at 4-6 months posttreatment on the basis of MR images. In the validation cohort, the radiomic model showed good performance (AUROC, 0.89) for prediction of CR, compared with models comprising clinical and standard imaging criteria (AUROC, 0.58 and 0.59, respectively). Baseline imaging features appeared to be more heavily weighted in the radiomic model. CONCLUSIONS The use of ML modeling of radiomic data combining baseline and early follow-up MR imaging could predict HCC response to TARE. These models need to be investigated further in an independent cohort.
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Affiliation(s)
- Brett Marinelli
- Biomedical Engineering and Imaging Institute; Interventional Radiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Mark Chen
- Department of Diagnostic, Interventional and Molecular Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Daniel Stocker
- Institute of Interventional and Diagnostic Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Dudley Charles
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia
| | - Jake Radell
- Department of Diagnostic, Interventional and Molecular Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jun Yoep Lee
- Department of Diagnostic, Interventional and Molecular Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | | | - Edward Kim
- Department of Diagnostic, Interventional and Molecular Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Bachir Taouli
- Biomedical Engineering and Imaging Institute; Department of Diagnostic, Interventional and Molecular Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
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Cao Y, Cuneo KC, Evans J, Ten Haken RK, Chang DT, Lawrence TS. Is Apparent Diffusion Coefficient Established as an Imaging Biomarker for Stereotactic Body Radiation Therapy Assessment in Hepatocellular Carcinoma? Cancer J 2023; 29:238-242. [PMID: 37471615 PMCID: PMC10372684 DOI: 10.1097/ppo.0000000000000668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
ABSTRACT In this article, as part of this special issue on biomarkers of early response, we review currently available reports regarding magnetic resonance imaging apparent diffusion coefficient (ADC) changes in hepatocellular carcinoma (HCC) in response to stereotactic body radiation therapy. We compare diffusion image acquisition, ADC analysis, methods for HCC response assessment, and statistical methods for prediction of local tumor progression by ADC metrics. We discuss the pros and cons of these studies. Following detailed analyses of existing investigations, we cannot conclude that ADC is established as an imaging biomarker for stereotactic body radiation therapy assessment in HCC.
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Affiliation(s)
- Yue Cao
- From the Department of Radiation Oncology, University of Michigan, Ann Arbor, MI
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Wang L, Yang JD, Yoo CC, Lai KKY, Braun J, McGovern DPB, Xie Y, Pandol SJ, Lu SC, Li D. Magnetic resonance imaging for characterization of hepatocellular carcinoma metabolism. Front Physiol 2022; 13:1056511. [PMID: 36589457 PMCID: PMC9800006 DOI: 10.3389/fphys.2022.1056511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
With a better understanding of the pathophysiological and metabolic changes in hepatocellular carcinoma (HCC), multiparametric and novel functional magnetic resonance (MR) and positron emission tomography (PET) techniques have received wide interest and are increasingly being applied in preclinical and clinical research. These techniques not only allow for non-invasive detection of structural, functional, and metabolic changes in malignant tumor cells but also characterize the tumor microenvironment (TME) and the interactions of malignant tumor cells with the TME, which has hypoxia and low pH, resulting from the Warburg effect and accumulation of metabolites produced by tumor cells and other cellular components. The heterogeneity and complexity of the TME require a combination of images with various parameters and modalities to characterize tumors and guide therapy. This review focuses on the value of multiparametric magnetic resonance imaging and PET/MR in evaluating the structural and functional changes of HCC and in detecting metabolites formed owing to HCC and the TME.
