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Song Y, Zhang YY, Yu Q, Ma R, Xiao Y, Shen JK, Wei CG. Modified LR-5 criteria based on gadoxetic acid can improve the sensitivity in the diagnosis of hepatocellular carcinoma. World J Radiol 2025; 17:103822. [PMID: 40176954 PMCID: PMC11959622 DOI: 10.4329/wjr.v17.i3.103822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2024] [Revised: 01/24/2025] [Accepted: 02/21/2025] [Indexed: 03/27/2025] Open
Abstract
BACKGROUND Currently, only tumors classified as LR-5 are considered definitive hepatocellular carcinoma (HCC), and no further pathologic confirmation is required to initiate therapy. Previous studies have shown that the sensitivity of LR-5 is modest, and lesions enhanced by gadoxetic acid (Gd-EOB-DTPA) may exhibit lower sensitivity than those enhanced by Gd-DTPA. AIM To identify malignant ancillary features (AFs) that can independently and significantly predict HCC in Liver Imaging Reporting and Data System version 2018, and to develop modified LR-5 criteria to improve diagnostic performance on Gd-EOB-DTPA - enhanced magnetic resonance imaging. METHODS Imaging data from patients with HCC risk factors who underwent abdominal Gd-EOB-DTPA - enhanced magnetic resonance imaging were collected. Univariate and multivariate logistic regression analyses were performed to determine AFs that could independently and significantly predict HCC. The modified LR-5 criteria involved reclassifying LR-4/LR-3 lesions based on major features combined with independently significant AFs for HCC, or by substituting threshold growth with significant AFs. McNemar's test was used to compare the diagnostic performance of the modified LR-5 criteria. RESULTS A total of 244 lesions from 216 patients were included. Transitional phase hypointensity, mild - moderate T2 hyperintensity, and fat in mass (more than adjacent liver) were identified as significant independent predictors of HCC. Using the modified LR-5 criteria (e.g., LR-5-M1: LR-4 + transitional phase hypointensity; LR-5-M4: LR-5 by transitional phase hypointensity instead of threshold growth; LR-5-M5: LR-5 by mild - moderate T2 hyperintensity instead of threshold growth; LR-5-M8: LR-3/LR-4 + any two features of transitional phase hypointensity/mild - moderate T2 hyperintensity/fat in mass), sensitivities were significantly increased (88.5%-89.1%) compared to the standard LR-5 (60.6%; all P values < 0.05), while specificities (84.8%-89.9%) remained largely unchanged (93.7%; all P values > 0.05). The LR-5-M8 criterion achieved the highest sensitivity. CONCLUSION Mild - moderate T2 hyperintensity, transitional phase hypointensity, and fat in mass are independent and significant predictors of HCC malignant AFs. The modified LR-5 criteria can improve sensitivity without significantly reducing specificity.
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Affiliation(s)
- Yan Song
- Department of Radiology, Jieshou City People's Hospital (Jieshou Hospital Affiliated to Anhui Medical College), Fuyang 236500, Anhui Province, China
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215123, Jiangsu Province, China
| | - Yue-Yue Zhang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215123, Jiangsu Province, China
| | - Qin Yu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215123, Jiangsu Province, China
- Department of Radiology, Dongtai City People's Hospital, Yancheng 224200, Jiangsu Province, China
| | - Rui Ma
- Department of Dialysis Center, Jieshou City People's Hospital (Jieshou Hospital Affiliated to Anhui Medical College), Fuyang 236500, Anhui Province, China
| | - Yue Xiao
- Department of Intensive Care Unit, Jieshou City People's Hospital (Jieshou Hospital Affiliated to Anhui Medical College), Fuyang 236500, Anhui Province, China
| | - Jun-Kang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215123, Jiangsu Province, China
| | - Chao-Gang Wei
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215123, Jiangsu Province, China
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Kulkarni AM, Kruse D, Harper K, Lam E, Osman H, Ansari DH, Sivanesan U, Bashir MR, Costa AF, McInnes M, van der Pol CB. Current State of Evidence for Use of MRI in LI-RADS. J Magn Reson Imaging 2025. [PMID: 39981949 DOI: 10.1002/jmri.29748] [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: 12/21/2024] [Revised: 02/07/2025] [Accepted: 02/08/2025] [Indexed: 02/22/2025] Open
Abstract
The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) is the preeminent framework for classification and risk stratification of liver observations on imaging in patients at high risk for hepatocellular carcinoma. In this review, the pathogenesis of hepatocellular carcinoma and the use of MRI in LI-RADS is discussed, including specifically the LI-RADS diagnostic algorithm, its components, and its reproducibility with reference to the latest supporting evidence. The LI-RADS treatment response algorithms are reviewed, including the more recent radiation treatment response algorithm. The application of artificial intelligence, points of controversy, LI-RADS relative to other liver imaging systems, and possible future directions are explored. After reading this article, the reader will have an understanding of the foundation and application of LI-RADS as well as possible future directions.
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Affiliation(s)
- Ameya Madhav Kulkarni
- Department of Medical Imaging, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Danielle Kruse
- Departments of Radiology and Medicine, Duke University Medical Center, Durham, North Carolina, USA
| | - Kelly Harper
- Department of Radiology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Eric Lam
- Ottawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada
| | - Hoda Osman
- Ottawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada
| | - Danyaal H Ansari
- Ottawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada
| | - Umaseh Sivanesan
- Department of Diagnostic Radiology, Kingston Health Sciences Centre, Kingston General Hospital, Kingston, Ontario, Canada
| | - Mustafa R Bashir
- Departments of Radiology and Medicine, Duke University Medical Center, Durham, North Carolina, USA
- Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, North Carolina, USA
| | - Andreu F Costa
- Queen Elizabeth II Health Sciences Centre and Dalhousie University, Halifax, Nova Scotia, Canada
| | - Matthew McInnes
- Department of Radiology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
- Ottawa Hospital Research Institute Clinical Epidemiology Program, Ottawa, Ontario, Canada
| | - Christian B van der Pol
- Department of Medical Imaging, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Department of Diagnostic Imaging, Juravinski Hospital and Cancer Centre, Hamilton Health Sciences, Hamilton, Ontario, Canada
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Li Y, Li X, Xiao X, Cheng J, Li Q, Liu C, Cai P, Chen W, Zhang H, Li X. A novel hybrid model for predicting tertiary lymphoid structures and targeted immunotherapy outcomes in hepatocellular carcinoma: a multicenter retrospective study. Eur Radiol 2024:10.1007/s00330-024-11255-9. [PMID: 39658681 DOI: 10.1007/s00330-024-11255-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 09/29/2024] [Accepted: 11/24/2024] [Indexed: 12/12/2024]
Abstract
OBJECTIVE To develop a novel hybrid model for preoperative prediction of tertiary lymphoid structures (TLSs) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from postoperative targeted immunotherapy. METHODS Retrospective data were gathered from 332 patients with HCC who underwent surgical resection and gadoxetate disodium (Gd-EOB-DTPA) enhanced MRI at two tertiary hospitals (training cohort, n = 205; internal validation cohort, n = 90; and external validation cohort, n = 37) between March 2020 and January 2023. Radiomic features were extracted from Gd-EOB-DTPA-enhanced MRI sequences. These signatures were integrated with clinical-radiologic (CR) factors into a hybrid model and nomogram for clinical application. The performance of the model was assessed using the area under the curve (AUC) and 95% confidence intervals (CI). RESULTS The hybrid model outperformed the radiomics and CR models in the training cohort (AUC = 0.860 [95% CI: 0.805, 0.904], 0.784 [95% CI: 0.721, 0.838], and 0.809 [95% CI: 0.748, 0.860]). The hybrid model showed optimal performance, with AUCs of 0.823 (95% CI: 0.728, 0.895) and 0.875 (95% CI: 0.725, 0.960) in the internal and external validation cohorts, respectively. The calibration curve demonstrated that the nomogram had good diagnostic ability, and decision curve analysis indicated good clinical utility across all cohorts. Importantly, patients with a predicted high risk of TLSs from the hybrid model gained a survival benefit from targeted immunotherapy. CONCLUSION The hybrid model showed satisfactory performance in predicting intra-tumoral TLS positivity and targeted immunotherapy benefit in patients with HCC, potentially assisting clinicians in selecting precise individualized therapies. KEY POINTS Question How can accurate preoperative risk stratification of tertiary lymphoid structures positivity HCC be achieved to support targeted immunotherapy decision-making? Findings A hybrid model combining radiomics model and clinical-radiological model may be a reliable marker for predicting tertiary lymphoid structures positivity HCC. Clinical relevance Using this hybrid model may be useful in predicting tertiary lymphoid structures and screening candidate patients for targeted immunotherapy based on multiparametric MRI, which has potential clinical value in guiding clinical decision-making and improving patient outcomes.
