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Liu H, Fu Y, Guo D, Li S, Jin Y, Zhang A, Wu C. TMM: A comprehensive CAD system for hepatic fibrosis 5-grade METAVIR staging based on liver MRI. Med Phys 2024; 51:2032-2043. [PMID: 37734071 DOI: 10.1002/mp.16700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/16/2023] [Accepted: 05/26/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Precise staging of hepatic fibrosis with MRI is necessary as it can assist precision medicine. Computer aided diagnosis (CAD) system with distinguishing radiomics features and radiologists domain knowledge is expected to obtain 5-grade meta-analysis of histological data in viral hepatitis (METAVIR) staging. PURPOSE This study aims to obtain the precise staging of hepatic fibrosis based on MRI as it predicts the risk of future liver-related morbidity and the need for treatment, monitoring and surveillance. Based on METAVIR score, fibrosis can be divided into five stages. Most previous researches focus on binary classification, such as cirrhosis versus non-cirrhosis, early versus advanced fibrosis, and substantial fibrosis or not. In this paper, a comprehensive CAD system TMM is proposed to precisely class hepatic fibrosis into five stages for precision medicine instead of the common binary classification. METHODS We propose a novel hepatic fibrosis staging CAD system TMM which includes three modules, Two-level Image Statistical Radiomics Feature (TISRF), Monotonic Error Correcting Output Codes (MECOC) and Monotone Multiclassification with Deep Forest (MMDF). TISRF extracts radiomics features for distinguishing different hepatic fibrosis stages. MECOC is proposed to encode monotonic multiclass by making full use of the progressive severity of hepatic fibrosis and increase the fault tolerance and error correction ability. MMDF combines multiple Deep Forest network to ensure the final five-class classification, which can achieve more precise classification than the common binary classification. The performance of the proposed hepatic fibrosis CAD system is tested on the hepatic data collected from our rabbits models of fibrosis. RESULTS A total of 140 regions of interest (ROI) are selected from MRI T1W of liver fibrosis models in 35 rabbits with F0(n = 16), F1(n = 28), F2(n = 29), F3(n = 44) and F4(n = 23). The performance is evaluated by five-fold cross-validation. TMM can achieve the highest total accuracy of 72.14% for five fibrosis stages compared with other popular classifications. To make a comprehensive comparison, a binary classification experiment have been carried out. CONCLUSIONS T1WI can obtain precise staging of hepatic fibrosis with the help of comprehensive CAD including radiomics features extraction inspired by radiologists, monotonic multiclass according to the severity of hepatic fibrosis, and deep learning classification.
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Affiliation(s)
- Hui Liu
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
| | - Yaqing Fu
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
| | - Dongmei Guo
- Department of Radiology Second Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Shuo Li
- Department of Radiology Second Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Yilin Jin
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
| | - Aoran Zhang
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
| | - Chengjun Wu
- School of Biomedical Engineering, Dalian University of Technology & Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian, China
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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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3
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Moura Cunha G, Fan B, Navin PJ, Olivié D, Venkatesh SK, Ehman RL, Sirlin CB, Tang A. Interpretation, Reporting, and Clinical Applications of Liver MR Elastography. Radiology 2024; 310:e231220. [PMID: 38470236 PMCID: PMC10982829 DOI: 10.1148/radiol.231220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 11/21/2023] [Accepted: 11/24/2023] [Indexed: 03/13/2024]
Abstract
Chronic liver disease is highly prevalent and often leads to fibrosis or cirrhosis and complications such as liver failure and hepatocellular carcinoma. The diagnosis and staging of liver fibrosis is crucial to determine management and mitigate complications. Liver biopsy for histologic assessment has limitations such as sampling bias and high interreader variability that reduce precision, which is particularly challenging in longitudinal monitoring. MR elastography (MRE) is considered the most accurate noninvasive technique for diagnosing and staging liver fibrosis. In MRE, low-frequency vibrations are applied to the abdomen, and the propagation of shear waves through the liver is analyzed to measure liver stiffness, a biomarker for the detection and staging of liver fibrosis. As MRE has become more widely used in clinical care and research, different contexts of use have emerged. This review focuses on the latest developments in the use of MRE for the assessment of liver fibrosis; provides guidance for image acquisition and interpretation; summarizes diagnostic performance, along with thresholds for diagnosis and staging of liver fibrosis; discusses current and emerging clinical applications; and describes the latest technical developments.
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Affiliation(s)
- Guilherme Moura Cunha
- From the Department of Radiology, University of Washington, Seattle,
Wash (G.M.C.); Department of Radiology, Université Laval, Québec,
Québec, Canada (B.F.); Department of Radiology, Mayo Clinic, Rochester,
Minn (P.J.N., S.K.V., R.L.E.); Department of Radiology, Centre Hospitalier de
l'Université de Montréal, 1058 Rue Saint-Denis,
Montréal, QC, Canada H2X 3J4 (D.O., A.T.); and Department of Radiology,
University of California San Diego, San Diego, Calif (C.B.S.)
| | - Boyan Fan
- From the Department of Radiology, University of Washington, Seattle,
Wash (G.M.C.); Department of Radiology, Université Laval, Québec,
Québec, Canada (B.F.); Department of Radiology, Mayo Clinic, Rochester,
Minn (P.J.N., S.K.V., R.L.E.); Department of Radiology, Centre Hospitalier de
l'Université de Montréal, 1058 Rue Saint-Denis,
Montréal, QC, Canada H2X 3J4 (D.O., A.T.); and Department of Radiology,
University of California San Diego, San Diego, Calif (C.B.S.)
| | - Patrick J. Navin
- From the Department of Radiology, University of Washington, Seattle,
Wash (G.M.C.); Department of Radiology, Université Laval, Québec,
Québec, Canada (B.F.); Department of Radiology, Mayo Clinic, Rochester,
Minn (P.J.N., S.K.V., R.L.E.); Department of Radiology, Centre Hospitalier de
l'Université de Montréal, 1058 Rue Saint-Denis,
Montréal, QC, Canada H2X 3J4 (D.O., A.T.); and Department of Radiology,
University of California San Diego, San Diego, Calif (C.B.S.)
| | - Damien Olivié
- From the Department of Radiology, University of Washington, Seattle,
Wash (G.M.C.); Department of Radiology, Université Laval, Québec,
Québec, Canada (B.F.); Department of Radiology, Mayo Clinic, Rochester,
Minn (P.J.N., S.K.V., R.L.E.); Department of Radiology, Centre Hospitalier de
l'Université de Montréal, 1058 Rue Saint-Denis,
Montréal, QC, Canada H2X 3J4 (D.O., A.T.); and Department of Radiology,
University of California San Diego, San Diego, Calif (C.B.S.)
| | - Sudhakar K. Venkatesh
- From the Department of Radiology, University of Washington, Seattle,
Wash (G.M.C.); Department of Radiology, Université Laval, Québec,
Québec, Canada (B.F.); Department of Radiology, Mayo Clinic, Rochester,
Minn (P.J.N., S.K.V., R.L.E.); Department of Radiology, Centre Hospitalier de
l'Université de Montréal, 1058 Rue Saint-Denis,
Montréal, QC, Canada H2X 3J4 (D.O., A.T.); and Department of Radiology,
University of California San Diego, San Diego, Calif (C.B.S.)
| | - Richard L. Ehman
- From the Department of Radiology, University of Washington, Seattle,
Wash (G.M.C.); Department of Radiology, Université Laval, Québec,
Québec, Canada (B.F.); Department of Radiology, Mayo Clinic, Rochester,
Minn (P.J.N., S.K.V., R.L.E.); Department of Radiology, Centre Hospitalier de
l'Université de Montréal, 1058 Rue Saint-Denis,
Montréal, QC, Canada H2X 3J4 (D.O., A.T.); and Department of Radiology,
University of California San Diego, San Diego, Calif (C.B.S.)
| | - Claude B. Sirlin
- From the Department of Radiology, University of Washington, Seattle,
Wash (G.M.C.); Department of Radiology, Université Laval, Québec,
Québec, Canada (B.F.); Department of Radiology, Mayo Clinic, Rochester,
Minn (P.J.N., S.K.V., R.L.E.); Department of Radiology, Centre Hospitalier de
l'Université de Montréal, 1058 Rue Saint-Denis,
Montréal, QC, Canada H2X 3J4 (D.O., A.T.); and Department of Radiology,
University of California San Diego, San Diego, Calif (C.B.S.)
| | - An Tang
- From the Department of Radiology, University of Washington, Seattle,
Wash (G.M.C.); Department of Radiology, Université Laval, Québec,
Québec, Canada (B.F.); Department of Radiology, Mayo Clinic, Rochester,
Minn (P.J.N., S.K.V., R.L.E.); Department of Radiology, Centre Hospitalier de
l'Université de Montréal, 1058 Rue Saint-Denis,
Montréal, QC, Canada H2X 3J4 (D.O., A.T.); and Department of Radiology,
University of California San Diego, San Diego, Calif (C.B.S.)