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Affiliation(s)
- Lixia Wang
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Ju Dong Yang
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States,Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA, United States,Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Charles C. Yoo
- Office of the Medical Director 1st MRI, Los Angeles, CA, United States
| | - Keane K. Y. Lai
- Department of Molecular Medicine, Beckman Research Institute of City of Hope and City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | - Jonathan Braun
- F. Widjaja Inflammatory Bowel Disease Institute, Division of Digestive and Liver Diseases, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Dermot P. B. McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Division of Digestive and Liver Diseases, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Yibin Xie
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Stephen J. Pandol
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Shelly C. Lu
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States,Department of Bioengineering, University of California, Los Angeles, CA, United States,*Correspondence: Debiao Li,
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Zhou Y, Zheng J, Yang C, Peng J, Liu N, Yang L, Zhang XM. Application of intravoxel incoherent motion diffusion-weighted imaging in hepatocellular carcinoma. World J Gastroenterol 2022; 28:3334-3345. [PMID: 36158259 PMCID: PMC9346463 DOI: 10.3748/wjg.v28.i27.3334] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 04/26/2022] [Accepted: 06/26/2022] [Indexed: 02/06/2023] Open
Abstract
The morbidity and mortality of hepatocellular carcinoma (HCC) rank 6th and 4th, respectively, among malignant tumors worldwide. Traditional diffusion-weighted imaging (DWI) uses the apparent diffusion coefficient (ADC) obtained by applying the monoexponential model to reflect water molecule diffusion in active tissue; however, the value of ADC is affected by microcirculation perfusion. Using a biexponential model, intravoxel incoherent motion (IVIM)-DWI quantitatively measures information related to pure water molecule diffusion and microcirculation perfusion, thus compensating for the shortcomings of DWI. The number of studies examining the application of IVIM-DWI in patients with HCC has gradually increased over the last few years, and many results show that IVIM-DWI has vital value for HCC differentiation, pathological grading, and predicting and evaluating the treatment response. The present study principally reviews the principle of IVIM-DWI and its research progress in HCC differentiation, pathological grading, predicting and evaluating the treatment response, predicting postoperative recurrence and predicting gene expression prediction.
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Affiliation(s)
- Yi Zhou
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, People's Hospital of Deyang City, Deyang 618000, Sichuan Province, China
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Cui Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Juan Peng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
- Department of Radiology, Sichuan Provincial People's Hospital Jinniu Hospital, Chengdu Jinniu District People's Hospital, Chengdu 610007, Sichuan Province, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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9
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Andersson M, Jalnefjord O, Montelius M, Rizell M, Sternby Eilard M, Ljungberg M. Evaluation of response in patients with hepatocellular carcinoma treated with intratumoral dendritic cell vaccination using intravoxel incoherent motion (IVIM) MRI and histogram analysis. Acta Radiol 2021; 64:32-41. [PMID: 34904868 DOI: 10.1177/02841851211065935] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Immunotherapy of hepatocellular carcinoma (HCC) is an emerging method with promising results. Immunotherapy can have an antitumor effect without affecting tumor size, calling for functional imaging methods for response evaluation. PURPOSE To evaluate the response to intratumoral injections with the immune primer ilixadencel in HCCs with diffusion-weighted magnetic resonance imaging (DW-MRI) using intravoxel incoherent motion (IVIM) and histogram analysis. MATERIAL AND METHODS A total of 17 patients with advanced HCC were treated with intratumoral injections with ilixadencel on three occasions 2-5 weeks apart. The patients were examined with IVIM before each injection as well as approximately three months after the first injection. RESULTS The 10th percentile of perfusion-related parameter D* decreased significantly after the first and second intratumoral injections of ilixadencel compared to baseline (P < 0.05). There was a non-significant trend of lower median region of interest f (perfusion fraction) before injection 2 compared to baseline (P = 0.07). There were significant correlations between the 10th percentile and median of D at baseline and change in tumor size after three months (r = 0.79, P < 0.01 and r = 0.72, P < 0.05, respectively). CONCLUSION DW-MRI with IVIM and histogram analysis revealed significant reductions of D* early after treatment as well as an association between D at baseline and smaller tumor growth at three months. The lower percentiles (10th and 50th) were found more important. Further research is needed to confirm our preliminary findings of reduced perfusion after ilixadencel vaccinations, suggesting a treatment effect on HCC.
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Affiliation(s)
- Mats Andersson
- Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute and Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
| | - Oscar Jalnefjord
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mikael Montelius
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Magnus Rizell
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Malin Sternby Eilard
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Maria Ljungberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
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10
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Miller FH, Lopes Vendrami C, Gabr A, Horowitz JM, Kelahan LC, Riaz A, Salem R, Lewandowski RJ. Evolution of Radioembolization in Treatment of Hepatocellular Carcinoma: A Pictorial Review. Radiographics 2021; 41:1802-1818. [PMID: 34559587 DOI: 10.1148/rg.2021210014] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Transarterial radioembolization (TARE) with yttrium 90 has increasingly been performed to treat hepatocellular carcinoma (HCC). TARE was historically used as a palliative lobar therapy for patients with advanced HCC beyond surgical options, ablation, or transarterial chemoembolization, but recent advancements have led to its application across the Barcelona Clinic Liver Cancer staging paradigm. Newer techniques, termed radiation lobectomy and radiation segmentectomy, are being performed before liver resection to facilitate hypertrophy of the future liver remnant, before liver transplant to bridge or downstage to transplant, or as a definite curative treatment. Imaging assessment of therapeutic response to TARE is challenging as the intent of TARE is to deliver local high-dose radiation to tumors through microembolic microspheres, preserving blood flow to promote radiation injury to the tumor. Because of the microembolic nature, early imaging assessment after TARE cannot rely solely on changes in size. Knowledge of the evolving methods of TARE along with the tools to assess posttreatment imaging and response is essential to optimize TARE as a therapeutic option for patients with HCC. ©RSNA, 2021.
