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Fortier V, Mohamed A, McNabb E, Dana J, Zakarian R, Levesque IR, Reinhold C. R 2* Impact on Hepatic Fat Quantification With a Commercial Single Voxel Technique at 1.5 and 3.0 T. Can Assoc Radiol J 2024; 75:838-846. [PMID: 38832642 DOI: 10.1177/08465371241255896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024] Open
Abstract
Rationale and Objectives: Fat quantification accuracy using a commercial single-voxel high speed T2-corrected multi-echo (HISTO) technique and its robustness to R2* variations at 3.0 T, such as those introduced by iron in liver, has not been fully established. This study evaluated HISTO at 3.0 T and sought to reproduce results at 1.5 T. Methods: Phantoms were prepared with a range of fat content and R2*. Data were acquired at 1.5 T and 3.0 T, using HISTO and a Dixon technique. Fat quantification accuracy was evaluated as a function of R2*. The patient study included 239 consecutive patients. Data were acquired at 1.5 T or 3.0 T, using HISTO and Dixon techniques. The techniques were compared using Bland-Altman plots. Bias significance was evaluated using a one-sample t-test. Results: In phantoms, HISTO was accurate within 10% up to a R2* of 100 s-1 at both field strengths, while Dixon was accurate within 10% where R2* was accurately quantified (up to 350 s-1 at 1.5 T, and 550 s-1 at 3.0 T). In patients, where R2* was <100 s-1, fat quantification from both techniques agreed at 1.5 T (P = .71), but not at 3.0 T (P = .007), with a bias <1%. Conclusion: Results suggest that HISTO is reliable when R2* is <100 s-1, corresponding to patients with at most mild liver iron overload, and that it should be used with caution when R2* is >100 s-1. Dixon should be preferred for hepatic fat quantification due to its robustness to R2* variations.
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Affiliation(s)
- Véronique Fortier
- Department of Medical Imaging, McGill University Health Centre, Montreal, QC, Canada
- Diagnostic Radiology, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
- Medical Physics Unit, McGill University, Montreal, QC, Canada
| | - Ahmed Mohamed
- Radiology Department, National Cancer Institute, Cairo University, Cairo, Egypt
| | - Evan McNabb
- Department of Medical Imaging, McGill University Health Centre, Montreal, QC, Canada
| | - Jérémy Dana
- Department of Medical Imaging, McGill University Health Centre, Montreal, QC, Canada
| | - Rita Zakarian
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Ives R Levesque
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
- Medical Physics Unit, McGill University, Montreal, QC, Canada
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Caroline Reinhold
- Department of Medical Imaging, McGill University Health Centre, Montreal, QC, Canada
- Diagnostic Radiology, McGill University, Montreal, QC, Canada
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Montreal Imaging Experts Inc., Montreal, QC, Canada
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Baskaya F, Lemainque T, Klinkhammer B, Koletnik S, von Stillfried S, Talbot SR, Boor P, Schulz V, Lederle W, Kiessling F. Pathophysiologic Mapping of Chronic Liver Diseases With Longitudinal Multiparametric MRI in Animal Models. Invest Radiol 2024; 59:699-710. [PMID: 38598653 DOI: 10.1097/rli.0000000000001075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
OBJECTIVES Chronic liver diseases (CLDs) have diverse etiologies. To better classify CLDs, we explored the ability of longitudinal multiparametric MRI (magnetic resonance imaging) in depicting alterations in liver morphology, inflammation, and hepatocyte and macrophage activity in murine high-fat diet (HFD)- and carbon tetrachloride (CCl 4 )-induced CLD models. MATERIALS AND METHODS Mice were either untreated, fed an HFD for 24 weeks, or injected with CCl 4 for 8 weeks. Longitudinal multiparametric MRI was performed every 4 weeks using a 7 T MRI scanner, including T1/T2 relaxometry, morphological T1/T2-weighted imaging, and fat-selective imaging. Diffusion-weighted imaging was applied to assess fibrotic remodeling and T1-weighted and T2*-weighted dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI using gadoxetic acid and ferucarbotran to target hepatocytes and the mononuclear phagocyte system, respectively. Imaging data were associated with histopathological and serological analyses. Principal component analysis and clustering were used to reveal underlying disease patterns. RESULTS The MRI parameters significantly correlated with histologically confirmed steatosis, fibrosis, and liver damage, with varying importance. No single MRI parameter exclusively correlated with 1 pathophysiological feature, underscoring the necessity for using parameter patterns. Clustering revealed early-stage, model-specific patterns. Although the HFD model exhibited pronounced liver fat content and fibrosis, the CCl 4 model indicated reduced liver fat content and impaired hepatocyte and macrophage function. In both models, MRI biomarkers of inflammation were elevated. CONCLUSIONS Multiparametric MRI patterns can be assigned to pathophysiological processes and used for murine CLD classification and progression tracking. These MRI biomarker patterns can directly be explored clinically to improve early CLD detection and differentiation and to refine treatments.