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Jimenez Ramos M, Kendall TJ, Drozdov I, Fallowfield JA. A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease. Ann Hepatol 2024; 29:101278. [PMID: 38135251 PMCID: PMC10907333 DOI: 10.1016/j.aohep.2023.101278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.
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Affiliation(s)
- Maria Jimenez Ramos
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK
| | - Timothy J Kendall
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK; Edinburgh Pathology, University of Edinburgh, 51 Little France Crescent, Old Dalkeith Rd, Edinburgh EH16 4SA, UK
| | - Ignat Drozdov
- Bering Limited, 54 Portland Place, London, W1B 1DY, UK
| | - Jonathan A Fallowfield
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK.
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Maino C, Vernuccio F, Cannella R, Franco PN, Giannini V, Dezio M, Pisani AR, Blandino AA, Faletti R, De Bernardi E, Ippolito D, Gatti M, Inchingolo R. Radiomics and liver: Where we are and where we are headed? Eur J Radiol 2024; 171:111297. [PMID: 38237517 DOI: 10.1016/j.ejrad.2024.111297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/03/2024] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
Hepatic diffuse conditions and focal liver lesions represent two of the most common scenarios to face in everyday radiological clinical practice. Thanks to the advances in technology, radiology has gained a central role in the management of patients with liver disease, especially due to its high sensitivity and specificity. Since the introduction of computed tomography (CT) and magnetic resonance imaging (MRI), radiology has been considered the non-invasive reference modality to assess and characterize liver pathologies. In recent years, clinical practice has moved forward to a quantitative approach to better evaluate and manage each patient with a more fitted approach. In this setting, radiomics has gained an important role in helping radiologists and clinicians characterize hepatic pathological entities, in managing patients, and in determining prognosis. Radiomics can extract a large amount of data from radiological images, which can be associated with different liver scenarios. Thanks to its wide applications in ultrasonography (US), CT, and MRI, different studies were focused on specific aspects related to liver diseases. Even if broadly applied, radiomics has some advantages and different pitfalls. This review aims to summarize the most important and robust studies published in the field of liver radiomics, underlying their main limitations and issues, and what they can add to the current and future clinical practice and literature.
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Affiliation(s)
- Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Paolo Niccolò Franco
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Michele Dezio
- Department of Radiology, Miulli Hospital, Acquaviva delle Fonti 70021, Bari, Italy
| | - Antonio Rosario Pisani
- Nuclear Medicine Unit, Interdisciplinary Department of Medicine, University of Bari, Bari 70121, Italy
| | - Antonino Andrea Blandino
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Elisabetta De Bernardi
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milano Bicocca, Milano 20100, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy; School of Medicine, University of Milano Bicocca, Milano 20100, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
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Sung JJY, Savulescu J, Ngiam KY, An B, Ang TL, Yeoh KG, Cham TJ, Tsao S, Chua TS. Artificial intelligence for gastroenterology: Singapore artificial intelligence for Gastroenterology Working Group Position Statement. J Gastroenterol Hepatol 2023; 38:1669-1676. [PMID: 37277693 DOI: 10.1111/jgh.16241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND Successful implementation of artificial intelligence in gastroenterology and hepatology practice requires more than technology. There are ethical, legal, and social issues that need to be settled. AIM A group consisting of AI developers (engineer), AI users (gastroenterologist, hepatologist, and surgeon) and AI regulators (ethicist and administrator) formed a Working Group to draft these Positions Statements with the objective of arousing public and professional interest and dialogue, to promote ethical considerations when implementing AI technology, to suggest to policy makers and health authorities relevant factors to take into account when approving and regulating the use of AI tools, and to engage the profession in preparing for change in clinical practice. STATEMENTS These series of Position Statements point out the salient issues to maintain the trust between care provider and care receivers, and to legitimize the use of a non-human tool in healthcare delivery. It is based on fundamental principles such as respect, autonomy, privacy, responsibility, and justice. Enforcing the use of AI without considering these factor risk damaging the doctor-patient relationship.
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Affiliation(s)
- Joseph J Y Sung
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Julian Savulescu
- Centre for Biomedical Ethics, National University of Singapore, Singapore
| | - K Y Ngiam
- Department of Surgery, National University Hospital, Singapore
| | - Bo An
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Tiing Leong Ang
- Singapore Health Service, Changi General Hospital, Singapore
| | - K G Yeoh
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Gastroenterology and Hepatology, National University Hospital, National University Health System, Singapore
| | - Tat-Jen Cham
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Stephen Tsao
- National Healthcare Group, Tan Tock Seng Hospital Singapore, Singapore
- Gastroenterological Society of Singapore, Singapore
| | - T S Chua
- Gastroenterology Chapter, Academy of Medicine, Singapore
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Schattenberg JM, Chalasani N, Alkhouri N. Artificial Intelligence Applications in Hepatology. Clin Gastroenterol Hepatol 2023; 21:2015-2025. [PMID: 37088460 DOI: 10.1016/j.cgh.2023.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 03/16/2023] [Accepted: 04/14/2023] [Indexed: 04/25/2023]
Abstract
Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine learning, computers are now able to learn from complex and diverse clinical datasets to solve real-world medical problems with performance that surpasses that of physicians in certain areas. AI algorithms are currently being implemented in liver imaging, interpretation of liver histopathology, noninvasive tests, prediction models, and more. In this review, we provide a summary of the state of AI in hepatology and discuss current challenges for large-scale implementation including some ethical aspects. We emphasize to the readers that most AI-based algorithms that are discussed in this review are still considered in early development and their utility and impact on patient outcomes still need to be assessed in future large-scale and inclusive studies. Our vision is that the use of AI in hepatology will enhance physician performance, decrease the burden and time spent on documentation, and reestablish the personalized patient-physician relationship that is of utmost importance for obtaining good outcomes.
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Affiliation(s)
- Jörn M Schattenberg
- Metabolic Liver Research Program, I. Department of Medicine, University Medical Center Mainz, Mainz, Germany
| | - Naga Chalasani
- Indiana University School of Medicine and Indiana University Health, Indianapolis, Indiana
| | - Naim Alkhouri
- Arizona Liver Health and University of Arizona, Tucson, Arizona.
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Zheng S, He K, Zhang L, Li M, Zhang H, Gao P. Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging. Eur J Radiol 2023; 165:110912. [PMID: 37290363 DOI: 10.1016/j.ejrad.2023.110912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/10/2023]
Abstract
Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.
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Affiliation(s)
- Shuang Zheng
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Kan He
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Lei Zhang
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Mingyang Li
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Huimao Zhang
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Pujun Gao
- Department of Hepatology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
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Kovatsch A, Honcharova-Biletska H, Segna D, Steigmiller K, Blümel S, Deibel RA, Kühlewindt T, Leinenkugel G, Müller S, Furrer E, Schawkat K, Reiner CS, Weber A, Müllhaupt B, Scharl M, Gubler C, Jüngst C. Performance of two-dimensional shear wave elastography and transient elastography compared to liver biopsy for staging of liver fibrosis. Eur J Clin Invest 2023:e13980. [PMID: 36880934 DOI: 10.1111/eci.13980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 02/12/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Staging of liver fibrosis traditionally relied on liver histology; however, transient elastography (TE) and more recently two-dimensional shear wave elastography (2D-SWE) evolved to noninvasive alternatives. Hence, we evaluated the diagnostic accuracy of 2D-SWE assessed by the Canon Aplio i800 ultrasound system using liver biopsy as reference and compared the performance to TE. METHODS In total, 108 adult patients with chronic liver disease undergoing liver biopsy, 2D-SWE and TE were enrolled prospectively at the University Hospital Zurich. Diagnostic accuracies were evaluated using the area under the receiver operating characteristic (AUROC) analysis, and optimal cut-off values by Youden's index. RESULTS Diagnostic accuracy of 2D-SWE was good for significant (≥F2; AUROC 85.2%, 95% confidence interval (95%CI):76.2-91.2%) as well as severe fibrosis (≥F3; AUROC 86.8%, 95%CI: 78.1-92.4%) and excellent for cirrhosis (AUROC 95.6%, 95%CI: 89.9-98.1%), compared to histology. TE performed equally well (significant fibrosis: 87.5%, 95%CI: 77.7-93.3%; severe fibrosis: 89.7%, 95%CI: 82.0-94.3%; cirrhosis: 96%, 95%CI: 90.4-98.4%), and accuracy was not statistically different to 2D-SWE. 2D-SWE optimal cut-off values were 6.5, 9.8 and 13.1 kPa for significant fibrosis, severe fibrosis and cirrhosis, respectively. CONCLUSIONS Performance of 2D-SWE was good to excellent and well comparable with TE, supporting the application of this 2D-SWE system in the diagnostic workup of chronic liver disease.