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Affiliation(s)
- Yiman Li
- 7T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Xiaofeng Li
- Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xixi Xiao
- Department of Oncology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jie Cheng
- 7T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Qingrui Li
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Chen Liu
- 7T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China
| | - Ping Cai
- 7T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
| | - Wei Chen
- 7T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
| | - Huarong Zhang
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
| | - Xiaoming Li
- 7T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
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Liu Y, Xiao Y, Ni X, Huang P, Wu F, Zhou C, Xu J, Zeng M, Yang C. Value of magnetic resonance imaging for diagnosis of LR‑3 and LR-4 lesions coexisting with hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:2629-2638. [PMID: 38834779 DOI: 10.1007/s00261-024-04338-0] [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: 02/29/2024] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE To explore which preoperative clinical data and conventional magnetic resonance imaging (MRI) features may indicate the presence of hepatocellular carcinoma (HCC) in HCC patients coexisting with LR-3 and LR-4 lesions. METHODS HCC Patients coexisting with LR-3 and LR-4 lesions who participated in a prospective clinical trial (XX) were included in this study. Two radiologists independently assessed the preoperative MRI features and each lesion was assigned according to the liver imaging reporting and data system (LI-RADS). The preoperative clinical data were also evaluated. The relative values of these parameters were assessed as potential predictors of HCC for coexisting LR-3 and LR-4 lesions. RESULTS We enrolled 102 HCC patients (58.1 ± 11.5 years; 84.3% males) coexisting with 110 LR-3 and LR-4 lesions (HCCs group [n = 66]; non-HCCs group [n = 44]). The presence of restricted diffusion (OR: 18.590, p < 0.001), delayed enhancement (OR: 0.113, p < 0.001), and mild-moderate T2 hyperintensity (OR: 3.084, p = 0.048) were found to be independent predictors of HCC diagnosis. The sensitivity and specificity of the above independent variables for the diagnosis of HCC ranged from 66.7 to 80.3% and 56.8 to 88.6%, respectively. ROC analysis showed that, in discriminating HCC, the AUCs of the above factors were 0.777, 0.686, and 0.670, respectively. Combining these three findings for the prediction of HCC resulted in a specificity greater than 97%, and the AUC further increased to 0.874. CONCLUSION The presence of restricted diffusion, delayed enhancement, and mild-moderate T2 hyperintensity can be useful features for risk stratification of coexisting LR-3 and LR-4 lesions in HCC patients. Trial registration a prospective clinical trial (ChiCTR2000036201).
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Affiliation(s)
- Yang Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing University Medical School, No. 1 Lijiang Road, Suzhou, 215153, Jiangsu, China
| | - Yuyao Xiao
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Xiaoyan Ni
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Peng Huang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Fei Wu
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Changwu Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China
| | - Jianming Xu
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing University Medical School, No. 1 Lijiang Road, Suzhou, 215153, Jiangsu, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China.
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
- Department of Cancer Center, Zhongshan Hospital, Fudan University, Xuhui District, No. 180 Fenglin Road, Shanghai, 200032, China.
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Lyu R, Hu WJ, Wang D, Wang J, Ye YB, Jia KF. Simplified liver imaging reporting and data system for the diagnosis of hepatocellular carcinoma on gadoxetic acid-enhanced magnetic resonance imaging. World J Gastrointest Oncol 2024; 16:2439-2448. [PMID: 38994131 PMCID: PMC11236241 DOI: 10.4251/wjgo.v16.i6.2439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/28/2024] [Accepted: 04/11/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND The liver imaging reporting and data system (LI-RADS) diagnostic table has 15 cells and is too complex. The diagnostic performance of LI-RADS for hepatocellular carcinoma (HCC) is not satisfactory on gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). AIM To evaluate the ability of the simplified LI-RADS (sLI-RADS) to diagnose HCC on EOB-MRI. METHODS A total of 331 patients with 356 hepatic observations were retrospectively analysed. The diagnostic performance of sLI-RADS A-D using a single threshold was evaluated and compared with LI-RADS v2018 to determine the optimal sLI-RADS. The algorithms of sLI-RADS A-D are as follows: The single threshold for sLI-RADS A and B was 10 mm, that is, classified observations ≥ 10mm using an algorithm of 10-19 mm observations (sLI-RADS A) and ≥ 20 mm observations (sLI-RADS B) in the diagnosis table of LI-RADS v2018, respectively, while the classification algorithm remained unchanged for observations < 10 mm; the single threshold for sLI-RADS C and D was 20 mm, that is, for < 20 mm observations, the algorithms for < 10 mm observations (sLI-RADS C)and 10-19 mm observations (sLI-RADS D) were used, respectively, while the algorithm remained unchanged for observations ≥ 20 mm. With hepatobiliary phase (HBP) hypointensity as a major feature (MF), the final sLI-RADS (F-sLI-RADS) was formed according to the optimal sLI-RADS, and its diagnostic performance was evaluated. The times needed to classify the observations according to F-sLI-RADS and LI-RADS v2018 were compared. RESULTS The optimal sLI-RADS was sLI-RADS D (with a single threshold of 20 mm), because its sensitivity was greater than that of LI-RADS v2018 (89.8% vs 87.0%, P = 0.031), and its specificity was not lower (89.4% vs 90.1%, P > 0.999). With HBP hypointensity as an MF, the sensitivity of F-sLI-RADS was greater than that of LI-RADS v2018 (93.0% vs 87.0%, P < 0.001) and sLI-RADS D (93.0% vs 89.8%, P = 0.016), without a lower specificity (86.5% vs 90.1%, P = 0.062; 86.5% vs 89.4%, P = 0.125). Compared with that of LI-RADS v2018, the time to classify lesions according to F-sLI-RADS was shorter (51 ± 21 s vs 73 ± 24 s, P < 0.001). CONCLUSION The use of sLI-RADS with HBP hypointensity as an MF may improve the sensitivity of HCC diagnosis and reduce lesion classification time.