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Affiliation(s)
- Frank H Miller
- From the Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611
| | - Camila Lopes Vendrami
- From the Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611
| | - Ahmed Gabr
- From the Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611
| | - Jeanne M Horowitz
- From the Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611
| | - Linda C Kelahan
- From the Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611
| | - Ahsun Riaz
- From the Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611
| | - Riad Salem
- From the Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611
| | - Robert J Lewandowski
- From the Department of Radiology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 676 N St. Clair St, Ste 800, Chicago, IL 60611
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11
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Treatment response assessment following transarterial radioembolization for hepatocellular carcinoma. Abdom Radiol (NY) 2021; 46:3596-3614. [PMID: 33909092 DOI: 10.1007/s00261-021-03095-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 04/01/2021] [Accepted: 04/10/2021] [Indexed: 12/17/2022]
Abstract
Transarterial radioembolization with yttrium-90 microspheres is an established therapy for hepatocellular carcinoma. Post-procedural imaging is important for the assessment of both treatment response and procedural complications. A variety of challenging treatment-specific imaging phenomena complicate imaging assessment, such as changes in tumoral size, tumoral and peritumoral enhancement, and extrahepatic complications. A review of the procedural steps, emerging variations, and timelines for post-treatment tumoral and extra-tumoral imaging changes are presented, which may aid the reporting radiologist in the interpretation of post-procedural imaging. Furthermore, a description of post-procedural complications and their significance is provided.
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12
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The Role of Non-Gaussian Models of Diffusion Weighted MRI in Hepatocellular Carcinoma: A Systematic Review. J Clin Med 2021; 10:jcm10122641. [PMID: 34203995 PMCID: PMC8232758 DOI: 10.3390/jcm10122641] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/10/2021] [Accepted: 06/14/2021] [Indexed: 12/14/2022] Open
Abstract
The importance of Diffusion Weighted Imaging (DWI) in hepatocellular carcinoma (HCC) has been widely handled in the literature. Due to the mono-exponential model limitations, several studies recently investigated the role of non-Gaussian DWI models in HCC. However, their results are variable and inconsistent. Therefore, the aim of this systematic review is to summarize current knowledge on non-Gaussian DWI techniques in HCC. A systematic search of the literature, including PubMed, Google Scholar, MEDLINE, and ScienceDirect databases, was performed to identify original articles since 2010 that evaluated the role of non-Gaussian DWI models for HCC diagnosis, grading, response to treatment, and prognosis. Studies were grouped and summarized according to the non-Gaussian DWI models investigated. We focused on the most used non-Gaussian DWI models (Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Stretched Exponential—SE). The quality of included studies was evaluated by using QUADAS-2 and QUIPS tools. Forty-three articles were included, with IVIM and DKI being the most investigated models. Although the role of non-Gaussian DWI models in clinical settings has not fully been established, our findings showed that their parameters may potentially play a role in HCC. Further studies are required to identify a standardized DWI acquisition protocol for HCC diagnosis, grading, response to treatment, and prognosis.