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Affiliation(s)
- Audrey Kovatsch
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | | | - Daniel Segna
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland.,Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Klaus Steigmiller
- Institute of Epidemiology, Biostatistics and Prevention, University of Zurich, Zurich, Switzerland
| | - Sena Blümel
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Rudolf A Deibel
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Tobias Kühlewindt
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Georg Leinenkugel
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Sandra Müller
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Eva Furrer
- Institute of Epidemiology, Biostatistics and Prevention, University of Zurich, Zurich, Switzerland
| | - Khoschy Schawkat
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Cäcilia S Reiner
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Achim Weber
- Department of Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Beat Müllhaupt
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Michael Scharl
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Christoph Gubler
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland.,Division of Gastroenterology &Hepatology, Triemli Hospital, Zurich, Switzerland
| | - Christoph Jüngst
- Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland.,Department of Medicine II, Saarland University Medical Center, Saarland University, Homburg, Germany
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Zhang D, Cao Y, Sun Y, Zhao X, Peng C, Zhao J, Bao X, Wang L, Zhang C. Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study. Eur Radiol 2023; 33:1653-67. [PMID: 36149481 DOI: 10.1007/s00330-022-09137-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/05/2022] [Accepted: 09/01/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To investigate the value of R2* mapping-based radiomics nomograms in staging liver fibrosis in patients with chronic hepatitis B. METHODS Between January 2020 and December 2020, 151 patients with chronic hepatitis B were randomly divided into training (n = 103) and validation (n = 48) cohorts. From January to February 2021, 58 patients were included in a test cohort. Radiomics features were selected using the interclass correlation coefficient and least absolute shrinkage and selection operator method. Three radiomics nomograms, combining the radiomics score (Radscore) derived from R2* mapping and clinical variables, were used for staging significant and advanced fibrosis, and cirrhosis. Performance of the model was evaluated using the AUC. The utility and clinical benefits were evaluated using the continuous net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). RESULTS The Radscore calculated by 12 radiomics features and independent factors (laminin and platelet) of advanced fibrosis were used to construct the radiomics nomograms. In the test cohort, the AUCs of the radiomics nomograms for staging significant fibrosis, advanced fibrosis, and cirrhosis were 0.738 (95% confidence interval [CI]: 0.604-0.872), 0.879 (95% CI: 0.779-0.98), and 0.952 (95% CI: 0.878-1), respectively. NRI, IDI, and DCA confirmed that radiomics nomograms demonstrated varying degrees of clinical benefit and improvement for advanced fibrosis and cirrhosis, but not for significant fibrosis. CONCLUSIONS Radiomics nomograms combined with R2* mapping-based Radscore, laminin, and platelet have value in staging advanced fibrosis and cirrhosis but limited value for staging significant fibrosis. KEY POINTS • Laminin and platelets were independent predictors of advanced fibrosis. • Radiomics analysis based on R2* mapping was beneficial for evaluating advanced fibrosis and cirrhosis. • It was difficult to distinguish significant fibrosis using a radiomics nomogram, which is possibly due to the complex pathological microenvironment of chronic liver diseases.
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11
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Mo X, Chen W, Chen S, Chen Z, Guo Y, Chen Y, Wu X, Zhang L, Chen Q, Jin Z, Li M, Chen L, You J, Xiong Z, Zhang B, Zhang S. MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study. Insights Imaging 2023; 14:28. [PMID: 36746892 PMCID: PMC9902579 DOI: 10.1186/s13244-023-01370-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 01/03/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models. RESULTS The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively. CONCLUSION We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.
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Affiliation(s)
- Xiaokai Mo
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Wenbo Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China ,grid.470066.3Department of Radiology, Huizhou Municipal Central Hospital, No. 41 Eling Bei Road, Huizhou, 516001 Guangdong People’s Republic of China
| | - Simin Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhuozhi Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Yuanshu Guo
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Yulian Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Xuewei Wu
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Lu Zhang
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Qiuying Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhe Jin
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Minmin Li
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Luyan Chen
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Jingjing You
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Zhiyuan Xiong
- grid.412601.00000 0004 1760 3828Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627 Guangdong People’s Republic of China
| | - Bin Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
| | - Shuixing Zhang
- Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, People's Republic of China.
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12
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Yu JH, Lee HA, Kim SU. Noninvasive imaging biomarkers for liver fibrosis in nonalcoholic fatty liver disease: current and future. Clin Mol Hepatol 2023; 29:S136-S149. [PMID: 36503205 PMCID: PMC10029967 DOI: 10.3350/cmh.2022.0436] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is increasingly prevalent worldwide and becoming a major cause of liver disease-related morbidity and mortality. The presence of liver fibrosis in patients with NAFLD is closely related to prognosis, including the development of hepatocellular carcinoma and other complications of cirrhosis. Therefore, assessment of the presence of significant or advanced liver fibrosis is crucial. Although liver biopsy has been considered the "gold standard" method for evaluating the degree of liver fibrosis, it is not suitable for extensive use in all patients with NAFLD owing to its invasiveness and high cost. Therefore, noninvasive biochemical and imaging biomarkers have been developed to overcome the limitations of liver biopsy. Imaging biomarkers for the stratification of liver fibrosis have been evaluated in patients with NAFLD using different imaging techniques, such as transient elastography, shear wave elastography, and magnetic resonance elastography. Furthermore, artificial intelligence and deep learning methods are increasingly being applied to improve the diagnostic accuracy of imaging techniques and overcome the pitfalls of existing imaging biomarkers. In this review, we describe the usefulness and future prospects of noninvasive imaging biomarkers that have been studied and used to evaluate the degree of liver fibrosis in patients with NAFLD.
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Affiliation(s)
- Jung Hwan Yu
- Department of Internal Medicine, Inha University Hospital and School of Medicine, Incheon, Korea
| | - Han Ah Lee
- Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Seung Up Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
- Yonsei Liver Center, Severance Hospital, Seoul, Korea
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13
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Zhang L, Mao Y. Artificial Intelligence in NAFLD: Will Liver Biopsy Still Be Necessary in the Future? Healthcare (Basel) 2022; 11. [PMID: 36611577 DOI: 10.3390/healthcare11010117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/03/2022] [Accepted: 12/26/2022] [Indexed: 01/03/2023] Open
Abstract
As the advanced form of nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH) will significantly increase the risks of liver fibrosis, cirrhosis, and HCC. However, there is no non-invasive method to distinguish NASH from NAFLD so far. Additionally, liver biopsy remains the gold standard to diagnose NASH, which is not appropriate for routine screening. Recently, artificial intelligence (AI) is under rapid development in many aspects of medicine. Additionally, the application of AI in clinical information may have the potential to diagnose NASH non-invasively. This review summarizes the latest research using AI, specifically machine learning, to facilitate the diagnosis, prognosis, and monitoring of NAFLD. Additionally, according to our prior results, this work proposes future development in this area.