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Affiliation(s)
- Rong Lyu
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Wei-Juan Hu
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Di Wang
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Jiao Wang
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Yu-Bing Ye
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Ke-Feng Jia
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
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Lyu R, Hu WJ, Wang D, Wang J, Ye YB, Jia KF. Simplified liver imaging reporting and data system for the diagnosis of hepatocellular carcinoma on gadoxetic acid-enhanced magnetic resonance imaging. World J Gastrointest Oncol 2024; 16:2427-2436. [DOI: 10.4251/wjgo.v16.i6.2427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/28/2024] [Accepted: 04/11/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND The liver imaging reporting and data system (LI-RADS) diagnostic table has 15 cells and is too complex. The diagnostic performance of LI-RADS for hepatocellular carcinoma (HCC) is not satisfactory on gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI).
AIM To evaluate the ability of the simplified LI-RADS (sLI-RADS) to diagnose HCC on EOB-MRI.
METHODS A total of 331 patients with 356 hepatic observations were retrospectively analysed. The diagnostic performance of sLI-RADS A-D using a single threshold was evaluated and compared with LI-RADS v2018 to determine the optimal sLI-RADS. The algorithms of sLI-RADS A-D are as follows: The single threshold for sLI-RADS A and B was 10 mm, that is, classified observations ≥ 10mm using an algorithm of 10-19 mm observations (sLI-RADS A) and ≥ 20 mm observations (sLI-RADS B) in the diagnosis table of LI-RADS v2018, respectively, while the classification algorithm remained unchanged for observations < 10 mm; the single threshold for sLI-RADS C and D was 20 mm, that is, for < 20 mm observations, the algorithms for < 10 mm observations (sLI-RADS C)and 10-19 mm observations (sLI-RADS D) were used, respectively, while the algorithm remained unchanged for observations ≥ 20 mm. With hepatobiliary phase (HBP) hypointensity as a major feature (MF), the final sLI-RADS (F-sLI-RADS) was formed according to the optimal sLI-RADS, and its diagnostic performance was evaluated. The times needed to classify the observations according to F-sLI-RADS and LI-RADS v2018 were compared.
RESULTS The optimal sLI-RADS was sLI-RADS D (with a single threshold of 20 mm), because its sensitivity was greater than that of LI-RADS v2018 (89.8% vs 87.0%, P = 0.031), and its specificity was not lower (89.4% vs 90.1%, P > 0.999). With HBP hypointensity as an MF, the sensitivity of F-sLI-RADS was greater than that of LI-RADS v2018 (93.0% vs 87.0%, P < 0.001) and sLI-RADS D (93.0% vs 89.8%, P = 0.016), without a lower specificity (86.5% vs 90.1%, P = 0.062; 86.5% vs 89.4%, P = 0.125). Compared with that of LI-RADS v2018, the time to classify lesions according to F-sLI-RADS was shorter (51 ± 21 s vs 73 ± 24 s, P < 0.001).
CONCLUSION The use of sLI-RADS with HBP hypointensity as an MF may improve the sensitivity of HCC diagnosis and reduce lesion classification time.
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Affiliation(s)
- Rong Lyu
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Wei-Juan Hu
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Di Wang
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Jiao Wang
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Yu-Bing Ye
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
| | - Ke-Feng Jia
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin 300170, China
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Chen J, Chen H, Zheng D, Yan C, Ye R, Wen L, Li Y. LI-RADS category 3, 4, and M observations: a multiple parameters diagnostic model for hepatocellular carcinoma. Acta Radiol 2023; 64:2977-2986. [PMID: 37753552 DOI: 10.1177/02841851231203830] [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: 09/28/2023]
Abstract
BACKGROUND Hepatic lesions categorized as LR-3, LR-4, and LR-M are challenging to accurately assess and diagnose. PURPOSE To combine potential clinical and/or magnetic resonance imaging (MRI) features for a more comprehensive hepatocellular carcinoma (HCC) versus non-HCC diagnosis for patients with LR-3, LR-4, and LR-M graded lesions. METHODS Data were consecutively retrieved from 82 at-risk patients with LR-3 (n = 43), LR-4 (n = 20), and LR-M (n = 23) lesions. Significant findings for the differentiation of HCC and non-HCC, including MRI features and clinical factors, were identified with univariable and multivariable analyses. The variables for a prediction model were selected through stepwise use of Akaike's Information Criterion (AIC) to build multivariable logistic regression model. RESULTS Serum alpha-fetoprotein (AFP) >16.2 ng/mL (odds ratio [OR] = 22.4; P = 0.006), septum (OR = 52.1; P = 0.011), and hepatobiliary phase (HBP) hypointensity (OR = 40.2; P = 0.001) were confirmed as independent predictors of HCC. When combining the three predictors and mild-moderate T2 hyperintensity, the model (AIC = 50.91) showed good accuracy with a C-index of 0.948. CONCLUSION In at-risk patients with LR-3, LR-4, or LR-M lesions, integrating AFP, septum, HBP hypointensity, and mild-moderate T2 hyperintensity achieved high diagnostic performance for the diagnosis of HCC.
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Affiliation(s)
- Jianwei Chen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, PR China
| | - Huizhen Chen
- Department of Good Clinical Practice, Fuzhou Pulmonary Hospital, Fuzhou, Fujian Province, PR China
| | - Dechun Zheng
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, PR China
| | - Chuan Yan
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, PR China
| | - Rongping Ye
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, PR China
| | - Liting Wen
- Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, PR China
| | - Yueming Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian Province, PR China
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Nishioka E, Sofue K, Maruyama K, Ueshima E, Ueno Y, Tsurusaki M, Komatsu S, Fukumoto T, Murakami T. Improved diagnosis of histological capsule in hepatocallular carcinoma by using nonenhancing capsule appearance in addition to enhancing capsule appearance in gadoxetic acid-enhanced MRI. Sci Rep 2023; 13:6113. [PMID: 37059750 PMCID: PMC10104865 DOI: 10.1038/s41598-023-33048-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 04/06/2023] [Indexed: 04/16/2023] Open
Abstract
To assess the value of nonenhancing capsule by adding to enhancing capsule in gadoxetic acid-enhanced MRI (EOB-MRI) in comparison with contrast-enhanced CT (CE-CT) for diagnosing histological capsule in hepatocellular carcinoma (HCC). One-hundred fifty-one patients with HCC who underwent both CE-CT and EOB-MRI were retrospectively reviewed. Liver Imaging-Reporting and Data System (LI-RADS) v2018 imaging features, including enhancing and nonenhancing capsule were evaluated by two readers in CE-CT and EOB-MRI. Frequencies of each imaging feature were compared between CE-CT and EOB-MRI. The area under the receiver operating characteristic (AUC) curve for the diagnosis of histological capsule was compared across the following three imaging criteria: (1) enhancing capsule in CE-CT, (2) enhancing capsule in EOB-MRI, and (3) enhancing/nonenhancing capsule in EOB-MRI. Enhancing capsule in EOB-MRI was significantly less frequently depicted than that in CE-CT (p < 0.001 and = 0.016 for reader 1 and 2). Enhancing/nonenhancing capsule in EOB-MRI achieved a similar frequency of enhancing in CE-CT (p = 0.590 and 0.465 for reader 1 and 2). Adding nonenhancing capsule to enhancing capsule in EOB-MRI significantly increased AUCs (p < 0.001 for both readers) and achieved similar AUCs compared with enhancing capsule in CE-CT (p = 0.470 and 0.666 for reader 1 and 2). Adding nonenhancing capsule to the definition of capsule appearance can improve the diagnosis of capsule in EOB-MRI for the diagnosis of histological capsule in HCC and decrease discordance of capsule appearance between EOB-MRI and CE-CT.
