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Suhail Najm Alareer H, Arian A, Fotouhi M, Taher HJ, Dinar Abdullah A. Evidence Supporting Diagnostic Value of Liver Imaging Reporting and Data System for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis. J Biomed Phys Eng 2024; 14:5-20. [PMID: 38357604 PMCID: PMC10862115 DOI: 10.31661/jbpe.v0i0.2211-1562] [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: 11/08/2022] [Accepted: 03/12/2023] [Indexed: 02/16/2024]
Abstract
Background Based on the Liver Imaging Data and Reporting System (LI-RADS) guidelines, Hepatocellular Carcinoma (HCC) can be diagnosed using imaging criteria in patients at risk of HCC. Objective This study aimed to assess the diagnostic value of LI-RADS in high-risk patients with HCC. Material and Methods This systematic review is conducted on international databases, including Google Scholar, Web of Science, PubMed, Embase, PROQUEST, and Cochrane Library, with appropriate keywords. Using the binomial distribution formula, the variance of each study was calculated, and all the data were analyzed using STATA version 16. The pooled sensitivity and specificity were determined using a random-effects meta-analysis approach. Also, we used the chi-squared test and I2 index to calculate heterogeneity among studies, and Funnel plots and Egger tests were used for evaluating publication bias. Results The pooled sensitivity was estimated at 0.80 (95% CI 0.76-0.84). According to different types of Liver Imaging Reporting and Data Systems (LI-RADS), the highest pooled sensitivity was in version 2018 (0.83 (95% CI 0.79-0.87) (I2: 80.6%, P of chi 2 test for heterogeneity <0.001 and T2: 0.001). The pooled specificity was estimated as 0.89 (95% CI 0.87-0.92). According to different types of LI-RADS, the highest pooled specificity was in version 2014 (93.0 (95% CI 89.0-96.0) (I2: 81.7%, P of chi 2 test for heterogeneity <0.001 and T2: 0.001). Conclusion LI-RADS can assist radiologists in achieving the required sensitivity and specificity in high-risk patients suspected to have HCC. Therefore, this strategy can serve as an appropriate tool for identifying HCC.
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Affiliation(s)
- Hayder Suhail Najm Alareer
- Department of Radiology, College of Health and Medical Technology, Al-Ayen University, Thi-Qar, 64001, Iraq
| | - Arvin Arian
- Cancer Institute ADIR, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Maryam Fotouhi
- Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Centre for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | | | - Ayoob Dinar Abdullah
- Department of Radiology Technology, Al-Manara College for Medical Sciences, Missan, Iraq
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Wang D, Zhang L, Sun Z, Jiang H, Zhang J. A radiomics signature associated with underlying gene expression pattern for the prediction of prognosis and treatment response in hepatocellular carcinoma. Eur J Radiol 2023; 167:111086. [PMID: 37708675 DOI: 10.1016/j.ejrad.2023.111086] [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] [Received: 06/12/2023] [Revised: 08/13/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE Identifying robust prognosis and treatment efficiency predictive biomarkers of hepatocellular carcinoma (HCC) is challenging. The purpose of this study is to develop a radiomics approach for predicting the overall survival (OS) based on pretreatment CT images and to explore the radiomic-associated key genes. METHODS Patients with pathologically or clinically proven HCC from three data sets were retrospectively included in this study. The institute internal data that received transarterial chemoembolization (TACE) treatment was used as the training set to construct the radiomics signature to predict OS by the least absolute shrinkage and selection operator COX (LASSO-COX) regression algorithms. The model was externally tested in 41 patients from The Cancer Genome Atlas (TCGA) with available CT images. Area under the receiver operating characteristics curve (AUC) and the log-rank test were used for survival analysis based on high versus low radiomics score. RNA sequencing data of TCGA and Gene Expression Omnibus (GEO) public database were used for gene expression analysis. RESULTS A total of 752 patients were divided into the Radiomics cohort (n = 267), the TCGA cohort (n = 338) and GEO cohort (n = 147). The rad-score divided patients into high and low risk groups, with significant survival differences (P < 0.0001 and P = 0.0055) in the training and external test set. The AUC for 5 years' OS were 0.730 and 0.695, respectively. Seven OS-related genes (SPP1, GJA5, GJA4, INMT, PDZD4, ALDOA and MAFG) were identified, all of which were related with TACE efficiency, except for MAFG (P greater than 0.05). CONCLUSIONS CT-radiomics signature could effectively predict the prognosis and treatment response of HCC, which were also associated with the tumor microenvironment heterogeneity.