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Zerunian M, Pucciarelli F, Masci B, Siciliano F, Polici M, Bracci B, Guido G, Polidori T, De Santis D, Laghi A, Caruso D. Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review. Biomed Res Int 2022; 2022:1147111. [PMID: 36619303 DOI: 10.1155/2022/1147111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 12/29/2022]
Abstract
Diffuse liver diseases are highly prevalent conditions around the world, including pathological liver changes that occur when hepatocytes are damaged and liver function declines, often leading to a chronic condition. In the last years, Magnetic Resonance Imaging (MRI) is reaching an important role in the study of diffuse liver diseases moving from qualitative to quantitative assessment of liver parenchyma. In fact, this can allow noninvasive accurate and standardized assessment of diffuse liver diseases and can represent a concrete alternative to biopsy which represents the current reference standard. MRI approach already tested for other pathologies include diffusion-weighted imaging (DWI) and radiomics, able to quantify different aspects of diffuse liver disease. New emerging MRI quantitative methods include MR elastography (MRE) for the quantification of the hepatic stiffness in cirrhotic patients, dedicated gradient multiecho sequences for the assessment of hepatic fat storage, and iron overload. Thus, the aim of this review is to give an overview of the technical principles and clinical application of new quantitative MRI techniques for the evaluation of diffuse liver disease.
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15
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Im WH, Song JS, Jang W. Noninvasive staging of liver fibrosis: review of current quantitative CT and MRI-based techniques. Abdom Radiol (NY) 2022; 47:3051-3067. [PMID: 34228199 DOI: 10.1007/s00261-021-03181-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 01/18/2023]
Abstract
Liver fibrosis features excessive protein accumulation in the liver interstitial space resulting from repeated tissue injury due to chronic liver disease. Liver fibrosis eventually proceeds to cirrhosis and associated complications. So, early diagnosis and staging of liver fibrosis are of vital importance for clinical treatment. Liver biopsy remains the gold standard for the diagnosing and staging of fibrosis, but it is suboptimal due to various limitations. Recently, efforts have been made to migrate toward noninvasive techniques for assessing liver fibrosis. CT is relatively easy to perform, relatively standardized for different scanners, and does not require additional hardware in liver fibrosis staging. MRI is frequently performed to characterize indeterminate liver lesions. Because it does not use ionizing radiation and features high image contrast, its role has increased in the staging of liver fibrosis. More recently, several studies on liver fibrosis staging using deep learning algorithms in CT or MRI have been proposed and have shown meaningful results. In this review, we summarize the basic concept, diagnostic performance, and advantages and limitations of each technique to noninvasively stage liver fibrosis.
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Affiliation(s)
- Won Hyeong Im
- Department of Radiology, The 3rd Flying Training Wing, Sacheon, 52516, South Korea
| | - Ji Soo Song
- Department of Radiology, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju, 54907, Jeonbuk, South Korea.
- Research Institute of Clinical Medicine of Jeonbuk National University, Jeonju, South Korea.
- Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea.
| | - Weon Jang
- Department of Radiology, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju, 54907, Jeonbuk, South Korea
- Research Institute of Clinical Medicine of Jeonbuk National University, Jeonju, South Korea
- Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
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Zou L, Zhang H, Wang Q, Zhong W, Du Y, Liu H, Xing W. Simultaneous liver steatosis, fibrosis and iron deposition quantification with mDixon quant based on radiomics analysis in a rabbit model. Magn Reson Imaging 2022; 94:36-42. [PMID: 35988836 DOI: 10.1016/j.mri.2022.08.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/08/2022] [Accepted: 08/14/2022] [Indexed: 11/17/2022]
Abstract
PURPOSE To evaluate the feasibility of simultaneous quantification of liver fibrosis, liver steatosis and abnormal iron deposition using mDixon Quant based on radiomics analysis, and to eliminate the interference among different histopathologic features. METHODS One hundred and twenty rabbits that were administered CCl4 for 4-16 weeks and a cholesterol rich diet for the initial 4 weeks in the experimental group and 20 rabbits in the control group were examined using mDixon. Radiomics features of the whole liver were extracted from PDFF and R2* and radiomics models for discriminating steatosis: S0-S1 vs. S2-S4, fibrosis: F0-F2 vs. F3-F4 and iron deposition: normal vs. abnormal were constructed respectively and evaluated using receiver operating characteristic (ROC) curves with the histopathological results as reference standard. Combined corrected models merging the radscore and the other two histopathologic features were evaluated using multiple logistic regression analyses and compared with radiomics models. RESULTS The area under the ROC curve (AUC) of the radiomics model with PDFF features was 0.886 and 0.843 in the training and the test set, respectively, for the diagnosis of liver steatosis grade S0-1 and S2-S4. The radiomics model based on R2* features were 0.815 and 0.801 for distinguishing F0-F2 and F3-F4 and 0.831 and 0.738 for discriminating abnormal iron deposition in the training and test set, respectively. The corrected model for liver steatosis and fibrosis (0.944 and 0.912 in the test set) outperformed the radiomics models by eliminating the interference of histopathologic features(P < 0.05), but had comparable diagnostic performance for abnormal iron deposition(P > 0.05). CONCLUSIONS It is feasible for mDixon to simultaneously quantify whole liver steatosis, fibrosis and iron deposition based on radiomics analysis. It is valuable to minimize the interference of different pathological features for the assessment of liver steatosis and fibrosis.
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Affiliation(s)
- LiQiu Zou
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - Hao Zhang
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - WenXin Zhong
- Department of Radiology, Sixth Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong Province, China
| | - YaNan Du
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - HaiFeng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213200, China.
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Roubidoux MA, Kaur JS, Rhoades DA. Health Disparities in Cancer Among American Indians and Alaska Natives. Acad Radiol 2022; 29:1013-1021. [PMID: 34802904 DOI: 10.1016/j.acra.2021.10.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/15/2021] [Accepted: 10/20/2021] [Indexed: 12/15/2022]
Abstract
American Indians and Alaska Natives (AI/AN) are underserved populations who suffer from several health disparities, 1 of which is cancer. Malignancies, especially cancers of the breast, liver, and lung, are common causes of death in this population. Health care disparities in this population include more limited access to diagnostic radiology because of geographic and/or health system limitations. Early detection of these cancers may be enabled by improving patient and physician access to medical imaging. Awareness by the radiology community of the cancer disparities among this population is needed to support research targeted to this specific ethnic group and to support outreach efforts to provide more imaging opportunities. Providing greater access to imaging facilities will also improve patient compliance with screening recommendations, ultimately improving mortality in these populations.
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Affiliation(s)
- Marilyn A Roubidoux
- Department of Radiology, Michigan Medicine, TC 2910, 1500 E. Medical Center Drive, Ann Arbor, Mi 48109-5326.
| | - Judith S Kaur
- Department of Hematology and Oncology, Mayo Clinic, Jacksonville, Florida
| | - Dorothy A Rhoades
- Department of Internal Medicine, Stephenson Cancer Center and the University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
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Zhao R, Zhao H, Ge YQ, Zhou FF, Wang LS, Yu HZ, Gong XJ. Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis. Can J Gastroenterol Hepatol 2022; 2022:2249447. [PMID: 35775068 DOI: 10.1155/2022/2249447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/29/2022] [Accepted: 05/30/2022] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To develop and validate a radiomic nomogram based on texture features from out-of-phase T1W images and clinical biomarkers in prediction of liver fibrosis. MATERIALS AND METHODS Patients clinically diagnosed with chronic liver fibrosis who underwent liver biopsy and noncontrast MRI were enrolled. All patients were assigned to the nonsignificant fibrosis group with fibrosis stage <2 and the significant fibrosis group with stage ≥2. Texture parameters were extracted from out-of-phase T1-weighted (T1W) images and calculated using the Artificial Intelligent Kit (AK). Boruta and LASSO regressions were used for feature selection and a multivariable logistic regression was used for construction of a combinational model integrating radiomics and clinical biomarkers. The performance of the models was assessed by using the receiver operator curve (ROC) and decision curve. RESULTS ROC analysis of the radiomics model that included the most discriminative features showed AUCs of the training and test groups were 0.80 and 0.78. A combinational model integrating RADscore and fibrosis 4 index was established. ROC analysis of the training and test groups showed good to excellent performance with AUC of 0.93 and 0.86. Decision curves showed the combinational model added more net benefit than radiomic and clinical models alone. CONCLUSIONS The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.