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Affiliation(s)
- Ferhan Baskaya
- From the Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany (F.B., T.L., S.K., V.S., W.L., F.K.); Department for Diagnostic and Interventional Radiology, RWTH Aachen University, Aachen, Germany (T.L.); Institute of Pathology, RWTH Aachen University, Aachen, Germany (B.K., S.S., P.B.); and Institute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany (S.R.T.)
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Wu B, Huang Z, Liang J, Yang H, Wang W, Huang S, Chen L, Huang Q. GLCV-NET: An automatic diagnosis system for advanced liver fibrosis using global-local cross view in B-mode ultrasound images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 257:108440. [PMID: 39378633 DOI: 10.1016/j.cmpb.2024.108440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 09/12/2024] [Accepted: 09/22/2024] [Indexed: 10/10/2024]
Abstract
BACKGROUND AND OBJECTIVE Advanced liver fibrosis is a critical stage in the evaluation of chronic liver disease (CLD), holding clinical significance in the development of treatment strategies and estimating the disease progression. METHODS This paper proposes an innovative Global-Local Cross-View Network (GLCV-Net) for the automatic diagnosis of advanced liver fibrosis from ultrasound (US) B-mode images. The proposed method consists of three main components: 1. A Segmentation-enhanced Global Hybrid Feature Extractor for segmenting the liver parenchyma and extracting global features; 2. A Heatmap-weighted Local Feature Extractor for selecting candidate regions and automatically identifying suspicious areas to construct local features; 3. A Scale-adaptive Fusion Module to balance the contributions of global and local scales in evaluating advanced liver fibrosis. RESULTS The predictive performance of the model was validated on an internal dataset of 1003 chronic liver disease (CLD) patients and an external dataset of 46 CLD patients, both subjected to liver fibrosis staging through pathological assessment. On the internal dataset, GLCV-Net achieved 86.9% accuracy, 85.0% recall, 85.4% precision, and 85.2% F1-score. Further validation on the external dataset confirmed its robustness, with scores of 86.1% in accuracy, 83.1% in recall, 80.8% in precision, and 81.9% in F1-score. CONCLUSION These results underscore the GLCV-Net's potential as a promising approach for non-invasively and accurately diagnosing advanced liver fibrosis in CLD patients, breaking through the limitations of traditional methods by integrating global and local information of liver fibrosis, significantly enhancing diagnostic accuracy.
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Affiliation(s)
- Bianzhe Wu
- School of Electronic and Information Engineering, South China University of Technology, 510640, China
| | - ZeRong Huang
- Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China; Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Jinglin Liang
- School of Electronic and Information Engineering, South China University of Technology, 510640, China
| | - Hong Yang
- Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Shuangping Huang
- School of Electronic and Information Engineering, South China University of Technology, 510640, China; Pazhou Laboratory, China.
| | - LiDa Chen
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
| | - Qinghua Huang
- School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China
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Park IG, Yoon SJ, Won SM, Oh KK, Hyun JY, Suk KT, Lee U. Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease. Sci Rep 2024; 14:16122. [PMID: 38997279 PMCID: PMC11245548 DOI: 10.1038/s41598-024-60768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/26/2024] [Indexed: 07/14/2024] Open
Abstract
Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725.
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Affiliation(s)
- In-Gyu Park
- Department of Electrical Engineering, Hallym University, Gyo-dong, Chuncheon, 24253, Republic of Korea
| | - Sang Jun Yoon
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, 24253, Republic of Korea
| | - Sung-Min Won
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, 24253, Republic of Korea
| | - Ki-Kwang Oh
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, 24253, Republic of Korea
| | - Ji Ye Hyun
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, 24253, Republic of Korea
| | - Ki Tae Suk
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, 24253, Republic of Korea.