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Affiliation(s)
- Dandan Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Linhan Zhang
- Department of PET/CT, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhongqi Sun
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
| | - Jinfeng Zhang
- Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
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Wei H, Yang T, Chen J, Duan T, Jiang H, Song B. Prognostic implications of CT/MRI LI-RADS in hepatocellular carcinoma: State of the art and future directions. Liver Int 2022; 42:2131-2144. [PMID: 35808845 DOI: 10.1111/liv.15362] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 06/13/2022] [Accepted: 07/05/2022] [Indexed: 02/05/2023]
Abstract
Hepatocellular carcinoma (HCC) is the fourth most lethal malignancy with an increasing incidence worldwide. Management of HCC has followed several clinical staging systems that rely on tumour morphologic characteristics and clinical variables. However, these algorithms are unlikely to profile the full landscape of tumour aggressiveness and allow accurate prognosis stratification. Noninvasive imaging biomarkers on computed tomography (CT) or magnetic resonance imaging (MRI) exhibit a promising prospect to refine the prognostication of HCC. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, techniques, interpretation, reporting and data collection of liver imaging. At present, it has been widely accepted as an effective diagnostic system for HCC in at-risk patients. Emerging data have provided new insights into the potential of CT/MRI LI-RADS in HCC prognostication, which may help refine the prognostic paradigm of HCC that promises to direct individualized management and improve patient outcomes. Therefore, this review aims to summarize several prognostic imaging features at CT/MRI for patients with HCC; the available evidence regarding the use of LI-RDAS for evaluation of tumour biology and clinical outcomes, pitfalls of current literature, and future directions for LI-RADS in the management of HCC.
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Affiliation(s)
- Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Ting Duan
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.,Department of Radiology, Sanya People's Hospital, Sanya, China
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Borhani AA, Catania R, Velichko YS, Hectors S, Taouli B, Lewis S. Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response. Abdom Radiol (NY) 2021; 46:3674-3685. [PMID: 33891149 DOI: 10.1007/s00261-021-03085-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/02/2021] [Accepted: 04/09/2021] [Indexed: 12/13/2022]
Abstract
Radiomics refers to the process of conversion of conventional medical images into quantifiable data ("features") which can be further mined to reveal complex patterns and relationships between the voxels in the image. These high throughput features can potentially reflect the histology of biologic tissues at macroscopic and microscopic levels. Several studies have investigated radiomics of hepatocellular carcinoma (HCC) before and after treatment. HCC is a heterogeneous disease with diverse phenotypical and genotypical landscape. Due to this inherent heterogeneity, HCC lesions can manifest variable aggressiveness with different response to treatment options, including the newer targeted therapies. Hence, radiomics can be used as a potential tool to enable patient selection for therapies and to predict response to treatments and outcome. Additionally, radiomics may serve as a tool for earlier and more efficient assessment of response to treatment. Radiomics, radiogenomics, and radio-immunoprofiling and their potential roles in management of patients with HCC will be discussed and critically reviewed in this article.
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Affiliation(s)
- Amir A Borhani
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Suite 800, Chicago, IL, 60611, USA.
| | - Roberta Catania
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Suite 800, Chicago, IL, 60611, USA
| | - Yuri S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, 676 N. Saint Clair Street, Suite 800, Chicago, IL, 60611, USA
| | - Stefanie Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine At Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine At Mount Sinai, 1Gustave L. Levy Place, New York, NY, 1470, 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, 1Gustave L. Levy Place, New York, NY, 1470, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine At Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine At Mount Sinai, 1Gustave L. Levy Place, New York, NY, 1470, USA
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Abstract
The diagnosis of hepatocellular carcinoma relies largely on non-invasive imaging, and is well suited for radiomics analysis. Radiomics is an emerging method for quantification of tumor heterogeneity by mathematically analyzing the spatial distribution and relationships of gray levels in medical images. The published studies on radiomics analysis of HCC provide encouraging data demonstrating potential utility for prediction of tumor biology, molecular profiles, post-therapy response, and outcome. The combination of radiomics data and clinical/laboratory information provides added value in many studies. Radiomics is a multi-step process that requires optimization and standardization, the development of semi-automated or automated segmentation methods, robust data quality control, and refinement of algorithms and modeling approaches for high-throughput data analysis. While radiomics remains largely in the research setting, the strong associations of predictive models and nomograms with certain pathologic, molecular, and immune markers with tumor aggressiveness and patient outcomes, provide great potential for clinical applications to inform optimized treatment strategies and patient prognosis.