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Sim KC, Kim MJ, Cho Y, Kim HJ, Park BJ, Sung DJ, Han YE, Han NY, Kim TH, Lee YJ. Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. J Comput Assist Tomogr 2022. [PMID: 35483092 DOI: 10.1097/RCT.0000000000001308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
OBJECTIVE The aim of the study was to investigate the diagnostic feasibility of radiomics analysis using magnetic resonance elastography (MRE) to assess hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). METHODS One hundred patients with suspected NAFLD were retrospectively enrolled. All patients underwent a liver parenchymal biopsy. Magnetic resonance elastography was performed using a 3.0-T scanner. After multislice segmentation of MRE images, 834 radiomic features were analyzed using a commercial program. Radiologic features, such as median and mean values of the regions of interest and variable clinical features, were analyzed. A random forest regressor was used to extract important radiomic, radiological, and clinical features. A random forest classifier model was trained to use these features to classify the fibrosis stage. The area under the receiver operating characteristic curve was evaluated using a classifier for fibrosis stage diagnosis. RESULTS The pathological hepatic fibrosis stage was classified as low-grade fibrosis (stages F0-F1, n = 82) or clinically significant fibrosis (stages F2-F4, n = 18). Eight important features were extracted from radiomics analysis, with the 2 most important being wavelet-high high low gray level dependence matrix dependence nonuniformity-normalized and wavelet-high high low gray level dependence matrix dependence entropy. The median value of the multiple small regions of interest was identified as the most important radiologic feature. Platelet count has been identified as an important clinical feature. The area under the receiver operating characteristic curve of the classifier using radiomics was comparable with that of radiologic measures (0.97 ± 0.07 and 0.96 ± 0.06, respectively). CONCLUSIONS Magnetic resonance elastography radiomics analysis provides diagnostic performance comparable with conventional MRE analysis for the assessment of clinically significant hepatic fibrosis in patients with NAFLD.
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Chen ZW, Xiao HM, Ye X, Liu K, Rios RS, Zheng KI, Jin Y, Targher G, Byrne CD, Shi J, Yan Z, Chi XL, Zheng MH. A novel radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with biopsy-proven nonalcoholic fatty liver disease: a derivation and independent validation study. Hepatobiliary Surg Nutr 2022; 11:212-226. [PMID: 35464279 DOI: 10.21037/hbsn-21-23] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/08/2021] [Indexed: 12/12/2022]
Abstract
Background Currently, there are no effective methods for assessing hepatic inflammation without resorting to histological examination of liver tissue obtained by biopsy. T2-weighted images (T2WI) are routinely obtained from liver magnetic resonance imaging (MRI) scan sequences. We aimed to establish a radiomics signature based on T2WI (T2-RS) for assessment of hepatic inflammation in people with nonalcoholic fatty liver disease (NAFLD). Methods A total of 203 individuals with biopsy-confirmed NAFLD from two independent Chinese cohorts with liver MRI examination were enrolled in this study. The hepatic inflammatory activity score (IAS) was calculated by the unweighted sum of the histologic scores for lobular inflammation and ballooning. One thousand and thirty-two radiomics features were extracted from the localized region of interest (ROI) in the right liver lobe of T2WI and, subsequently, selected by minimum redundancy maximum relevance and least absolute shrinkage and selection operator (LASSO) methods. The T2-RS was calculated by adding the selected features weighted by their coefficients. Results Eighteen radiomics features from Laplacian of Gaussian, wavelet, and original images were selected for establishing T2-RS. The T2-RS value differed significantly between groups with increasing grades of hepatic inflammation (P<0.01). The T2-RS yielded an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.80 [95% confidence interval (CI): 0.71-0.89] for predicting hepatic inflammation in the training cohort with excellent calibration. The AUROCs of T2-RS in the internal cohort and external validation cohorts were 0.77 (0.61-0.93) and 0.75 (0.63-0.84), respectively. Conclusions The T2-RS derived from radiomics analysis of T2WI shows promising utility for predicting hepatic inflammation in individuals with NAFLD.
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Affiliation(s)
- Zhong-Wei Chen
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huan-Ming Xiao
- Department of Hepatology, Guangdong Provincial Hospital of Chinese Medicine, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xinjian Ye
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Liu
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Rafael S Rios
- NAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kenneth I Zheng
- NAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yi Jin
- Department of Pathology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Giovanni Targher
- Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy
| | - Christopher D Byrne
- Southampton National Institute for Health Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, Southampton, UK
| | - Junping Shi
- Department of Hepatology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiao-Ling Chi
- Department of Hepatology, Guangdong Provincial Hospital of Chinese Medicine, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ming-Hua Zheng
- NAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Institute of Hepatology, Wenzhou Medical University, Wenzhou, China.,Key Laboratory of Diagnosis and Treatment for The Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
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21
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Abstract
Liver fibrosis (LF) is the wound healing response to chronic liver injury. LF is the endpoint of chronic liver disease (CLD) regardless of etiology and the single most important determinant of long-term liver-related clinical outcomes. Quantification of LF is important for staging, to evaluate response to treatment and to predict outcomes. LF is traditionally staged by liver biopsy. However, liver biopsy is invasive and suffers from sampling errors when biopsy size is inadequate; therefore, non-invasive tests (NITs) have found important roles in clinical care. NITs include simple laboratory-based serum tests, panels of serum tests, and imaging biomarkers. NITs are validated against the liver biopsy and will be used in the future for evaluation of nearly all CLDs with invasive liver biopsy reserved for some cases. Both serum tests and some imaging biomarkers such as elastography are currently used clinically as surrogate markers for LF. Several other imaging biomarkers are still considered research and awaiting clinical application in the future. As the evaluation of imaging biomarkers will likely become the norm in the future, understanding pathogenesis of LF is important. Knowledge of properties measured by imaging biomarkers and its correlation with LF is important to understand the application of NITs by abdominal radiologists. In this review, we present a brief overview of pathogenesis of LF, spatiotemporal evolution of LF in different CLD, and severity assessment with liver biopsy. This will be followed by a brief discussion on properties measured by imaging biomarkers and their relationship to the LF.
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Affiliation(s)
- Sudhakar K Venkatesh
- Abdominal Imaging Division, Department of Radiology, Mayo Clinic, 200, First Street SW, Rochester, MN, 55905, USA.
| | - Michael S Torbenson
- Anatomic Pathology Division, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Pollack BL, Batmanghelich K, Cai SS, Gordon E, Wallace S, Catania R, Morillo-Hernandez C, Furlan A, Borhani AA. Deep Learning Prediction of Voxel-Level Liver Stiffness in Patients with Nonalcoholic Fatty Liver Disease. Radiol Artif Intell 2021; 3:e200274. [PMID: 34870213 DOI: 10.1148/ryai.2021200274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 09/07/2021] [Accepted: 09/13/2021] [Indexed: 12/14/2022]
Abstract
Purpose To reconstruct virtual MR elastography (MRE) images based on traditional MRI inputs with a machine learning algorithm. Materials and Methods In this single-institution, retrospective study, 149 patients (mean age, 58 years ± 12 [standard deviation]; 71 men) with nonalcoholic fatty liver disease who underwent MRI and MRE between January 2016 and January 2019 were evaluated. Nine conventional MRI sequences and clinical data were used to train a convolutional neural network to reconstruct MRE images at the per-voxel level. The architecture was further modified to accept multichannel three-dimensional inputs and to allow inclusion of clinical and demographic information. Liver stiffness and fibrosis category (F0 [no fibrosis] to F4 [significant fibrosis]) of reconstructed images were assessed by using voxel- and patient-level agreement by correlation, sensitivity, and specificity calculations; in addition, classification by receiver operator characteristic analyses was performed, and Dice score was used to evaluate hepatic stiffness locality. Results The model for predicting liver stiffness incorporated four image sequences (precontrast T1-weighted liver acquisition with volume acquisition [LAVA] water and LAVA fat, 120-second-delay T1-weighted LAVA water, and single-shot fast spin-echo T2 weighted) and clinical data. The model had a patient-level and voxel-level correlation of 0.50 ± 0.05 and 0.34 ± 0.03, respectively. By using a stiffness threshold of 3.54 kPa to make a binary classification into no fibrosis or mild fibrosis (F0-F1) versus clinically significant fibrosis (F2-F4), the model had sensitivity of 80% ± 4, specificity of 75% ± 5, accuracy of 78% ± 3, area under the receiver operating characteristic curve of 84 ± 0.04, and a Dice score of 0.74. Conclusion The generation of virtual elastography images is feasible by using conventional MRI and clinical data with a machine learning algorithm.Keywords: MR Imaging, Abdomen/GI, Liver, Cirrhosis, Computer Applications/Virtual Imaging, Experimental Investigations, Feature Detection, Classification, Reconstruction Algorithms, Supervised Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2021.
