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Mehta N, Kelley RK, Yao FY. Refining the approach to down-staging of HCC prior to liver transplantation: Patient selection, loco-regional treatments, and systemic therapies. Hepatology 2024; 80:238-253. [PMID: 37183865 DOI: 10.1097/hep.0000000000000452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/20/2023] [Indexed: 05/16/2023]
Affiliation(s)
- Neil Mehta
- Division of Gastroenterology, Department of Medicine, University of California, San Francisco, California, USA
| | - R Katie Kelley
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Francis Y Yao
- Division of Gastroenterology, Department of Medicine, University of California, San Francisco, California, USA
- Division of Transplant Surgery, Department of Surgery, University of California, San Francisco, California, USA
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Yilma M, Mehta N. Optimal Liver Transplantation Criteria for Hepatocellular Carcinoma. Surg Oncol Clin N Am 2024; 33:133-142. [PMID: 37945139 DOI: 10.1016/j.soc.2023.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Liver transplantation continues to be the optimal treatment for hepatocellular carcinoma (HCC). Given the limited organ supply, patient selection for liver transplant must carefully balance tumor progression with risk of recurrence posttransplant. There are several pretransplant selection criteria that incorporate biomarkers as well as imaging modality to risk-stratify patients as we continue to look for the optimal transplant cutoff for patients with HCC, which should be transplant-center specific, and account for organ availability and dynamic response to locoregional therapy.
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Affiliation(s)
- Mignote Yilma
- Department of Surgery, University of California San Francisco, 513 Parnassus Avenue, S-321, San Francisco, CA 94143, USA; National Clinician Scholars Program, University of California San Francisco, 513 Parnassus Avenue, S-321, San Francisco, CA 94143, USA. https://twitter.com/mignoteyilmaMD
| | - Neil Mehta
- Department of Medicine, University of California San Francisco, Connie Frank Transplant Center, 400 Parnassus Avenue 7th Floor, San Francisco, CA 94143, USA.
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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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Patel R, Aslam A, Parikh ND, Mervak B, Mubarak E, Higgins L, Lala K, Conner JF, Khaykin V, Bashir M, Do RKG, Burke LMB, Smith EN, Kim CY, Shampain KL, Owen D, Mendiratta-Lala M. Updates on LI-RADS Treatment Response Criteria for Hepatocellular Carcinoma: Focusing on MRI. J Magn Reson Imaging 2023; 57:1641-1654. [PMID: 36872608 PMCID: PMC11078141 DOI: 10.1002/jmri.28659] [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: 12/16/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/07/2023] Open
Abstract
As the incidence of hepatocellular carcinoma (HCC) and subsequent treatments with liver-directed therapies rise, the complexity of assessing lesion response has also increased. The Liver Imaging Reporting and Data Systems (LI-RADS) treatment response algorithm (LI-RADS TRA) was created to standardize the assessment of response after locoregional therapy (LRT) on contrast-enhanced CT or MRI. Originally created based on expert opinion, these guidelines are currently undergoing revision based on emerging evidence. While many studies support the use of LR-TRA for evaluation of HCC response after thermal ablation and intra-arterial embolic therapy, data suggest a need for refinements to improve assessment after radiation therapy. In this manuscript, we review expected MR imaging findings after different forms of LRT, clarify how to apply the current LI-RADS TRA by type of LRT, explore emerging literature on LI-RADS TRA, and highlight future updates to the algorithm. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Richa Patel
- Department of Radiology, Stanford, California, USA
| | - Anum Aslam
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Neehar D Parikh
- Department of Internal Medicine, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Benjamin Mervak
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Eman Mubarak
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Lily Higgins
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Kayli Lala
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Jack F Conner
- Department of Radiology, University of Toledo Medical Center, Toledo, Ohio, USA
| | - Valerie Khaykin
- Department of Radiology and Hepatology, University of Michigan Medicine, Michigan, USA
| | - Mustafa Bashir
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Richard Kinh Gian Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Lauren M B Burke
- Department of Radiology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Elainea N Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Charles Y Kim
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Kimberly L Shampain
- Department of Radiology, University of Michigan Medicine, Ann Arbor, Michigan, USA
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, Minnesota, USA