- Department of Internal Medicine, Hallym University Chuncheon Sacred Heart Hospital, Hallym University, Gyo-dong, Chuncheon, 24253, Republic of Korea.
| | - Unjoo Lee
- Department of Electrical Engineering, Hallym University, Gyo-dong, Chuncheon, 24253, Republic of Korea.
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Tarchi SM, Salvatore M, Lichtenstein P, Sekar T, Capaccione K, Luk L, Shaish H, Makkar J, Desperito E, Leb J, Navot B, Goldstein J, Laifer S, Beylergil V, Ma H, Jambawalikar S, Aberle D, D'Souza B, Bentley-Hibbert S, Marin MP. Radiology of fibrosis part II: abdominal organs. J Transl Med 2024; 22:610. [PMID: 38956593 PMCID: PMC11218138 DOI: 10.1186/s12967-024-05346-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 05/25/2024] [Indexed: 07/04/2024] Open
Abstract
Fibrosis is the aberrant process of connective tissue deposition from abnormal tissue repair in response to sustained tissue injury caused by hypoxia, infection, or physical damage. It can affect almost all organs in the body causing dysfunction and ultimate organ failure. Tissue fibrosis also plays a vital role in carcinogenesis and cancer progression. The early and accurate diagnosis of organ fibrosis along with adequate surveillance are helpful to implement early disease-modifying interventions, important to reduce mortality and improve quality of life. While extensive research has already been carried out on the topic, a thorough understanding of how this relationship reveals itself using modern imaging techniques has yet to be established. This work outlines the ways in which fibrosis shows up in abdominal organs and has listed the most relevant imaging technologies employed for its detection. New imaging technologies and developments are discussed along with their promising applications in the early detection of organ fibrosis.
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Affiliation(s)
- Sofia Maria Tarchi
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
| | - Mary Salvatore
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Philip Lichtenstein
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Thillai Sekar
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Kathleen Capaccione
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Lyndon Luk
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Hiram Shaish
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jasnit Makkar
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Elise Desperito
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jay Leb
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Benjamin Navot
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jonathan Goldstein
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sherelle Laifer
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Volkan Beylergil
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Hong Ma
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Dwight Aberle
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Belinda D'Souza
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
| | | | - Monica Pernia Marin
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA
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6
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Chen S, Zhuang D, Jia Q, Guo B, Hu G. Advances in Noninvasive Molecular Imaging Probes for Liver Fibrosis Diagnosis. Biomater Res 2024; 28:0042. [PMID: 38952717 PMCID: PMC11214848 DOI: 10.34133/bmr.0042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/08/2024] [Indexed: 07/03/2024] Open
Abstract
Liver fibrosis is a wound-healing response to chronic liver injury, which may lead to cirrhosis and cancer. Early-stage fibrosis is reversible, and it is difficult to precisely diagnose with conventional imaging modalities such as magnetic resonance imaging, positron emission tomography, single-photon emission computed tomography, and ultrasound imaging. In contrast, probe-assisted molecular imaging offers a promising noninvasive approach to visualize early fibrosis changes in vivo, thus facilitating early diagnosis and staging liver fibrosis, and even monitoring of the treatment response. Here, the most recent progress in molecular imaging technologies for liver fibrosis is updated. We start by illustrating pathogenesis for liver fibrosis, which includes capillarization of liver sinusoidal endothelial cells, cellular and molecular processes involved in inflammation and fibrogenesis, as well as processes of collagen synthesis, oxidation, and cross-linking. Furthermore, the biological targets used in molecular imaging of liver fibrosis are summarized, which are composed of receptors on hepatic stellate cells, macrophages, and even liver collagen. Notably, the focus is on insights into the advances in imaging modalities developed for liver fibrosis diagnosis and the update in the corresponding contrast agents. In addition, challenges and opportunities for future research and clinical translation of the molecular imaging modalities and the contrast agents are pointed out. We hope that this review would serve as a guide for scientists and students who are interested in liver fibrosis imaging and treatment, and as well expedite the translation of molecular imaging technologies from bench to bedside.