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Affiliation(s)
- Eiko Nishioka
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan.
| | - Koji Maruyama
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Eisuke Ueshima
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yoshiko Ueno
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Masakatsu Tsurusaki
- Department of Radiology, Faculty of Medicine, Kindai University, Osaka-Sayama, Japan
| | - Shohei Komatsu
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Takumi Fukumoto
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
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9
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Chernyak V, Fowler KJ, Do RKG, Kamaya A, Kono Y, Tang A, Mitchell DG, Weinreb J, Santillan CS, Sirlin CB. LI-RADS: Looking Back, Looking Forward. Radiology 2023; 307:e222801. [PMID: 36853182 PMCID: PMC10068888 DOI: 10.1148/radiol.222801] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/09/2023] [Accepted: 01/23/2023] [Indexed: 03/01/2023]
Abstract
Since its initial release in 2011, the Liver Imaging Reporting and Data System (LI-RADS) has evolved and expanded in scope. It started as a single algorithm for hepatocellular carcinoma (HCC) diagnosis with CT or MRI with extracellular contrast agents and has grown into a multialgorithm network covering all major liver imaging modalities and contexts of use. Furthermore, it has developed its own lexicon, report templates, and supplementary materials. This article highlights the major achievements of LI-RADS in the past 11 years, including adoption in clinical care and research across the globe, and complete unification of HCC diagnostic systems in the United States. Additionally, the authors discuss current gaps in knowledge, which include challenges in surveillance, diagnostic population definition, perceived complexity, limited sensitivity of LR-5 (definite HCC) category, management implications of indeterminate observations, challenges in reporting, and treatment response assessment following radiation-based therapies and systemic treatments. Finally, the authors discuss future directions, which will focus on mitigating the current challenges and incorporating advanced technologies. Tha authors envision that LI-RADS will ultimately transform into a probability-based system for diagnosis and prognostication of liver cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.
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Affiliation(s)
- Victoria Chernyak
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - Kathryn J. Fowler
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - Richard K. G. Do
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - Aya Kamaya
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - Yuko Kono
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - An Tang
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - Donald G. Mitchell
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - Jeffrey Weinreb
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - Cynthia S. Santillan
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
| | - Claude B. Sirlin
- From the Department of Radiology, Memorial Sloan-Kettering Cancer
Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of
Radiology, University of California, San Diego, San Diego, Calif (K.J.F.,
C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center,
Stanford, Calif (A.K.); Department of Medicine and Radiology, University of
California, San Diego, San Diego, Calif (Y.K.); Department of Radiology,
Radiation Oncology and Nuclear Medicine, Université de Montréal,
Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson
University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology,
Yale Medical School, New Haven, Conn (J.W.)
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10
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Lyu R, Hu W, Wang D, Wang J, Gao Z, Jia K. LI-RADS v2018: utilizing ancillary features on gadoxetic acid-enhanced MRI to improve the diagnostic performance of small hapatocellular carcinoma (≤ 20 mm). Abdom Radiol (NY) 2023; 48:1987-1994. [PMID: 36939913 DOI: 10.1007/s00261-023-03871-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/23/2023] [Accepted: 02/23/2023] [Indexed: 03/21/2023]
Abstract
PURPOSE To evaluate the role of ancillary features (AFs) of Liver Imaging Reporting and Data System (LI-RADS) in the diagnostic performance of small HCC (≤ 20 mm) on gadoxetic acid-enhanced MRI. METHODS A total of 154 patients with 183 hepatic observations were analysed in this retrospective study. Observations were categorized using only major features (MFs) and combined MFs and AFs. Independently significant AFs were identified through logistic regression analysis, and upgraded LR-5 criteria were developed using these as new MFs. The diagnostic performance of the modified LI-RADS (mLI-RADS) was calculated and compared with that of LI-RADS v2018 using McNemar's test. RESULTS Restricted diffusion, transitional and hepatobiliary phase hypointensity were independently significant AFs. The mLI-RADS a, c, e, g, h and i (upgraded LR-4 lesions that were categorized using only MFs to LR-5 using a certain or any one, two, three of the above AFs as new MFs) yielded a significantly greater sensitivity than that of the LI-RADS v2018 (68.0%, 69.1%, 69.1%, 69.1%, 69.1%, 68.0% vs. 61.9%, all p < 0.05), whereas the specificities were not significantly different (84.9%, 86.0%, 84.9%, 83.7%, 84.9%, 87.2% vs. 88.4% all p > 0.05). When independently significant AFs were used to upgrade the LR-4 nodules categorized by combined MFs and AFs (mLI-RADS b, d and f), the sensitivities were improved, but the specificities were decreased (all p < 0.05). CONCLUSIONS Independently significant AFs may be used to upgrade an observation from LR-4 (categorized only using MFs) to LR-5, which can improve diagnostic performance for small HCC.
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Affiliation(s)
- Rong Lyu
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, NO. 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Weijuan Hu
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, NO. 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Di Wang
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, NO. 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Jiao Wang
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, NO. 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Zhongsong Gao
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, NO. 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Kefeng Jia
- Department of Radiology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, NO. 83 Jintang Road, Hedong District, Tianjin, 300170, China.