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Li JP, Zhao S, Jiang HJ, Jiang H, Zhang LH, Shi ZX, Fan TT, Wang S. Quantitative dual-energy computed tomography texture analysis predicts the response of primary small hepatocellular carcinoma to radiofrequency ablation. Hepatobiliary Pancreat Dis Int 2022; 21:569-576. [PMID: 35729000 DOI: 10.1016/j.hbpd.2022.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 05/31/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Radiofrequency ablation (RFA) is one of the effective therapeutic modalities in patients with hepatocellular carcinoma (HCC). However, there is no proper method to evaluate the HCC response to RFA. This study aimed to establish and validate a clinical prediction model based on dual-energy computed tomography (DECT) quantitative-imaging parameters, clinical variables, and CT texture parameters. METHODS We enrolled 63 patients with small HCC. Two to four weeks after RFA, we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients' clinical baseline variables. DECT images were manually segmented, and 56 CT texture features were extracted. We used LASSO algorithm for feature selection and data dimensionality reduction; logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters; we then added texture features to build a clinical-texture model based on clinical model. RESULTS A total of six optimal CT texture analysis (CTTA) features were selected, which were statistically different between patients with or without tumor progression (P < 0.05). When clinical variables and DECT-quantitative parameters were included, the clinical models showed that albumin-bilirubin grade (ALBI) [odds ratio (OR) = 2.77, 95% confidence interval (CI): 1.35-6.65, P = 0.010], λAP (40-100 keV) (OR = 3.21, 95% CI: 3.16-5.65, P = 0.045) and ICAP (OR = 1.25, 95% CI: 1.01-1.62, P = 0.028) were associated with tumor progression, while the clinical-texture models showed that ALBI (OR = 2.40, 95% CI: 1.19-5.68, P = 0.024), λAP (40-100 keV) (OR = 1.43, 95% CI: 1.10-2.07, P = 0.019), and CTTA-score (OR = 2.98, 95% CI: 1.68-6.66, P = 0.001) were independent risk factors for tumor progression. The clinical model, clinical-texture model, and CTTA-score all performed well in predicting tumor progression within 12 months after RFA (AUC = 0.917, 0.962, and 0.906, respectively), and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957, respectively. CONCLUSIONS DECT-quantitative parameters, CTTA, and clinical variables were helpful in predicting HCC progression after RFA. The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.
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Affiliation(s)
- Jin-Ping Li
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Sheng Zhao
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Hui-Jie Jiang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China.
| | - Hao Jiang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Lin-Han Zhang
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China; Department of Nuclear Medicine, the First Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Zhong-Xing Shi
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Ting-Ting Fan
- Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
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Youn SY, Kim B, Kim DH, Choi HJ, Sung PS, Choi JI. Liver Imaging-Reporting and Data System treatment response algorithm predicts postsurgical recurrence in locoregional therapy–treated hepatocellular carcinoma. Eur Radiol 2022; 32:6270-6280. [DOI: 10.1007/s00330-022-08720-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 02/24/2022] [Accepted: 03/07/2022] [Indexed: 11/04/2022]
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Mendiratta-Lala M, Aslam A, Maturen KE, Westerhoff M, Sun Y, Maurino C, Parikh ND, Sonnenday CJ, Stein EB, Shampain KL, Kaza RK, Cuneo K, Masch W, Do RKG, Lawrence TS, Owen D. LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy With Radiologic-Pathologic Explant Correlation in Patients With SBRT-Treated Hepatocellular Carcinoma. Int J Radiat Oncol Biol Phys 2022; 112:704-714. [PMID: 34644607 PMCID: PMC9400832 DOI: 10.1016/j.ijrobp.2021.10.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/30/2021] [Accepted: 10/05/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE Our purpose was to evaluate the accuracy of LI-RADS Treatment Response Algorithm (LR-TRA) for assessing the viability of hepatocellular carcinoma (HCC) treated with stereotactic body radiation therapy (SBRT), using explant pathology as the gold standard. METHODS AND MATERIALS This retrospective study included patients who underwent SBRT for locoregional treatment of HCC between 2008 and 2019 with subsequent liver transplantation. Five radiologists independently assessed all treated lesions by using the LR-TRA. Imaging and posttransplant histopathology were compared. Lesions were categorized as either completely (100%) or incompletely (<100%) necrotic, and performance characteristics and predictive values for the LR-TR viable and nonviable categories were calculated for each reader. Interreader reliability was calculated using the Fleiss kappa test. RESULTS A total of 40 treated lesions in 26 patients (median age, 63 years [interquartile range, 59.4-65.5]; 23 men) were included. For lesions treated with SBRT, sensitivity for incomplete tumor necrosis across readers ranged between 71% and 86%, specificity between 85% and 96%, and positive predictive value between 86% and 92%, when the LR-TR equivocal category was treated as nonviable, accounting for subject clustering. When the LR-TR equivocal category was treated as viable, sensitivity of complete tumor necrosis for lesions treated with SBRT ranged from 88% to 96%, specificity from 71% to 93%, and negative predictive value from 85% to 96%. Interreader reliability was fair (k = 0.22; 95% confidence interval, 0.13-0.33). Although a loss of arterial phase hyperenhancement (APHE) was highly correlated with pathologically nonviable tumor on explant, almost half of the patients with APHE had pathologically nonviable tumor on explant. CONCLUSIONS LR-TRA v2018 performs well for predicting complete and incomplete necrosis in HCC treated with SBRT. In contrast to other locoregional therapies, the presence of APHE after SBRT does not always indicate viable tumor and suggests that observation may be an appropriate strategy for these patients.