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Affiliation(s)
- Brian L Pollack
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
| | - Kayhan Batmanghelich
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
| | - Stephen S Cai
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
| | - Emile Gordon
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
| | - Stephen Wallace
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
| | - Roberta Catania
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
| | - Carlos Morillo-Hernandez
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
| | - Alessandro Furlan
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
| | - Amir A Borhani
- Department of Biomedical Informatics (B.L.P., K.B.) and Department of Radiology (C.M.H.), University of Pittsburgh School of Medicine, Pittsburgh, Pa; and Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (S.S.C., E.G., S.W., R.C., A.F., A.A.B.)
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Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27:6825-6843. [PMID: 34790009 PMCID: PMC8567471 DOI: 10.3748/wjg.v27.i40.6825] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/16/2021] [Accepted: 09/30/2021] [Indexed: 02/06/2023] Open
Abstract
Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.
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Affiliation(s)
- Charles E Hill
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Luca Biasiolli
- Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
| | | | - Vicente Grau
- Department of Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Michael Pavlides
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
- Translational Gastroenterology Unit, University of Oxford, Oxford OX3 9DU, United Kingdom
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 9DU, United Kingdom
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25
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Dinani AM, Kowdley KV, Noureddin M. Application of Artificial Intelligence for Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art. Hepatology 2021; 74:2233-2240. [PMID: 33928671 DOI: 10.1002/hep.31869] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/24/2021] [Accepted: 04/23/2021] [Indexed: 12/17/2022]
Abstract
The diagnosis of nonalcoholic fatty liver disease and associated fibrosis is challenging given the lack of signs, symptoms and nonexistent diagnostic test. Furthermore, follow up and treatment decisions become complicated with a lack of a simple reproducible method to follow these patients longitudinally. Liver biopsy is the current standard to detect, risk stratify and monitor individuals with nonalcoholic fatty liver disease. However, this method is an unrealistic option in a population that affects about one in three to four individuals worldwide. There is an urgency to develop innovative methods to facilitate management at key points in an individual's journey with nonalcoholic fatty liver disease fibrosis. Artificial intelligence is an exciting field that has the potential to achieve this. In this review, we highlight applications of artificial intelligence by leveraging our current knowledge of nonalcoholic fatty liver disease to diagnose and risk stratify NASH phenotypes.
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Affiliation(s)
- Amreen M Dinani
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kris V Kowdley
- Liver Institute Northwest, Seattle, WA; Elson S. Floyd College of Medicine, Washington State University, WA
| | - Mazen Noureddin
- Division of Digestive and Liver Diseases, Cedar Sinai Medical Center, Los Angeles, CA
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26
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Sofias AM, De Lorenzi F, Peña Q, Azadkhah Shalmani A, Vucur M, Wang JW, Kiessling F, Shi Y, Consolino L, Storm G, Lammers T. Therapeutic and diagnostic targeting of fibrosis in metabolic, proliferative and viral disorders. Adv Drug Deliv Rev 2021; 175:113831. [PMID: 34139255 PMCID: PMC7611899 DOI: 10.1016/j.addr.2021.113831] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/30/2021] [Accepted: 06/10/2021] [Indexed: 02/07/2023]
Abstract
Fibrosis is a common denominator in many pathologies and crucially affects disease progression, drug delivery efficiency and therapy outcome. We here summarize therapeutic and diagnostic strategies for fibrosis targeting in atherosclerosis and cardiac disease, cancer, diabetes, liver diseases and viral infections. We address various anti-fibrotic targets, ranging from cells and genes to metabolites and proteins, primarily focusing on fibrosis-promoting features that are conserved among the different diseases. We discuss how anti-fibrotic therapies have progressed over the years, and how nanomedicine formulations can potentiate anti-fibrotic treatment efficacy. From a diagnostic point of view, we discuss how medical imaging can be employed to facilitate the diagnosis, staging and treatment monitoring of fibrotic disorders. Altogether, this comprehensive overview serves as a basis for developing individualized and improved treatment strategies for patients suffering from fibrosis-associated pathologies.
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Affiliation(s)
- Alexandros Marios Sofias
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Faculty of Medicine, RWTH Aachen University, Aachen, Germany; Mildred Scheel School of Oncology (MSSO), Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO(ABCD)), University Hospital Aachen, Aachen, Germany; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
| | - Federica De Lorenzi
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Quim Peña
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Armin Azadkhah Shalmani
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Mihael Vucur
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University, Duesseldorf, Germany
| | - Jiong-Wei Wang
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Cardiovascular Research Institute, National University Heart Centre Singapore, Singapore, Singapore; Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Nanomedicine Translational Research Programme, Centre for NanoMedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Fabian Kiessling
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Yang Shi
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Lorena Consolino
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
| | - Gert Storm
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Nanomedicine Translational Research Programme, Centre for NanoMedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; Department of Targeted Therapeutics, University of Twente, Enschede, the Netherlands.
| | - Twan Lammers
- Department of Nanomedicine and Theranostics, Institute for Experimental Molecular Imaging, Faculty of Medicine, RWTH Aachen University, Aachen, Germany; Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; Department of Targeted Therapeutics, University of Twente, Enschede, the Netherlands.
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Cardobi N, Dal Palù A, Pedrini F, Beleù A, Nocini R, De Robertis R, Ruzzenente A, Salvia R, Montemezzi S, D’Onofrio M. An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging. Cancers (Basel) 2021; 13:2162. [PMID: 33946223 PMCID: PMC8124771 DOI: 10.3390/cancers13092162] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.
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Affiliation(s)
- Nicolò Cardobi
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Alessandro Dal Palù
- Department of Mathematical, Physical and Computer Sciences, University of Parma, 43121 Parma, Italy;
| | - Federica Pedrini
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
| | - Alessandro Beleù
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
| | - Riccardo Nocini
- Otolaryngology-Head and Neck Surgery Department, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy;
| | - Riccardo De Robertis
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Andrea Ruzzenente
- Department of Surgery, General and Hepatobiliary Surgery, University Hospital G.B. Rossi, University and Hospital Trust of Verona, 37126 Verona, Italy;
| | - Roberto Salvia
- Unit of General and Pancreatic Surgery, Department of Surgery and Oncology, University of Verona Hospital Trust, 37126 Verona, Italy;
| | - Stefania Montemezzi
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Mirko D’Onofrio
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
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Qu Z, Yang S, Xing F, Tong R, Yang C, Guo R, Huang J, Lu F, Fu C, Yan X, Hectors S, Gillen K, Wang Y, Liu C, Zhan S, Li J. Magnetic resonance quantitative susceptibility mapping in the evaluation of hepatic fibrosis in chronic liver disease: a feasibility study. Quant Imaging Med Surg 2021; 11:1170-1183. [PMID: 33816158 DOI: 10.21037/qims-20-720] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background Noninvasive methods for the early diagnosis and staging of hepatic fibrosis are needed. The present study aimed to investigate the alteration of magnetic susceptibility in the liver of patients with various fibrosis stages and to evaluate the feasibility of using susceptibility to stage hepatic fibrosis. Methods A total of 30 consecutive patients with chronic liver diseases (CLDs) underwent magnetic resonance imaging (MRI) and liver biopsy evaluation of hepatic fibrosis, necroinflammatory activity, iron load, and steatosis. Quantitative susceptibility mapping (QSM), R2* and proton density fat fraction (PDFF) images were postprocessed from the same gradient-echo data for quantitative tissue characterization using region of interest (ROI) analysis. The differences for MRI measurements between cohorts of non-significant (Ishak-F <3) and significant fibrosis (Ishak-F ≥3) and the correlation of MRI measurements with fibrosis stages and necroinflammatory activity grades were tested. Receiver operating characteristic (ROC) analysis was also performed. Results There was a significant difference in liver susceptibility between the cohorts of significant and non-significant fibrosis (Z=-2.880, P=0.004). A moderate negative correlation between the stages of liver fibrosis and liver susceptibility was observed (r=-0.471, P=0.015). Liver magnetic susceptibility differentiated non-significant from significant hepatic fibrosis with an area under the receiver operating curve (AUC) of 0.836 (P=0.004). A highly sensitive diagnostic performance with an AUC of 0.933 was obtained using magnetic susceptibility and PDFF together (P<0.001). Conclusions A noninvasive liver QSM-based evaluation promises an accurate assessment of significant fibrosis in patients with CLDs.