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11
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Jhaveri KS, Babaei Jandaghi A, Bhayana R, Elbanna KY, Espin-Garcia O, Fischer SE, Ghanekar A, Sapisochin G. Prospective evaluation of Gadoxetate-enhanced magnetic resonance imaging and computed tomography for hepatocellular carcinoma detection and transplant eligibility assessment with explant histopathology correlation. Cancer Imaging 2023; 23:22. [PMID: 36841796 PMCID: PMC9960413 DOI: 10.1186/s40644-023-00532-3] [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: 10/04/2022] [Accepted: 02/08/2023] [Indexed: 02/27/2023] Open
Abstract
BACKGROUND We aimed to prospectively compare the diagnostic performance of gadoxetic acid-enhanced MRI (EOB-MRI) and contrast-enhanced Computed Tomography (CECT) for hepatocellular carcinoma (HCC) detection and liver transplant (LT) eligibility assessment in cirrhotic patients with explant histopathology correlation. METHODS In this prospective, single-institution ethics-approved study, 101 cirrhotic patients were enrolled consecutively from the pre-LT clinic with written informed consent. Patients underwent CECT and EOB-MRI alternately every 3 months until LT or study exclusion. Two blinded radiologists independently scored hepatic lesions on CECT and EOB-MRI utilizing the liver imaging reporting and data system (LI-RADS) version 2018. Liver explant histopathology was the reference standard. Pre-LT eligibility accuracies with EOB-MRI and CECT as per Milan criteria (MC) were assessed in reference to post-LT explant histopathology. Lesion-level and patient-level statistical analyses were performed. RESULTS Sixty patients (49 men; age 33-72 years) underwent LT successfully. One hundred four non-treated HCC and 42 viable HCC in previously treated HCC were identified at explant histopathology. For LR-4/5 category lesions, EOB-MRI had a higher pooled sensitivity (86.7% versus 75.3%, p < 0.001) but lower specificity (84.6% versus 100%, p < 0.001) compared to CECT. EOB-MRI had a sensitivity twice that of CECT (65.9% versus 32.2%, p < 0.001) when all HCC identified at explant histopathology were included in the analysis instead of imaging visible lesions only. Disregarding the hepatobiliary phase resulted in a significant drop in EOB-MRI performance (86.7 to 72.8%, p < 0.001). EOB-MRI had significantly lower pooled sensitivity and specificity versus CECT in the LR5 category with lesion size < 2 cm (50% versus 79%, p = 0.002 and 88.9% versus 100%, p = 0.002). EOB-MRI had higher sensitivity (84.8% versus 75%, p < 0.037) compared to CECT for detecting < 2 cm viable HCC in treated lesions. Accuracies of LT eligibility assessment were comparable between EOB-MRI (90-91.7%, p = 0.156) and CECT (90-95%, p = 0.158). CONCLUSION EOB-MRI had superior sensitivity for HCC detection; however, with lower specificity compared to CECT in LR4/5 category lesions while it was inferior to CECT in the LR5 category under 2 cm. The accuracy for LT eligibility assessment based on MC was not significantly different between EOB-MRI and CECT. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT03342677 , Registered: November 17, 2017.
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Affiliation(s)
- Kartik S. Jhaveri
- grid.17063.330000 0001 2157 2938Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, 610 University Ave, 3-957, Toronto, ON M5G 2M9 Canada
| | - Ali Babaei Jandaghi
- grid.231844.80000 0004 0474 0428Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, Toronto, ON M5G 1X6 Canada
| | - Rajesh Bhayana
- grid.17063.330000 0001 2157 2938Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON M5G 2M9 Canada
| | - Khaled Y. Elbanna
- grid.17063.330000 0001 2157 2938Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women’s College Hospital, University of Toronto, Toronto, ON M5G 2M9 Canada
| | - Osvaldo Espin-Garcia
- grid.415224.40000 0001 2150 066XDepartment of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1 Canada ,grid.17063.330000 0001 2157 2938Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Sandra E. Fischer
- grid.231844.80000 0004 0474 0428Department of Pathology, University Health Network and University of Toronto, Toronto, Ontario Canada
| | - Anand Ghanekar
- grid.17063.330000 0001 2157 2938University Health Network, Department of Surgery, Toronto General Hospital, University of Toronto, Toronto, ON M5G 2N2 Canada
| | - Gonzalo Sapisochin
- grid.17063.330000 0001 2157 2938University Health Network, Department of Surgery, Toronto General Hospital, University of Toronto, Toronto, ON M5G 2N2 Canada
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12
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Park S, Byun J, Hwang SM. Utilization of a Machine Learning Algorithm for the Application of Ancillary Features to LI-RADS Categories LR3 and LR4 on Gadoxetate Disodium-Enhanced MRI. Cancers (Basel) 2023; 15:cancers15051361. [PMID: 36900153 PMCID: PMC10000173 DOI: 10.3390/cancers15051361] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND This study aimed to identify the important ancillary features (AFs) and determine the utilization of a machine-learning-based strategy for applying AFs for LI-RADS LR3/4 observations on gadoxetate disodium-enhanced MRI. METHODS We retrospectively analyzed MRI features of LR3/4 determined with only major features. Uni- and multivariate analyses and random forest analysis were performed to identify AFs associated with HCC. A decision tree algorithm of applying AFs for LR3/4 was compared with other alternative strategies using McNemar's test. RESULTS We evaluated 246 observations from 165 patients. In multivariate analysis, restricted diffusion and mild-moderate T2 hyperintensity showed independent associations with HCC (odds ratios: 12.4 [p < 0.001] and 2.5 [p = 0.02]). In random forest analysis, restricted diffusion is the most important feature for HCC. Our decision tree algorithm showed higher AUC, sensitivity, and accuracy (0.84, 92.0%, and 84.5%) than the criteria of usage of restricted diffusion (0.78, 64.5%, and 76.4%; all p < 0.05); however, our decision tree algorithm showed lower specificity than the criterion of usage of restricted diffusion (71.1% vs. 91.3%; p < 0.001). CONCLUSION Our decision tree algorithm of applying AFs for LR3/4 shows significantly increased AUC, sensitivity, and accuracy but reduced specificity. These appear to be more appropriate in certain circumstances in which there is an emphasis on the early detection of HCC.
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Affiliation(s)
- Seongkeun Park
- Machine Intelligence Laboratory, Department of Smart Automobile, Soonchunhyang University, Asan 31538, Republic of Korea
| | - Jieun Byun
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul 07804, Republic of Korea
- Correspondence:
| | - Sook Min Hwang
- Department of Radiology, Hallym University College of Medicine, Kangnam Sacred Heart Hospital, Seoul 07441, Republic of Korea
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13
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Kim YY, Choi JY. [CT/MRI Liver Imaging Reporting and Data System (LI-RADS): Standardization, Evidence, and Future Direction]. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:15-33. [PMID: 36818714 PMCID: PMC9935963 DOI: 10.3348/jksr.2022.0144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/29/2022] [Accepted: 12/27/2022] [Indexed: 02/10/2023]
Abstract
The liver imaging reporting and data system (LI-RADS) has been developed with the support of the American College of Radiology to standardize the diagnosis and evaluation of treatment response of hepatocellular carcinoma (HCC). The CT/MRI LI-RADS version 2018 has been incorporated in the American Association for the Study of Liver Diseases guidance. This review examines the effect of CT/MRI LI-RADS on the standardized reporting of liver imaging, and the evidence in diagnosing HCC and evaluating treatment response after locoregional treatment using CT/MRI LI-RADS. The results are compared with other HCC diagnosis guidelines, and future directions are described.