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Interobserver Agreement of Magnetic Resonance Imaging of Liver Imaging Reporting and Data System Version 2018. J Comput Assist Tomogr 2020; 44:118-123. [PMID: 31939892 DOI: 10.1097/rct.0000000000000945] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
AIM This study aimed to assess the interobserver agreement of magnetic resonance (MR) imaging of Liver Imaging Reporting and Data System version 2018 (LI-RADS v2018). SUBJECTS AND METHODS Retrospective analysis was done for 119 consecutive patients (77 male and 42 female) at risk of hepatocellular carcinoma who underwent dynamic contrast MR imaging. Image analysis was done by 2 independent and blinded readers for arterial phase hyperenhancement, washout appearance, enhancing capsule appearance, and size. Hepatic lesions were classified into 7 groups according to LI-RADS v2018. RESULTS There was excellent interobserver agreement of both reviewers for LR version 4 (κ = 0.887, P = 0.001) with 90.76% agreement. There was excellent interobserver agreement for nonrim arterial phase hyperenhancement (κ = 0.948; 95% confidence interval [CI], 0.89-0.99; P = 0.001), washout appearance (κ = 0.949; 95% CI, 0.89-1.0; P = 0.001); and enhancing capsule (κ = 0.848; 95% CI, 0.73-0.97; P = 0.001) and excellent reliability of size (interclass correlation, 0.99; P = 0.001). There was excellent interobserver agreement for LR-1 (κ = 1.00, P = 0.001), LR-2 (κ = 0.94, P = 0.001), LR-5 (κ = 0.839, P = 0.001), LR-M (κ = 1.00, P = 0.001), and LR-TIV (κ = 1.00; 95% CI, 1.0-1.0; P = 0.001), and good agreement for LR-3 (κ = 0.61, P = 0.001) and LR-4 (κ = 0.61, P = 0.001). CONCLUSION MR imaging of LI-RADS v2018 is a reliable imaging modality and reporting system that may be used for standard interpretation of hepatic focal lesions.
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Hepatocellular carcinoma (HCC) versus non-HCC: accuracy and reliability of Liver Imaging Reporting and Data System v2018. Abdom Radiol (NY) 2019; 44:2116-2132. [PMID: 30798397 DOI: 10.1007/s00261-019-01948-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE The Liver Imaging Reporting and Data System (LI-RADS) was created to standardize the diagnostic criteria for hepatocellular carcinoma (HCC) and has undergone multiple revisions including a recent update in 2018 (v2018). The primary aim of this study was to determine the diagnostic performance and interrater reliability (IRR) of LI-RADS v2018 for distinguishing HCC from non-HCC primary hepatic malignancy in patients 'at-risk' for HCC. A secondary aim was to assess the impact of changes introduced in the v2018 diagnostic algorithm. METHODS This retrospective study combined a 10-year experience of pathologically proven primary liver malignancies from two large liver transplant centers. Two blinded readers independently evaluated each lesion and assigned a LI-RADS diagnostic category, additionally scoring all relevant imaging features. Changes in category based on the reader-provided features and the new v2018 criteria were assessed by a study coordinator. RESULTS The final study cohort comprised 105 HCCs and 73 non-HCC primarily liver malignancies. LI-RADS had a high specificity for distinguishing HCC from non-HCC (89% and 90% for reader 1 and reader 2, respectively), and IRR was moderate to substantial for final LI-RADS category and most features. Revision of the LI-RADS v2018 diagnostic algorithm resulted in very few changes [5 (2.8%) and 3 (1.7%) for reader 1 and reader 2, respectively] in overall lesion classification. CONCLUSION LI-RADS diagnostic categories and features had moderate to substantial IRR and high specificity for distinguishing HCC from non-HCC primary liver malignancy. Revision of LI-RADS v2018 diagnostic algorithm resulted in reclassification of very few lesions.