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Affiliation(s)
- Shaofang Chen
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Danping Zhuang
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Qingyun Jia
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Bing Guo
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application,
Harbin Institute of Technology, Shenzhen 518055, China
| | - Genwen Hu
- Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College,
Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
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Yao J, Ertl-Wagner BB, Dana J, Hanneman K, Kashif Al-Ghita M, Liu L, McInnes MDF, Nicolaou S, Reinhold C, Patlas MN. Canadian radiology: 2024 update. Diagn Interv Imaging 2024:S2211-5684(24)00140-2. [PMID: 38942638 DOI: 10.1016/j.diii.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/30/2024]
Abstract
Radiology in Canada is advancing through innovations in clinical practices and research methodologies. Recent developments focus on refining evidence-based practice guidelines, exploring innovative imaging techniques and enhancing diagnostic processes through artificial intelligence. Within the global radiology community, Canadian institutions play an important role by engaging in international collaborations, such as with the American College of Radiology to refine implementation of the Ovarian-Adnexal Reporting and Data System for ultrasound and magnetic resonance imaging. Additionally, researchers have participated in multidisciplinary collaborations to evaluate the performance of artificial intelligence-driven diagnostic tools for chronic liver disease and pediatric brain tumors. Beyond clinical radiology, efforts extend to addressing gender disparities in the field, improving educational practices, and enhancing the environmental sustainability of radiology departments. These advancements highlight Canada's role in the global radiology community, showcasing a commitment to improving patient outcomes and advancing the field through research and innovation. This update underscores the importance of continued collaboration and innovation to address emerging challenges and further enhance the quality and efficacy of radiology practices worldwide.
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Affiliation(s)
- Jason Yao
- Department of Radiology, McMaster University, Hamilton, ON L8S4K1, Canada.
| | - Birgit B Ertl-Wagner
- Department of Diagnostic Imaging, Division of Neuroradiology, the Hospital for Sick Children, Toronto, ON M5G1X8, Canada; Department of Medical Imaging, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S1A8, Canada
| | - Jérémy Dana
- Department of Radiology, McGill University Health Centre, McGill University, Montreal, QC H3G1A4, Canada
| | - Kate Hanneman
- Department of Medical Imaging, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S1A8, Canada; University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network (UHN), Toronto, ON M5G1X6, Canada
| | | | - Lulu Liu
- Department of Radiology, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z1M9, Canada
| | - Matthew D F McInnes
- Faculty of Medicine, University of Ottawa, Ottawa, ON K1H8M5, Canada; Departments of Radiology and Epidemiology, University of Ottawa, Ottawa, ON K1H8L6, Canada; The Ottawa Hospital Research Institute, Clinical Epidemiology Program, Ottawa, ON K1H8L6, Canada
| | - Savvas Nicolaou
- Department of Radiology, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z1M9, Canada
| | - Caroline Reinhold
- Department of Radiology, McGill University Health Centre, McGill University, Montreal, QC H3G1A4, Canada
| | - Michael N Patlas
- Department of Medical Imaging, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5S1A8, Canada; University Medical Imaging Toronto, Joint Department of Medical Imaging, University Health Network (UHN), Toronto, ON M5G1X6, Canada
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8
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Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, Shu D, Chen K, Wang Y, Chen G. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med 2024; 173:108337. [PMID: 38547656 DOI: 10.1016/j.compbiomed.2024.108337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.
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Affiliation(s)
- Zhiyuan Bo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiatao Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qikuan He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ziyan Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Danyang Shu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kaiyu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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9
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Chen LD, Huang ZR, Yang H, Cheng MQ, Hu HT, Lu XZ, Li MD, Lu RF, He DN, Lin P, Ma QP, Huang H, Ruan SM, Ke WP, Liao B, Zhong BH, Ren J, Lu MD, Xie XY, Wang W. US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening. Radiology 2024; 311:e231461. [PMID: 38652028 DOI: 10.1148/radiol.231461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.