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Affiliation(s)
- Zheng Qu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Shuohui Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Feng Xing
- Department of Liver Diseases, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rui Tong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Chenyao Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Rongfang Guo
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiling Huang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fang Lu
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Caixia Fu
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Xu Yan
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Stefanie Hectors
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Kelly Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.,Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Chenghai Liu
- Department of Liver Diseases, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Shanghai Key Laboratory of Traditional Chinese Clinical Medicine, Shanghai, China
| | - Songhua Zhan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
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Zhao R, Gong XJ, Ge YQ, Zhao H, Wang LS, Yu HZ, Liu B. Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis. Can J Gastroenterol Hepatol 2021; 2021:6677821. [PMID: 33791254 DOI: 10.1155/2021/6677821] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 01/28/2021] [Accepted: 03/03/2021] [Indexed: 12/30/2022] Open
Abstract
Purpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and noncontrast MRI scans, were enrolled. TA parameters were extracted from out-of-phase T1-weighted (T1W), in-phase T1W, and T2-weighted (T2W) images and calculated using Artificial Intelligent Kit (AK). Features were extracted including first-order, shape, gray-level cooccurrence matrix, gray-level run-length matrix, neighboring gray one tone difference matrix, and gray-level differential matrix. After statistical analyses, final diagnostic models were constructed. Receiver operating curves (ROCs) and areas under the ROC (AUCs) were used to assess the diagnostic value of each final model and 100-time repeated cross-validation was applied to assess the stability of the logistic regression models. Results. A total of 57 patients were enrolled in this study, with 27 in the fibrosis stage < 2 and 30 in stages ≥ 2. Overall, 851 features were extracted per ROI. Eight features with high correlation were selected by the maximum relevance method in each sequence, and all had a good diagnostic performance. ROC analysis of the final models showed that all sequences had a preferable performance with AUCs of 0.87, 0.90, and 0.96 in T2W and in-phase and out-of-phase T1W, respectively. Cross-validation results reported the following values of mean accuracy, specificity, and sensitivity: 0.98 each for out-of-phase T1W; 0.90, 0.89, and 0.90 for in-phase T1W; and 0.86, 0.88, 0.84 for T2W in the training set, and 0.76, 0.81, and 0.72 for out-of-phase T1W; 0.74, 0.72, and 0.75 for in-phase T1W; and 0.63, 0.64, and 0.63 for T2W for the test group, respectively. Conclusion. Noncontrast MRI scans with texture analysis are viable for classifying the early stages of liver fibrosis, exhibiting excellent diagnostic performance.
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Wong GLH, Yuen PC, Ma AJ, Chan AWH, Leung HHW, Wong VWS. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol 2021; 36:543-550. [PMID: 33709607 DOI: 10.1111/jgh.15385] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/14/2020] [Accepted: 12/20/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) has become increasingly widespread in our daily lives, including healthcare applications. AI has brought many new insights into better ways we care for our patients with chronic liver disease, including non-alcoholic fatty liver disease and liver fibrosis. There are multiple ways to apply the AI technology on top of the conventional invasive (liver biopsy) and noninvasive (transient elastography, serum biomarkers, or clinical prediction models) approaches. In this review article, we discuss the principles of applying AI on electronic health records, liver biopsy, and liver images. A few common AI approaches include logistic regression, decision tree, random forest, and XGBoost for data at a single time stamp, recurrent neural networks for sequential data, and deep neural networks for histology and images.
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Affiliation(s)
- Grace Lai-Hung Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong.,Medical Data Analytic Centre (MDAC), The Chinese University of Hong Kong, Shatin, Hong Kong.,Institute of Digestive Disease, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Pong-Chi Yuen
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Andy Jinhua Ma
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Anthony Wing-Hung Chan
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Howard Ho-Wai Leung
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Vincent Wai-Sun Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong.,Medical Data Analytic Centre (MDAC), The Chinese University of Hong Kong, Shatin, Hong Kong.,Institute of Digestive Disease, The Chinese University of Hong Kong, Shatin, Hong Kong
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Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 2021; 21:10. [PMID: 33407169 PMCID: PMC7788739 DOI: 10.1186/s12876-020-01585-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 12/16/2020] [Indexed: 12/12/2022] Open
Abstract
Background The gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Various noninvasive modalities, e.g., ultrasonography, elastography and clinical predictive scores, have been used as alternatives to liver biopsy, with limited performance. Recently, artificial intelligence (AI) models have been developed and integrated into noninvasive diagnostic tools to improve their performance. Methods We systematically searched for studies on AI-assisted diagnosis of liver fibrosis and NAFLD on MEDLINE, Scopus, Web of Science and Google Scholar. The pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic odds ratio (DOR) with their 95% confidence intervals (95% CIs) were calculated using a random effects model. A summary receiver operating characteristic curve and the area under the curve was generated to determine the diagnostic accuracy of the AI-assisted system. Subgroup analyses by diagnostic modalities, population and AI classifiers were performed. Results We included 19 studies reporting the performances of AI-assisted ultrasonography, elastrography, computed tomography, magnetic resonance imaging and clinical parameters for the diagnosis of liver fibrosis and steatosis. For the diagnosis of liver fibrosis, the pooled sensitivity, specificity, PPV, NPV and DOR were 0.78 (0.71–0.85), 0.89 (0.81–0.94), 0.72 (0.58–0.83), 0.92 (0.88–0.94) and 31.58 (11.84–84.25), respectively, for cirrhosis; 0.86 (0.80–0.90), 0.87 (0.80–0.92), 0.85 (0.75–0.91), 0.88 (0.82–0.92) and 37.79 (16.01–89.19), respectively; for advanced fibrosis; and 0.86 (0.78–0.92), 0.81 (0.77–0.84), 0.88 (0.80–0.93), 0.77 (0.58–0.89) and 26.79 (14.47–49.62), respectively, for significant fibrosis. Subgroup analyses showed significant differences in performance for the diagnosis of fibrosis among different modalities. The pooled sensitivity, specificity, PPV, NPV and DOR were 0.97 (0.76–1.00), 0.91 (0.78–0.97), 0.95 (0.87–0.98), 0.93 (0.80–0.98) and 191.52 (38.82–944.81), respectively, for the diagnosis of liver steatosis. Conclusions AI-assisted systems have promising potential for the diagnosis of liver fibrosis and NAFLD. Validations of their performances are warranted before implementing these AI-assisted systems in clinical practice. Trial registration: The protocol was registered with PROSPERO (CRD42020183295).
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Affiliation(s)
| | - Roongruedee Chaiteerakij
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama IV Rd., Pathum Wan, Bangkok, 10330, Thailand. .,Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
| | | | - Sombat Treeprasertsuk
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama IV Rd., Pathum Wan, Bangkok, 10330, Thailand
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Wang Q, Liu H, Zhu Z, Sheng Y, Du Y, Li Y, Liu J, Zhang J, Xing W. Feasibility of T1 mapping with histogram analysis for the diagnosis and staging of liver fibrosis: Preclinical results. Magn Reson Imaging 2020; 76:79-86. [PMID: 33242591 DOI: 10.1016/j.mri.2020.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 10/10/2020] [Accepted: 11/14/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To compare the diagnostic accuracy of parameters derived from the histogram analysis of precontrast, 10-min hepatobiliary phase (HBP) and 20-min HBP T1 maps for staging liver fibrosis (LF). METHODS LF was induced in New Zealand white rabbits by subcutaneous injections of carbon tetrachloride for 4-16 weeks (n = 120), and 20 rabbits injected with saline served as controls. Precontrast, 10-min and 20-min HBP modified Look-Locker inversion recovery (MOLLI) T1 mapping was performed. Histogram analysis of T1 maps was performed, and the mean, median, skewness, kurtosis, entropy, inhomogeneity and 10th/25th/75th/90th percentiles of T1native, T110min and T120min were derived. Quantitative histogram parameters were compared. For significant parameters, further receiver operating characteristic (ROC) analyses were performed to evaluate the potential diagnostic performance in differentiating LF stages. RESULTS Finally, 17, 20, 21, 21 and 20 rabbits were included for the F0, F1, F2, F3, and F4 pathological grades of fibrosis, respectively. The mean/75th of T1native, entropy of T110min and entropy/mean/median/10th of T120min demonstrated a significant good correlation with the LF stage (|r| = 0.543-0.866, all P < 0.05). The 75th of T1native, entropy10min, and entropy20min were the three most reliable imaging markers in reflecting the stage of LF. The area under the ROC curve of entropy20min was larger than that of entropy10min (P < 0.05 for LF ≥ F2, ≥F3, and ≥ F4) and the 75th of T1native (P < 0.05 for LF ≥ F2 and ≥ F3) for staging LF. CONCLUSION Magnetic resonance histogram analysis of T1 maps, particularly the entropy derived from 20-min HBP T1 mapping, is promising for predicting the LF stage.