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Krishan S, Dhiman RK, Kalra N, Sharma R, Baijal SS, Arora A, Gulati A, Eapan A, Verma A, Keshava S, Mukund A, Deva S, Chaudhary R, Ganesan K, Taneja S, Gorsi U, Gamanagatti S, Madhusudan KS, Puri P, Shalimar, Govil S, Wadhavan M, Saigal S, Kumar A, Thapar S, Duseja A, Saraf N, Khandelwal A, Mukhopadyay S, Gulati A, Shetty N, Verma N. Joint Consensus Statement of the Indian National Association for Study of the Liver and Indian Radiological and Imaging Association for the Diagnosis and Imaging of Hepatocellular Carcinoma Incorporating Liver Imaging Reporting and Data System. J Clin Exp Hepatol 2019; 9:625-651. [PMID: 31695253 PMCID: PMC6823668 DOI: 10.1016/j.jceh.2019.07.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 07/12/2019] [Indexed: 02/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the 6th most common cancer and the second most common cause of cancer-related mortality worldwide. There are currently no universally accepted practice guidelines for the diagnosis of HCC on imaging owing to the regional differences in epidemiology, target population, diagnostic imaging modalities, and staging and transplant eligibility. Currently available regional and national guidelines include those from the American Association for the Study of Liver Disease (AASLD), the European Association for the Study of the Liver (EASL), the Asian Pacific Association for the Study of the Liver, the Japan Society of Hepatology, the Korean Liver Cancer Study Group, Hong Kong, and the National Comprehensive Cancer Network in the United States. India with its large population and a diverse health infrastructure faces challenges unique to its population in diagnosing HCC. Recently, American Association have introduced a Liver Imaging Reporting and Data System (LIRADS, version 2017, 2018) as an attempt to standardize the acquisition, interpretation, and reporting of liver lesions on imaging and hence improve the coherence between radiologists and clinicians and provide guidance for the management of HCC. The aim of the present consensus was to find a common ground in reporting and interpreting liver lesions pertaining to HCC on imaging keeping LIRADSv2018 in mind.
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Affiliation(s)
- Sonal Krishan
- Department of Radiology, Medanta Hospital, Gurgaon, India
| | - Radha K. Dhiman
- Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India,Address for correspondence: Radha Krishan Dhiman, MD, DM, FACG, FRCP, FAASLD, Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
| | - Navin Kalra
- Department of Radiology, Postgraduate Institute Of Medical Education and Research, Chandigarh, India
| | - Raju Sharma
- Department of Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Sanjay S. Baijal
- Department of Diagnostic and Intervention Radiology, Medanta Hospital, Gurgaon, India
| | - Anil Arora
- Institute Of Liver Gastroenterology & Pancreatico Biliary Sciences, Sir Gangaram Hospital, New Delhi, India
| | - Ajay Gulati
- Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Anu Eapan
- Department of Radiology, Christian Medical College, Vellore, India
| | - Ashish Verma
- Department of Radiology, Banaras Hindu University, Varanasi, India
| | - Shyam Keshava
- Department of Radiology, Christian Medical College, Vellore, India
| | - Amar Mukund
- Department of Intervention Radiology, Institute of liver and biliary Sciences, New Delhi, India
| | - S. Deva
- Department of Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Ravi Chaudhary
- Department of Radiology, Medanta Hospital, Gurgaon, India
| | | | - Sunil Taneja
- Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Ujjwal Gorsi
- Department of Radiology, Postgraduate Institute Of Medical Education and Research, Chandigarh, India
| | | | - Kumble S. Madhusudan
- Department of Radiology, All India Institute of Medical Sciences, New Delhi, India
| | - Pankaj Puri
- Institute Of Liver Gastroenterology & Pancreatico Biliary Sciences, Sir Gangaram Hospital, New Delhi, India
| | - Shalimar
- Department of GastroEnterology, All India Institute of Medical Sciences, New Delhi, India
| | | | - Manav Wadhavan
- Institute of Digestive and Liver Diseases, BLK Hospital, Delhi, India
| | - Sanjiv Saigal
- Department of Hepatology, Medanta Hospital, Gurgaon, India
| | - Ashish Kumar
- Institute Of Liver Gastroenterology & Pancreatico Biliary Sciences, Sir Gangaram Hospital, New Delhi, India
| | - Shallini Thapar
- Department of Radiology, Institute of liver and biliary Sciences, New Delhi, India
| | - Ajay Duseja
- Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Neeraj Saraf
- Department of Hepatology, Medanta Hospital, Gurgaon, India
| | | | | | - Ajay Gulati
- Department of Radiology, Postgraduate Institute Of Medical Education and Research, Chandigarh, India
| | - Nitin Shetty
- Department of Radiology, Tata Memorial Hospital, Kolkata, India
| | - Nipun Verma
- Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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