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13
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Yoon JH, Lee JM, Yu MH, Hur BY, Grimm R, Sourbron S, Chandarana H, Son Y, Basak S, Lee KB, Yi NJ, Lee KW, Suh KS. Simultaneous evaluation of perfusion and morphology using GRASP MRI in hepatic fibrosis. Eur Radiol 2021; 32:34-45. [PMID: 34120229 DOI: 10.1007/s00330-021-08087-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/11/2021] [Accepted: 05/20/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To determine if golden-angle radial sparse parallel (GRASP) dynamic contrast-enhanced (DCE)-MRI allows simultaneous evaluation of perfusion and morphology in liver fibrosis. METHODS Participants who were scheduled for liver biopsy or resection were enrolled (NCT02480972). Images were reconstructed at 12-s temporal resolution for morphologic assessment and at 3.3-s temporal resolution for quantitative evaluation. The image quality of the morphologic images was assessed on a four-point scale, and the Liver Imaging Reporting and Data System score was recorded for hepatic observations. Comparisons were made between quantitative parameters of DCE-MRI for the different fibrosis stages, and for hepatocellular carcinoma (HCCs) with different LR features. RESULTS DCE-MRI of 64 participants (male = 48) were analyzed. The overall image quality consistently stood at 3.5 ± 0.4 to 3.7 ± 0.4 throughout the exam. Portal blood flow significantly decreased in participants with F2-F3 (n = 18, 175 ± 110 mL/100 mL/min) and F4 (n = 12, 98 ± 47 mL/100 mL/min) compared with those in participants with F0-F1 (n = 34, 283 ± 178 mL/100 mL/min, p < 0.05 for all). In participants with F4, the arterial fraction and extracellular volume were significantly higher than those in participants with F0-F1 and F2-F3 (p < 0.05). Compared with HCCs showing non-LR-M features (n = 16), HCCs with LR-M (n = 5) had a significantly prolonged mean transit time and lower arterial blood flow (p < 0.05). CONCLUSIONS Liver MRI using GRASP obtains both sufficient spatial resolution for confident diagnosis and high temporal resolution for pharmacokinetic modeling. Significant differences were found between the MRI-derived portal blood flow at different hepatic fibrosis stages. KEY POINTS A single MRI examination is able to provide both images with sufficient spatial resolution for anatomic evaluation and those with high temporal resolution for pharmacokinetic modeling. Portal blood flow was significantly lower in clinically significant hepatic fibrosis and mean transit time and extracellular volume increased in cirrhosis, compared with those in no or mild hepatic fibrosis. HCCs with different LR features showed different quantitative parameters of DCE-MRI: longer mean transit time and lower arterial flow were observed in HCCs with LR-M features.
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Affiliation(s)
- Jeong Hee Yoon
- Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.,Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Jeong Min Lee
- Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. .,Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. .,Institute of Radiation Medicine, Seoul National University Medical Research Center, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea.
| | - Mi Hye Yu
- Radiology, Konkuk University School of Medicine, Seoul, 05080, Republic of Korea
| | - Bo Yun Hur
- Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, 06236, Republic of Korea
| | | | - Steven Sourbron
- Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), New York, NY, USA.,Department of Radiology, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Yohan Son
- Siemens Healthcare Korea, Seoul, 03737, Republic of Korea
| | - Susmita Basak
- Biomedical Imaging Sciences Department, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Kyoung-Bun Lee
- Pathology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea
| | - Nam-Joon Yi
- Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea
| | - Kwang-Woong Lee
- Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea
| | - Kyung-Suk Suh
- Surgery, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03087, Republic of Korea
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14
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Zhang Y, Li Z, Gao C, Shen J, Chen M, Liu Y, Cao Z, Pang P, Cui F, Xu M. Preoperative histogram parameters of dynamic contrast-enhanced MRI as a potential imaging biomarker for assessing the expression of Ki-67 in prostate cancer. Cancer Med 2021; 10:4240-4249. [PMID: 34117733 PMCID: PMC8267123 DOI: 10.1002/cam4.3912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/26/2021] [Accepted: 03/28/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To investigate whether preoperative histogram parameters of dynamic contrast‐enhanced MRI (DCE‐MRI) can assess the expression of Ki‐67 in prostate cancer (PCa). Materials and methods A consecutive series of 76 patients with pathology‐proven PCa who underwent routine DCE‐MRI scans were retrospectively recruited. Quantitative parameters including the volume transfer constant (Ktrans), rate contrast (Kep), extracellular‐extravascular volume fraction (Ve), and plasma volume (Vp) by outlining the three‐dimensional volume of interest (VOI) of all lesions were processed. Then, the histogram analyses of these quantitative parameters were performed. The Spearman rank correlation analysis was used to evaluate the correlation of these parameters and Ki‐67 expression of PCa. Receiver operating characteristic (ROC) curve analysis was adopted to evaluate the efficacy of these quantitative histogram parameters in identifying high Ki‐67 expression from low Ki‐67 expression of PCa. Results Eighty‐eight PCa lesions were enrolled in this study, including 31 lesions with high Ki‐67 expression and 57 lesions with low Ki‐67 expression. The median, mean, 75th percentile, and 90th percentile derived from Ktrans and Kep had a moderately positive correlation with Ki‐67 expression (r = 0.361–0.450, p < 0.05), in which both the median and mean of Ktrans had the highest positive correlation (r = 0.450, p < 0.05). The diagnostic efficacy of the Ktrans median, mean, 75th percentile, and 90th percentile, along with the Kep‐based median and mean was assessed by the ROC curve. The area under the curve (AUC) of the mean for Ktrans was the highest (0.826). When the cut‐off of the mean for Ktrans was ≥0.47/min, its Youden index, sensitivity, and specificity were 0.625, 0.871, and 0.754, respectively. The AUC of the median of Kep was the lowest (0.772). Conclusion The histogram of DCE‐MRI quantitative parameters is correlated with Ki‐67 expression, which has the potential to noninvasively assess the expression of Ki‐67 with patients of PCa.