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Affiliation(s)
- Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou & Changzhou First People's Hospital, Jiangsu 213200, China.
| | - HaiFeng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou & Changzhou First People's Hospital, Jiangsu 213200, China
| | - ZuHui Zhu
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou & Changzhou First People's Hospital, Jiangsu 213200, China
| | - Ye Sheng
- Department of Interventional Radiology, Third Affiliated Hospital of Soochow University & Changzhou First People's Hospital, Changzhou, Jiangsu 213200, China
| | - YaNan Du
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou & Changzhou First People's Hospital, Jiangsu 213200, China
| | - YuFeng Li
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou & Changzhou First People's Hospital, Jiangsu 213200, China
| | - JianHong Liu
- Department of Pathology, The Third People's Hospital of Changzhou, Changzhou, Jiangsu 213200, China
| | | | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou & Changzhou First People's Hospital, Jiangsu 213200, China.
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Hectors SJ, Kennedy P, Huang KH, Stocker D, Carbonell G, Greenspan H, Friedman S, Taouli B. Fully automated prediction of liver fibrosis using deep learning analysis of gadoxetic acid-enhanced MRI. Eur Radiol 2020; 31:3805-3814. [PMID: 33201285 DOI: 10.1007/s00330-020-07475-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/26/2020] [Accepted: 11/05/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES To (1) develop a fully automated deep learning (DL) algorithm based on gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and (2) compare the diagnostic performance of DL vs. MR elastography (MRE) for noninvasive staging of liver fibrosis. METHODS This single-center retrospective study included 355 patients (M/F 238/117, mean age 60 years; training, n = 178; validation, n = 123; test, n = 54) who underwent gadoxetic acid-enhanced abdominal MRI, including HBP and MRE, and pathological evaluation of the liver within 1 year of MRI. Cropped liver HBP images from a custom-written fully automated liver segmentation were used as input for DL. A transfer learning approach based on the ImageNet VGG16 model was used. Different DL models were built for the prediction of fibrosis stages F1-4, F2-4, F3-4, and F4. ROC analysis was performed to evaluate the performance of DL in training, validation, and test sets and of MRE liver stiffness in the test set. RESULTS AUC values of DL were 0.99/0.70/0.77 (F1-4), 0.92/0.71/0.91 (F2-4), 0.91/0.78/0.90 (F3-4), and 0.98/0.83/0.85 (F4) for training/validation/test sets, respectively. The AUCs of MRE liver stiffness in the test set were 0.86 (F1-4), 0.87 (F2-4), 0.92 (F3-4), and 0.86 (F4). AUCs of MRE and DL were not significantly different for any of the fibrosis stages (p > 0.134). CONCLUSIONS The fully automated DL models based on HBP gadoxetic acid MRI showed good-to-excellent diagnostic performance for staging of liver fibrosis, with similar diagnostic performance to MRE. After validation in independent sets, the DL algorithm may allow for noninvasive liver fibrosis assessment without the need for additional MRI hardware. KEY POINTS • The developed deep learning algorithm, based on routine standard-of-care gadoxetic acid-enhanced MRI data, showed good-to-excellent diagnostic performance for noninvasive staging of liver fibrosis. • The diagnostic performance of the deep learning algorithm was equivalent to that of MR elastography in a separate test set.
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Affiliation(s)
- Stefanie J Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Paul Kennedy
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA
| | - Kuang-Han Huang
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Prealize Health, Palo Alto, CA, USA
| | - Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,Institute of Interventional and Diagnostic Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.,Department of Radiology, Virgen de la Arrixaca University Clinical Hospital, University of Murcia, Murcia, Spain
| | - Hayit Greenspan
- Medical Imaging Processing Lab, Faculty of Engineering, Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Scott Friedman
- Division of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave, New York, NY, 10029, USA.
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Taouli B, Alves FC. Imaging biomarkers of diffuse liver disease: current status. Abdom Radiol (NY) 2020; 45:3381-3385. [PMID: 32583139 DOI: 10.1007/s00261-020-02619-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 06/09/2020] [Accepted: 06/13/2020] [Indexed: 12/15/2022]
Abstract
We are happy to introduce this special issue of Abdominal Radiology on "diffuse liver disease". We have invited imaging experts to discuss various topics pertaining to diffuse liver disease, covering a vast array of imaging techniques including ultrasound (US), CT, MRI and new molecular imaging agents. Below, we briefly discussed the current status, limitations, and future directions of imaging biomarkers of diffuse liver disease.
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Affiliation(s)
- Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine At Mount Sinai, 1470 Madison Avenue, New York, NY, 10029, USA.
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine At Mount Sinai, New York, NY, USA.
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Ni M, Wang L, Yu H, Wen X, Yang Y, Liu G, Hu Y, Li Z. Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T 1 -Weighted Imaging: Comparison of Different Radiomics Models. J Magn Reson Imaging 2020; 53:1080-1089. [PMID: 33043991 DOI: 10.1002/jmri.27391] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/24/2020] [Accepted: 09/25/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Liver fibrosis is a common process resulting from various etiologies. Sustained progression of liver fibrosis leads to cirrhosis, even hepatocellular carcinoma. Thus, noninvasive staging of liver fibrosis is of clinical importance. Radiomics is an emerging approach for staging liver fibrosis. However, the feature selection methods and classifier models are complicated, and may result in a discrepancy of diagnostic performance owing to different radiomics models. PURPOSE To identify the optimal feature selection and classifier methods for predicting liver fibrosis by using nonenhanced T1 -weighted imaging. STUDY TYPE Prospective. ANIMAL MODEL Wistar rats, total 97. FIELD STRENGTH/SEQUENCE 3T, 3D T1 -weighted images with fast-spoiled gradient echo (FSPGR). ASSESSMENT Liver fibrosis rats were induced via subcutaneous injection of a mixture of carbon tetrachloride. Rats in the control group were injected with saline. Segmentation and feature extraction were performed by 3D slicer and the image biomarker explorer (IBEX) software package. Data preprocessing, feature selection, model building, and model comparative evaluation were conducted with Python. The liver fibrosis stage was determined by pathological examination. STATISTICAL TESTS Receiver operating characteristic curve, fuzzy comprehensive evaluation. RESULTS For discriminating between F0 and F1-2, F0 and F3-4, F0 and F1-4, F0-1 and F2-4, F0-2 and F3-4, and F0-3 and F4, the accuracies of 12 radiomics models were 77.27-90.91%, 73.33-86.67%, 80.56-91.67%, 74.07-88.89%, 76.47-88.24%, and 79.49-92.31%, respectively. The AUCs of the radiomics models were 0.86-0.97, 0.85-0.95, 0.89-0.97, 0.81-0.96, 0.82-0.93, and 0.85-0.96, respectively. The least absolute shrinkage and selection operator / support vector machine (LASSO-SVM) model had high AUCs of 0.93-0.97. For discriminating between F0 and F1-2, F0 and F3-4, F0 and F1-4, F0-1 and F2-4, and F0-2 and F3-4, the fuzzy comprehensive evaluation showed that the LASSO-SVM model had a high fuzzy score/order of 0.087-0.091/1. DATA CONCLUSION LASSO-SVM appears to be the optimal model for predicting liver fibrosis by using nonenhanced T1 -weighted imaging in a rodent model of liver fibrosis. LEVEL OF EVIDENCE 2. TECHNICAL EFFICACY STAGE 2.
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Affiliation(s)
- Ming Ni
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Lili Wang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Haiyang Yu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaoyi Wen
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, China
| | - Yinghua Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Guangzhen Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yabin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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