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Affiliation(s)
- Li-Da Chen
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Ze-Rong Huang
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Hong Yang
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Mei-Qing Cheng
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Hang-Tong Hu
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Xiao-Zhou Lu
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Ming-De Li
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Rui-Fang Lu
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Dan-Ni He
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Peng Lin
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Qiu-Ping Ma
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Hui Huang
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Si-Min Ruan
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Wei-Ping Ke
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Bing Liao
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Bi-Hui Zhong
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Jie Ren
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Ming-De Lu
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Xiao-Yan Xie
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
| | - Wei Wang
- From the Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound (L.D.C., Z.R.H., M.Q.C., H.T.H., M.D. Li, R.F.L., H.H., S.M.R., W.P.K., M.D. Lu, X.Y.X., W.W.), Department of Traditional Chinese Medicine (X.Z.L.), Department of Pathology (B.L.), Department of Gastroenterology (B.H.Z.), and Department of Hepatobiliary Surgery (M.D. Lu), the First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Rd 2, Guangzhou 510080, People's Republic of China; Department of Medical Ultrasound, the First Affiliated Hospital of Guangxi Medical University, Nanning, People's Republic of China (H.Y., P.L.); Department of Medical Ultrasonics, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, People's Republic of China (D.N.H.); and Department of Medical Ultrasonics, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China (Q.P.M., J.R.)
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Kazi IN, Kuo L, Tsai E. Noninvasive Methods for Assessing Liver Fibrosis and Steatosis. Gastroenterol Hepatol (N Y) 2024; 20:21-29. [PMID: 38405045 PMCID: PMC10885415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Accurate diagnosis and staging of liver fibrosis is crucial to the individualized management of patients with chronic liver disease. Liver biopsy remains the reference standard for the assessment of steatosis, necroinflammation, and fibrosis. However, over the past decade, there has been an exponential growth in noninvasive tests (NITs) designed to assess liver fibrosis and steatosis. These NITs range from serum biomarkers to imaging assessments of liver tissue stiffness. Current noninvasive methods overcome the limitations of non-specific laboratory markers, conventional imaging, and invasive procedures, and are now starting to be adopted. The Fibrosis-4 index, Enhanced Liver Fibrosis test, and elastography have gained the strongest clinical footholds for the diagnosis of advanced fibrosis. There remains significant interest in demonstrating superiority of any specific test or, alternatively, optimizing a sequential algorithm to provide the most accurate diagnosis of fibrosis staging. This article reviews currently available noninvasive methods for assessing liver fibrosis and steatosis.
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Affiliation(s)
| | - Lily Kuo
- UT Health San Antonio, San Antonio, Texas
| | - Eugenia Tsai
- UT Health San Antonio, San Antonio, Texas
- Texas Liver Institute, San Antonio, Texas
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11
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Nashwan AJ, Alkhawaldeh IM, Shaheen N, Albalkhi I, Serag I, Sarhan K, Abujaber AA, Abd-Alrazaq A, Yassin MA. Using artificial intelligence to improve body iron quantification: A scoping review. Blood Rev 2023; 62:101133. [PMID: 37748945 DOI: 10.1016/j.blre.2023.101133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/27/2023]
Abstract
This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.
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Affiliation(s)
- Abdulqadir J Nashwan
- Department of Nursing, Hazm Mebaireek General Hospital, Hamad Medical Corporation, Doha, Qatar; Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.
| | | | - Nour Shaheen
- Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Ibrahem Albalkhi
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia; Department of Neuroradiology, Great Ormond Street Hospital NHS Foundation Trust, Great Ormond St, London WC1N 3JH, United Kingdom.
| | - Ibrahim Serag
- Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Khalid Sarhan
- Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Ahmad A Abujaber
- Department of Nursing, Hazm Mebaireek General Hospital, Hamad Medical Corporation, Doha, Qatar.
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
| | - Mohamed A Yassin
- Hematology and Oncology, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar.
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12
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Hong Y, Yu Q, Mo F, Yin M, Xu C, Zhu S, Lin J, Xu G, Gao J, Liu L, Wang Y. Deep learning to predict esophageal variceal bleeding based on endoscopic images. J Int Med Res 2023; 51:3000605231200371. [PMID: 37818651 PMCID: PMC10566287 DOI: 10.1177/03000605231200371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/24/2023] [Indexed: 10/12/2023] Open
Abstract
OBJECTIVE Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.