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Affiliation(s)
- Yongsheng Zhang
- Department of Radiology, The Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhiping Li
- Department of Radiology, The Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jianliang Shen
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Mingtao Chen
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Yufeng Liu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhijian Cao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Peipei Pang
- GE Healthcare Life Sciences, Hangzhou, China
| | - Feng Cui
- Department of Radiology, The Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.,The First Clinical Medical College of Zhejiang, Chinese Medical University, Hangzhou, China
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15
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Stocker D, Hectors S, Bane O, Vietti-Violi N, Said D, Kennedy P, Cuevas J, Cunha GM, Sirlin CB, Fowler KJ, Lewis S, Taouli B. Dynamic contrast-enhanced MRI perfusion quantification in hepatocellular carcinoma: comparison of gadoxetate disodium and gadobenate dimeglumine. Eur Radiol 2021; 31:9306-9315. [PMID: 34043055 DOI: 10.1007/s00330-021-08068-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/22/2021] [Accepted: 05/11/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES (1) To assess the quality of the arterial input function (AIF) during dynamic contrast-enhanced (DCE) MRI of the liver and (2) to quantify perfusion parameters of hepatocellular carcinoma (HCC) and liver parenchyma during the first 3 min post-contrast injection with DCE-MRI using gadoxetate disodium compared to gadobenate dimeglumine (Gd-BOPTA) in different patient populations. METHODS In this prospective study, we evaluated 66 patients with 83 HCCs who underwent DCE-MRI, using gadoxetate disodium (group 1, n = 28) or Gd-BOPTA (group 2, n = 38). AIF qualitative and quantitative features were assessed. Perfusion parameters (based on the initial 3 min post-contrast) were extracted in tumours and liver parenchyma, including model-free parameters (time-to-peak enhancement (TTP), time-to-washout) and modelled parameters (arterial flow (Fa), portal venous flow (Fp), total flow (Ft), arterial fraction, mean transit time (MTT), distribution volume (DV)). In addition, lesion-to-liver contrast ratios (LLCRs) were measured. Fisher's exact tests and Mann-Whitney U tests were used to compare the two groups. RESULTS AIF quality, modelled and model-free perfusion parameters in HCC were similar between the 2 groups (p = 0.054-0.932). Liver parenchymal flow was lower and liver enhancement occurred later in group 1 vs group 2 (Fp, p = 0.002; Ft, p = 0.001; TTP, MTT, all p < 0.001), while there were no significant differences in tumour LLCR (max. positive LLCR, p = 0.230; max. negative LLCR, p = 0.317). CONCLUSION Gadoxetate disodium provides comparable AIF quality and HCC perfusion parameters compared to Gd-BOPTA during dynamic phases. Despite delayed and decreased liver enhancement with gadoxetate disodium, LLCRs were equivalent between contrast agents, indicating similar tumour conspicuity. KEY POINTS • Arterial input function quality, modelled, and model-free dynamic parameters measured in hepatocellular carcinoma are similar in patients receiving gadoxetate disodium or gadobenate dimeglumine during the first 3 min post injection. • Gadoxetate disodium and gadobenate dimeglumine show similar lesion-to-liver contrast ratios during dynamic phases in patients with HCC. • There is lower portal and lower total hepatic flow and longer hepatic mean transit time and time-to-peak with gadoxetate disodium compared to gadobenate dimeglumine.
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Affiliation(s)
- Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Institute of Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stefanie Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Naik Vietti-Violi
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Daniela Said
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Radiology, Universidad de los Andes, Santiago, Chile
| | - Paul Kennedy
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Jordan Cuevas
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Guilherme M Cunha
- Liver Imaging Group, Radiology, University of California-San Diego, San Diego, CA, USA
| | - Claude B Sirlin
- Liver Imaging Group, Radiology, University of California-San Diego, San Diego, CA, USA
| | - Kathryn J Fowler
- Liver Imaging Group, Radiology, University of California-San Diego, San Diego, CA, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA.
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