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Affiliation(s)
| | | | | | - Maria Westerhoff
- Department of Pathology and clinical labs, University of Michigan Health System, NCRC building 35 2800 Plymouth Road Ann Arbor, MI 48109
| | | | | | - Neehar D. Parikh
- Division of Gastroenterology and Hepatology, University of Michigan School of Medicine
| | | | | | | | | | - Kyle Cuneo
- Department of Radiation Oncology, University of Michigan School of Medicine
| | | | | | | | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic Rochester, 200 First St SW, Rochester, MN 55905
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Inter-observer agreement using the LI-RADS version 2018 CT treatment response algorithm in patients with hepatocellular carcinoma treated with conventional transarterial chemoembolization. Abdom Radiol (NY) 2022; 47:115-122. [PMID: 34581927 PMCID: PMC8776670 DOI: 10.1007/s00261-021-03272-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 09/02/2021] [Accepted: 09/03/2021] [Indexed: 11/06/2022]
Abstract
Aim To determine inter-reader agreement in categorization of imaging features using the Liver Imaging Reporting and Data System (LI-RADS) treatment response (LR-TR) algorithm in patients with hepatocellular carcinoma (HCC) treated with conventional transarterial chemoembolization (cTACE). Methods Two radiologists used the LR-TR algorithm to assess 112 computed tomography (CT) examinations of 102 patients treated with cTACE. The inter-observer agreement in categorization of LR-TR features was assessed using kappa (κ) statistics. Results There was substantial inter-observer agreement between the two reviewers using the LR-TR algorithm (κ = 0.70; 95% CI 0.58–0.81). The two reviewers categorized tumors as non-viable in 37 (33.0%) and 39 (34.8%) of 112 examinations, viable in 58 (51.8%) and 62 (55.4%) examinations, and equivocal in 18 (16.1%) and 11 (9.8%) examinations, respectively. There was almost perfect inter-observer agreement for the LR-TR non-viable category (κ = 0.80; 95% CI 0.68–0.92), substantial agreement for the viable category (κ = 0.78 95% CI 0.67–0.90), and fair agreement for the equivocal category (κ = 0.25; 95% CI 0.02–0.49). Conclusion The LR-TR algorithm conveys high degrees of inter-observer agreement for the assessment of CT imaging features in the viable and non-viable categories. Further refinement of indeterminate features may be necessary to improve the correct categorization of equivocal lesions. Graphic abstract ![]()
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Moura Cunha G, Chernyak V, Fowler KJ, Sirlin CB. Up-to-Date Role of CT/MRI LI-RADS in Hepatocellular Carcinoma. J Hepatocell Carcinoma 2021; 8:513-527. [PMID: 34104640 PMCID: PMC8180267 DOI: 10.2147/jhc.s268288] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/01/2021] [Indexed: 12/16/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of mortality worldwide and a major healthcare burden in most societies. Computed tomography (CT) and magnetic resonance imaging (MRI) play a pivotal role in the medical care of patients with or at risk for hepatocellular carcinoma (HCC). When stringent imaging criteria are fulfilled, CT and MRI allow for diagnosis, staging, and assessment of response to treatment, without the need for invasive workup, and can inform clinical decision making. Owing to the central role of these imaging modalities in HCC management, standardization is essential to facilitate proper imaging technique, accurate interpretation, and clear communication among all stakeholders in both the clinical practice and research settings. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system that provides standardization across the continuum of HCC imaging, including ordinal probabilistic approach for reporting that directs individualized management. This review discusses the up-to-date role of CT and MRI in HCC imaging from the LI-RADS perspective. It also provides a glimpse into the future by discussing how advances in knowledge and technology are likely to enrich the LI-RADS approach.
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Affiliation(s)
- Guilherme Moura Cunha
- Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Victoria Chernyak
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Kathryn J Fowler
- Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Claude B Sirlin
- Liver Imaging Group, Department of Radiology, University of California San Diego, La Jolla, CA, USA
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