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Affiliation(s)
- Yu Hong
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qianqian Yu
- Department of Oncology, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
| | - Feng Mo
- Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guoting Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yu Wang
- Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Jintan, China
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13
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Lew D, Klang E, Soffer S, Morgenthau AS. Current Applications of Artificial Intelligence in Sarcoidosis. Lung 2023; 201:445-454. [PMID: 37730926 DOI: 10.1007/s00408-023-00641-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/15/2023] [Indexed: 09/22/2023]
Abstract
PURPOSE Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis. METHODS Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded. CONCLUSIONS Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.
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Affiliation(s)
- Dana Lew
- Division of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eyal Klang
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel
| | - Shelly Soffer
- Division of Internal Medicine, Assuta Medical Center, Ashdod, Israel
| | - Adam S Morgenthau
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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14
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Zhao D, Wang W, Tang T, Zhang YY, Yu C. Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review. Comput Struct Biotechnol J 2023; 21:3315-3326. [PMID: 37333860 PMCID: PMC10275698 DOI: 10.1016/j.csbj.2023.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 05/28/2023] [Accepted: 05/28/2023] [Indexed: 06/20/2023] Open
Abstract
Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.
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Affiliation(s)
- Dan Zhao
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Wei Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Tian Tang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Ying-Ying Zhang
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
| | - Chen Yu
- Department of Nephrology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China
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15
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Popa SL, Ismaiel A, Abenavoli L, Padureanu AM, Dita MO, Bolchis R, Munteanu MA, Brata VD, Pop C, Bosneag A, Dumitrascu DI, Barsan M, David L. Diagnosis of Liver Fibrosis Using Artificial Intelligence: A Systematic Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59050992. [PMID: 37241224 DOI: 10.3390/medicina59050992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/04/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023]
Abstract
Background and Objectives: The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for improving the accuracy of diagnosis, involving large sets of clinical data. For this reason, the aim of this systematic review is to provide a comprehensive overview of current AI applications and analyze the accuracy of these systems to perform an automated diagnosis of liver fibrosis. Materials and Methods: We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of diagnosing liver fibrosis. Exclusion criteria were animal studies, case reports, abstracts, letters to the editor, conference presentations, pediatric studies, studies written in languages other than English, and editorials. Results: Our search identified a total of 24 articles analyzing the automated imagistic diagnosis of liver fibrosis, out of which six studies analyze liver ultrasound images, seven studies analyze computer tomography images, five studies analyze magnetic resonance images, and six studies analyze liver biopsies. The studies included in our systematic review showed that AI-assisted non-invasive techniques performed as accurately as human experts in detecting and staging liver fibrosis. Nevertheless, the findings of these studies need to be confirmed through clinical trials to be implemented into clinical practice. Conclusions: The current systematic review provides a comprehensive analysis of the performance of AI systems in diagnosing liver fibrosis. Automatic diagnosis, staging, and risk stratification for liver fibrosis is currently possible considering the accuracy of the AI systems, which can overcome the limitations of non-invasive diagnosis methods.
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Affiliation(s)
- Stefan Lucian Popa
- 2nd Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Abdulrahman Ismaiel
- 2nd Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Ludovico Abenavoli
- Department of Health Sciences, University "Magna Graecia", 88100 Catanzaro, Italy
| | | | - Miruna Oana Dita
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Roxana Bolchis
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Mihai Alexandru Munteanu
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410087 Oradea, Romania
| | - Vlad Dumitru Brata
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Cristina Pop
- Department of Pharmacology, Physiology, and Pathophysiology, Faculty of Pharmacy, Iuliu Hatieganu University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Andrei Bosneag
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, UMF "Iuliu Hatieganu" Cluj-Napoca, 400000 Cluj-Napoca, Romania
| | - Maria Barsan
- Department of Occupational Health, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
| | - Liliana David
- 2nd Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania
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16
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Qian J, Li H, Wang J, He L. Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging. Diagnostics (Basel) 2023; 13:1571. [PMID: 37174962 PMCID: PMC10178221 DOI: 10.3390/diagnostics13091571] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/29/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as "black boxes". There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications.