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Affiliation(s)
- Yeun-Yoon Kim
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jin-Young Choi
- Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
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14
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Jiang H, Song B, Qin Y, Konanur M, Wu Y, McInnes MDF, Lafata KJ, Bashir MR. Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study. J Magn Reson Imaging 2022; 56:399-412. [PMID: 34994029 DOI: 10.1002/jmri.28056] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The Liver Imaging Reporting and Data System (LI-RADS) is widely used for diagnosing hepatocellular carcinoma (HCC), however, with unsatisfactory sensitivity, complex ancillary features, and inadequate integration with gadoxetate disodium (EOB)-enhanced MRI. PURPOSE To modify LI-RADS (mLI-RADS) on EOB-MRI. STUDY TYPE Secondary analysis of a prospective observational study. POPULATION Between July 2015 and September 2018, 224 consecutive high-risk patients (median age, 51 years; range, 26-83; 180 men; training/testing sets: 169/55 patients) with 742 (median size, 13 mm; interquartile range, 7-27; 498 HCCs) LR-3/4/5 observations. FIELD STRENGTH/SEQUENCE 3.0 T T2 -weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar, and 3D T1 -weighted gradient echo sequences. ASSESSMENT Three radiologists (with 5, 5, and 10 years of experience in liver MR imaging, respectively) blinded to the reference standard (histopathology or imaging follow-up) reviewed all MR images independently. In the training set, the optimal LI-RADS version 2018 (v2018) features selected by Random Forest analysis were used to develop mLI-RADS via decision tree analysis. STATISTICAL TESTS In an independent testing set, diagnostic performances of mLI-RADS, LI-RADS v2018, and the Korean Liver Cancer Association (KLCA) guidelines were computed using a generalized estimating equation model and compared with McNemar's test. A two-tailed P < 0.05 was statistically significant. RESULTS Five features (nonperipheral "washout," restricted diffusion, nonrim arterial phase hyperenhancement [APHE], mild-moderate T2 hyperintensity, and transitional phase hypointensity) constituted mLI-RADS, and mLR-5 was nonperipheral washout coupled with either nonrim APHE or restricted diffusion. In the testing set, mLI-RADS was significantly more sensitive (72%) and accurate (80%) than LI-RADS v2018 (sensitivity, 61%; accuracy 74%; both P < 0.001) and the KLCA guidelines (sensitivity, 64%; accuracy 74%; both P < 0.001), without sacrificing positive predictive value (mLI-RADS, 94%; LI-RADS v2018, 94%; KLCA guidelines, 92%). DATA CONCLUSION In high-risk patients, the EOB-MRI-based mLI-RADS was simpler and more sensitive for HCC than LI-RADS v2018 while maintaining high positive predictive value. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Meghana Konanur
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Yuanan Wu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Matthew D F McInnes
- Departments of Radiology and Epidemiology, University of Ottawa, Ottawa, Ontario, Canada
- Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Kyle J Lafata
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Center for Advanced Magnetic Resonance in Medicine, Duke University Medical Center, Durham, North Carolina, USA
- Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
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15
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De Muzio F, Grassi F, Dell’Aversana F, Fusco R, Danti G, Flammia F, Chiti G, Valeri T, Agostini A, Palumbo P, Bruno F, Cutolo C, Grassi R, Simonetti I, Giovagnoni A, Miele V, Barile A, Granata V. A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls. Diagnostics (Basel) 2022; 12:1655. [PMID: 35885561 PMCID: PMC9319674 DOI: 10.3390/diagnostics12071655] [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: 06/07/2022] [Revised: 06/27/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Liver cancer is the sixth most detected tumor and the third leading cause of tumor death worldwide. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with specific risk factors and a targeted population. Imaging plays a major role in the management of HCC from screening to post-therapy follow-up. In order to optimize the diagnostic-therapeutic management and using a universal report, which allows more effective communication among the multidisciplinary team, several classification systems have been proposed over time, and LI-RADS is the most utilized. Currently, LI-RADS comprises four algorithms addressing screening and surveillance, diagnosis on computed tomography (CT)/magnetic resonance imaging (MRI), diagnosis on contrast-enhanced ultrasound (CEUS) and treatment response on CT/MRI. The algorithm allows guiding the radiologist through a stepwise process of assigning a category to a liver observation, recognizing both major and ancillary features. This process allows for characterizing liver lesions and assessing treatment. In this review, we highlighted both major and ancillary features that could define HCC. The distinctive dynamic vascular pattern of arterial hyperenhancement followed by washout in the portal-venous phase is the key hallmark of HCC, with a specificity value close to 100%. However, the sensitivity value of these combined criteria is inadequate. Recent evidence has proven that liver-specific contrast could be an important tool not only in increasing sensitivity but also in diagnosis as a major criterion. Although LI-RADS emerges as an essential instrument to support the management of liver tumors, still many improvements are needed to overcome the current limitations. In particular, features that may clearly distinguish HCC from cholangiocarcinoma (CCA) and combined HCC-CCA lesions and the assessment after locoregional radiation-based therapy are still fields of research.
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Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy;
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 81100 Naples, Italy; (F.G.); (F.D.); (R.G.)
| | - Federica Dell’Aversana
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 81100 Naples, Italy; (F.G.); (F.D.); (R.G.)
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Ginevra Danti
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy; (G.D.); (F.F.); (G.C.); (V.M.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Federica Flammia
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy; (G.D.); (F.F.); (G.C.); (V.M.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Giuditta Chiti
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy; (G.D.); (F.F.); (G.C.); (V.M.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Tommaso Valeri
- Department of Clinical Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (T.V.); (A.A.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, Via Tronto 10/a, 60126 Torrette, Italy
| | - Andrea Agostini
- Department of Clinical Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (T.V.); (A.A.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, Via Tronto 10/a, 60126 Torrette, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
- Area of Cardiovascular and Interventional Imaging, Department of Diagnostic Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
- Emergency Radiology, San Salvatore Hospital, Via Lorenzo Natali 1, 67100 L’Aquila, Italy;
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Fisciano, Italy;
| | - Roberta Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 81100 Naples, Italy; (F.G.); (F.D.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Igino Simonetti
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (I.S.); (V.G.)
| | - Andrea Giovagnoni
- Department of Clinical Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy; (T.V.); (A.A.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, Via Tronto 10/a, 60126 Torrette, Italy
| | - Vittorio Miele
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy; (G.D.); (F.F.); (G.C.); (V.M.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (P.P.); (F.B.)
| | - Antonio Barile
- Emergency Radiology, San Salvatore Hospital, Via Lorenzo Natali 1, 67100 L’Aquila, Italy;
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Via Mariano Semmola, 80131 Naples, Italy; (I.S.); (V.G.)
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16
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Xie Z, Peng Z, Zou Y, Xiao H, Li B, Zhou Q, Chen S, Xu L, Shen J, Mo Y, Peng S, Kuang M, Long J, Feng ST. Non-invasive diagnosis strategy of hepatocellular carcinoma in low-risk population. BMC Cancer 2022; 22:709. [PMID: 35761201 PMCID: PMC9238050 DOI: 10.1186/s12885-022-09812-w] [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: 01/14/2022] [Accepted: 06/20/2022] [Indexed: 11/25/2022] Open
Abstract
Aims With prevalence of hepatocellular carcinoma (HCC) in low-risk population (LRP), establishing a non-invasive diagnostic strategy becomes increasingly urgent to spare unnecessary biopsies in this population. The purposes of this study were to find characterisics of HCC and to establish a proper non-invasive method to diagnose HCC in LRP. Methods A total of 681 patients in LRP (defined as the population without cirrhosis, chronic HBV infection or HCC history) were collected from 2 institutions. The images of computed tomography (CT) and magnetic resonance imaging (MRI) were manually analysed. We divided the patients into the training cohort (n = 324) and the internal validating cohort (n = 139) by admission time in the first institution. The cohort in the second institution was viewed as the external validation (n = 218). A multivariate logistic regression model incorporating both imaging and clinical independent risk predictors was developed. C-statistics was used to evaluate the diagnostic performance. Results Besides the major imaging features of HCC (non-rim enhancement, washout and enhancing capsule), tumor necrosis or severe ischemia (TNSI) on imaging and two clinical characteristics (gender and alpha fetoprotein) were also independently associated with HCC diagnosis (all P < 0.01). A clinical model (including 3 major features, TNSI, gender and AFP) was built to diagnose HCC and achieved good diagnostic performance (area under curve values were 0.954 in the training cohort, 0.931 in the internal validation cohort and 0.902 in the external cohort). Conclusions The clinical model in this study developed a satisfied non-invasive diagnostic performance for HCC in LRP. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09812-w.
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Affiliation(s)
- Zonglin Xie
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Zhenpeng Peng
- Department of Radiology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu Distinct, 58 Zhongshan Road 2, Guangzhou, 500018, China
| | - Yujian Zou
- Department of Radiology, The Affiliated Dongguan Hospital, Southern Medical University, Dongguan, China
| | - Han Xiao
- Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Bin Li
- Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Qian Zhou
- Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Shuling Chen
- Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Lixia Xu
- Department of Oncology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu Distinct, 58 Zhongshan Road 2, Guangzhou, 500018, China
| | - Jingxian Shen
- Department of Medical Imaging, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yunxian Mo
- Department of Medical Imaging, Sun Yat-Sen University Cancer CenterState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Sui Peng
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.,Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming Kuang
- Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.,Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Jianting Long
- Department of Oncology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu Distinct, 58 Zhongshan Road 2, Guangzhou, 500018, China.
| | - Shi-Ting Feng
- Department of Radiology, the First Affiliated Hospital, Sun Yat-Sen University, Yuexiu Distinct, 58 Zhongshan Road 2, Guangzhou, 500018, China.
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17
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Do transition and hepatobiliary phase hypointensity improve LI-RADS categorization as an alternative washout: a systematic review and meta-analysis. Eur Radiol 2022; 32:5134-5143. [PMID: 35267090 DOI: 10.1007/s00330-022-08665-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/30/2021] [Accepted: 02/13/2022] [Indexed: 02/06/2023]
Abstract
OBJECTIVE The definition of washout in gadoxetate disodium-enhanced MRI (Gd-EOB-MRI) is controversial. The current Liver Imaging Reporting and Data System (LI-RADS) defines washout only in the portal venous phase on Gd-EOB-MRI, leading to low diagnostic sensitivity for HCC. We performed a meta-analysis to compare the diagnostic performance of Gd-EOB-MRI using conventional (cWO) and modified (mWO) definitions of washout. METHODS The PubMed and EMBASE databases were searched to identify studies published between January 1, 2010, and August 1, 2021, that compared the diagnostic performance of cWO and mWO for HCC. The mWOs added transition phase (TP) hypointensity (mWO-1), hepatobiliary phase (HBP) hypointensity (mWO-2), or both (mWO-3). The pooled sensitivity and specificity were calculated using a bivariate random-effects model. Study heterogeneity was explored by subgroup analysis and meta-regression analysis. RESULTS Ten comparative studies with 2391 patients were included. Compared to cWO, the overall mWO yielded significantly higher sensitivity (71% vs. 81%, p = 0.00) and lower specificity (97% vs. 93%, p = 0.01) for diagnosing HCC. The area under the curve (AUC) was 0.90 and 0.94 for the cWO and mWO, respectively. Regarding the three types of mWOs, mWO-2 showed the highest sensitivity (85%) and specificity (96%) for diagnosing HCC. mWO-2 achieved the highest AUC (0.97), followed by mWO-1 (0.90), and mWO-3 (0.89). Average reviewer experience and scanner field strength were significantly associated with study heterogeneity (p < 0.05). CONCLUSIONS Inclusion of TP and HBP hypointensity in the definition of washout improved the sensitivity with slightly lower specificity for diagnosing HCC in LI-RADS. KEY POINTS • Compared to the conventional definition of washout, studies using a modified definition had higher sensitivity (71% vs. 81%) but lower specificity (97% vs. 93%) in LI-RADS for the diagnosis of HCC. • Hepatobiliary phase hypointensity may be a preferred alternative washout for HCC diagnosis with the highest area under the curve. • Studies with experienced reviewer or 3.0T MRI showed higher sensitivity and lower specificity for diagnosing HCC when using modified washout (p < 0.05).
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18
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Xie S, Zhang Y, Chen J, Jiang T, Liu W, Rong D, Sun L, Zhang L, He B, Wang J. Can modified LI-RADS increase the sensitivity of LI-RADS v2018 for the diagnosis of 10-19 mm hepatocellular carcinoma on gadoxetic acid-enhanced MRI? Abdom Radiol (NY) 2022; 47:596-607. [PMID: 34773467 DOI: 10.1007/s00261-021-03339-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/29/2021] [Accepted: 10/29/2021] [Indexed: 12/11/2022]
Abstract
PURPOSE To evaluate whether the Liver Imaging Reporting and Data System (LI-RADS) v2018 LR-5 criteria can be modified to increase sensitivity without reducing specificity for diagnosing 10-19 mm hepatocellular carcinoma (HCC) on gadoxetic acid-enhanced magnetic resonance imaging (MRI). METHODS A total of 133 high-risk consecutive patients with 174 small observations (10-19 mm) detected on gadoxetic acid-enhanced MRI were retrospectively studied. LI-RADS MRI major features (MFs) and ancillary features (AFs) were reviewed by two independent radiologists in consensus. Observations were categorized using LI-RADS v2018 MFs. Independently significant AFs were identified through logistic regression analysis. Upgraded LR-5 criteria were developed by combining independently significant AFs with MFs of LR-3 or LR-4 v2018. The sensitivity and specificity of the new diagnostic criteria were compared with those of LR-5 v2018 using McNemar's test. RESULTS Three of the AFs favoring malignancy [mild-moderate T2 hyperintensity, transitional phase (TP) hypointensity and fat in mass] were independently significant features for diagnosing 10-19 mm HCC. The upgraded LR-5 criteria (mLI-RADS VII: LR-4 + mild-moderate T2 hyperintensity/TP hypointensity or LR-3 + fat in mass) yielded a significantly greater sensitivity than that of the LR-5 v2018 criteria (70.4% vs 55.1%; p < 0.001), whereas the specificity was not significantly different (94.7% vs 98.7%, p = 0.250). CONCLUSIONS Independently significant AFs may be used to upgrade an observation from LR-3/LR-4 to LR-5, which can improve the sensitivity without impairing the specificity for diagnosing 10-19 mm HCC on gadoxetic acid-enhanced MRI.
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Affiliation(s)
- Sidong Xie
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Yao Zhang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Jingbiao Chen
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Ting Jiang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Weimin Liu
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Dailin Rong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Lin Sun
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Linqi Zhang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Bingjun He
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China
| | - Jin Wang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University (SYSU), No 600, Tianhe Road, Guangzhou, 510630, Guangdong, People's Republic of China.
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19
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Jiang H, Song B, Qin Y, Wei Y, Konanur M, Wu Y, Zaki IH, McInnes MDF, Lafata KJ, Bashir MR. Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity. J Magn Reson Imaging 2022; 55:493-506. [PMID: 34236120 DOI: 10.1002/jmri.27824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/23/2021] [Accepted: 06/23/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The Liver Imaging Reporting and Data System (LI-RADS) is widely accepted as a reliable diagnostic scheme for hepatocellular carcinoma (HCC) in at-risk patients. However, its application is hampered by substantial complexity and suboptimal diagnostic sensitivity. PURPOSE To propose data-driven modifications to the LI-RADS version 2018 (v2018) major feature system (rLI-RADS) on gadoxetate disodium (EOB)-enhanced magnetic resonance imaging (MRI) to improve sensitivity and simplicity while maintaining high positive predictive value (PPV) for detecting HCC. STUDY TYPE Retrospective. POPULATION Two hundred and twenty-four consecutive at-risk patients (training dataset: 169, independent testing dataset: 55) with 742 LR-3 to LR-5 liver observations (HCC: N = 498 [67%]) were analyzed from a prospective observational registry collected between July 2015 and September 2018. FIELD STRENGTH/SEQUENCE 3.0 T/T2-weighted fast spin-echo, diffusion-weighted spin-echo based echo-planar and three-dimensional (3D) T1-weighted gradient echo sequences. ASSESSMENT All images were evaluated by three independent abdominal radiologists who were blinded to all clinical, pathological, and follow-up information. Composite reference standards of either histopathology or imaging follow-up were used. STATISTICAL TESTS In the training dataset, LI-RADS v2018 major features were used to develop rLI-RADS based on their associated PPV for HCC. In an independent testing set, diagnostic performances of LI-RADS v2018 and rLI-RADS were computed using a generalized estimating equation model and compared with McNemar's test. A P value <0.05 was considered statistically significant. RESULTS The median (interquartile range) size of liver observations was 13 mm (7-27 mm). The diagnostic table for rLI-RADS encompassed 9 cells, as opposed to 16 cells for LI-RADS v2018. In the testing set, compared to LI-RADS v2018, rLI-RADS category 5 demonstrated a significantly superior sensitivity (76% vs. 61%) while maintaining comparably high PPV (92.5% vs. 94.1%, P = 0.126). DATA CONCLUSION Compared with LI-RADS v2018, rLI-RADS demonstrated improved simplicity and significantly superior diagnostic sensitivity for HCC in at-risk patients. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Qin
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Meghana Konanur
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Yuanan Wu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Islam H Zaki
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Matthew D F McInnes
- Departments of Radiology and Epidemiology, University of Ottawa, Ottawa, Ontario, Canada
- Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Kyle J Lafata
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Radiation Oncology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Center for Advanced Magnetic Resonance in Medicine, Duke University Medical Center, Durham, North Carolina, USA
- Division of Gastroenterology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA
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20
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Park J, Lee JM, Kim TH, Yoon JH. Imaging Diagnosis of HCC: Future directions with special emphasis on hepatobiliary MRI and contrast-enhanced ultrasound. Clin Mol Hepatol 2021; 28:362-379. [PMID: 34955003 PMCID: PMC9293611 DOI: 10.3350/cmh.2021.0361] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a unique cancer entity that can be noninvasively diagnosed using imaging modalities without pathologic confirmation. In 2018, several major guidelines for HCC were updated to include hepatobiliary contrast agent magnetic resonance imaging (HBA-MRI) and contrast-enhanced ultrasound (CEUS) as major imaging modalities for HCC diagnosis. HBA-MRI enables the achievement of high sensitivity in HCC detection using the hepatobiliary phase (HBP). CEUS is another imaging modality with real-time imaging capability, and it is reported to be useful as a second-line modality to increase sensitivity without losing specificity for HCC diagnosis. However, until now, there is an unsolved discrepancy among guidelines on whether to accept “HBP hypointensity” as a definite diagnostic criterion for HCC or include CEUS in the diagnostic algorithm for HCC diagnosis. Furthermore, there is variability in terminology and inconsistencies in the definition of imaging findings among guidelines; therefore, there is an unmet need for the development of a standardized lexicon. In this article, we review the performance and limitations of HBA-MRI and CEUS after guideline updates in 2018 and briefly introduce some future aspects of imaging-based HCC diagnosis.
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Affiliation(s)
- Junghoan Park
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Tae-Hyung Kim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Jeong Hee Yoon
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
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21
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Jeon SK, Joo I, Bae JS, Park SJ, Lee JM. LI-RADS v2018: how to appropriately use ancillary features in category adjustment from intermediate probability of malignancy (LR-3) to probably HCC (LR-4) on gadoxetic acid-enhanced MRI. Eur Radiol 2021; 32:46-55. [PMID: 34132875 DOI: 10.1007/s00330-021-08116-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/22/2021] [Accepted: 06/01/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVES To determine the appropriate use of ancillary features (AFs) in upgrading LI-RADS category 3 (LR-3) to category 4 (LR-4) for hepatic nodules on gadoxetic acid-enhanced MRI. METHODS We retrospectively analyzed MRI features of solid hepatic nodules (≤ 30 mm) categorized as LR-3/4 on gadoxetic acid-enhanced MRI. In LI-RADS diagnostic table-based-LR-3 observations, logistic regression analyses were performed to identify AFs suggestive of hepatocellular carcinomas (HCCs) rather than non-malignant nodules. Using McNemar's test, the sensitivities and specificities of the final-LR-4 category for HCC diagnosis were compared according to the principles of AF application in category adjustment. RESULTS A total of 336 hepatic nodules (191 HCCs; 145 non-malignant) in 252 patients were evaluated. Based on major HCC features, 248 nodules (123 HCCs) were assigned as table-based-LR-3 and 88 nodules (68 HCCs) as table-based-LR-4. In table-based-LR-3 observations, mild-moderate T2 hyperintensity was identified as an independent predictor of HCC as opposed to non-malignant nodules (odds ratio = 3.01, p = 0.002). For HCC diagnosis, different criteria of final-LR-4: only table-based-LR-4, allowing category upgrade using only T2 hyperintensity, or using any AFs favoring malignancy resulted in sensitivities of 35.6% (68/191), 53.9% (103/191), and 88.5% (169/191), and specificities of 86.2% (125/145), 75.9% (110/145), and 21.4% (31/145), respectively, which differed from each other (all p < 0.001). CONCLUSIONS While the application of MRI AF in LI-RADS category adjustment increases the sensitivity of LR-4 category for HCC diagnosis, it is accompanied by a significant decrease in specificity. Mild-moderate T2 hyperintensity, a significant AF indicative of HCC, may be more appropriate for upgrading LR-3 to LR-4. KEY POINTS • When upgrading from LR-3 to LR-4 using any MRI ancillary features favoring malignancy, LR-4 sensitivity increases but specificity decreased for HCC diagnosis. • By upgrading LR-3 to LR-4 based on MRI ancillary features found to suggest HCC rather than non-malignant nodules in multivariate analysis (i.e., mild-moderate T2 hyperintensity), LR-4 demonstrated a more balanced sensitivity and specificity for HCC diagnosis (53.9% and 75.9%, respectively).
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Affiliation(s)
- Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.,Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. .,Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
| | - Jae Seok Bae
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.,Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Sae-Jin Park
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.,Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.,Department of Radiology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.,Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
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