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Affiliation(s)
- Jinzhao Qian
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
- Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA
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Wang Y, Hong Y, Wang Y, Zhou X, Gao X, Yu C, Lin J, Liu L, Gao J, Yin M, Xu G, Liu X, Zhu J. Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data. J Digit Imaging 2023; 36:326-338. [PMID: 36279027 PMCID: PMC9984604 DOI: 10.1007/s10278-022-00724-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 11/09/2022] Open
Abstract
Esophageal variceal (EV) bleeding is a severe medical emergency related to cirrhosis. Early identification of cirrhotic patients with at a high risk of EV bleeding is key to improving outcomes and optimizing medical resources. This study aimed to evaluate the feasibility of automated multimodal machine learning (MMML) for predicting EV bleeding by integrating endoscopic images and clinical structured data. This study mainly includes three steps: step 1, developing deep learning (DL) models using EV images by 12-month bleeding on TensorFlow (backbones include ResNet, Xception, EfficientNet, ViT and ConvMixer); step 2, training and internally validating MMML models integrating clinical structured data and DL model outputs to predict 12-month EV bleeding on an H2O-automated machine learning platform (algorithms include DL, XGBoost, GLM, GBM, RF, and stacking); and step 3, externally testing MMML models. Furthermore, existing clinical indices, e.g., the MELD score, Child‒Pugh score, APRI, and FIB-4, were also examined. Five DL models were transfer learning to the binary classification of EV endoscopic images at admission based on the occurrence or absence of bleeding events during the 12-month follow-up. An EfficientNet model achieved the highest accuracy of 0.868 in the validation set. Then, a series of MMML models, integrating clinical structured data and the output of the EfficientNet model, were automatedly trained to predict 12-month EV bleeding. A stacking model showed the highest accuracy (0.932), sensitivity (0.952), and F1-score (0.879) in the test dataset, which was also better than the existing indices. This study is the first to evaluate the feasibility of automated MMML in predicting 12-month EV bleeding based on endoscopic images and clinical variables.
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Affiliation(s)
- Yu Wang
- Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Changzhou, China
| | - Yu Hong
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Yue Wang
- Department of Hepatology, The Fifth People's Hospital of Suzhou, Suzhou, 215000, China
| | - Xin Zhou
- Department of Gastroenterology, Jintan Affiliated Hospital of Jiangsu University, Changzhou, China
| | - Xin Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Chenyan Yu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Guoting Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China.
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215000, China.
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18
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Wang L, Zhang L, Jiang B, Zhao K, Zhang Y, Xie X. Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review. Br J Radiol 2022; 95:20211136. [PMID: 35816550 PMCID: PMC10162062 DOI: 10.1259/bjr.20211136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 04/26/2022] [Accepted: 07/05/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (MR) imaging. METHODS We searched PubMed and Web of Science for publications, using terms related to deep learning, radiomics, imaging methods (CT or MR), and the liver. Two reviewers independently selected articles and extracted data from each eligible article. The Quality Assessment of Diagnostic Accuracy Studies-AI (QUADAS-AI) tool was used to assess the risk of bias and concerns regarding applicability. RESULTS The screening identified 45 high-quality publications from 235 candidates, including 8 on diffuse liver diseases and 37 on focal liver lesions. Nine studies used deep learning and 36 studies used radiomics. All 45 studies were rated as low risk of bias in patient selection and workflow, but 36 (80%) were rated as high risk of bias in the index test because they lacked external validation. In terms of concerns regarding applicability, all 45 studies were rated as low concerns. These studies demonstrated that deep learning and radiomics can evaluate liver fibrosis, cirrhosis, portal hypertension, and a series of complications caused by cirrhosis, predict the prognosis of malignant hepatic tumors, and differentiate focal hepatic lesions. CONCLUSIONS The latest studies have shown that deep learning and radiomics based on hepatic CT and MR imaging have potential application value in the diagnosis, treatment evaluation, and prognosis prediction of common liver diseases. The AI methods may become useful tools to support clinical decision-making in the future. ADVANCES IN KNOWLEDGE Deep learning and radiomics have shown their potential in the diagnosis, treatment evaluation, and prognosis prediction of a series of common diffuse liver diseases and focal liver lesions.
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Affiliation(s)
- Lingyun Wang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Keke Zhao
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yaping Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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19
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Bhat M, Rabindranath M. The promise of artificial intelligence for predictive biomarkers in hepatology. Hepatol Int 2022; 16:523-525. [PMID: 35575965 DOI: 10.1007/s12072-022-10342-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/13/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
- Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
| | - Madhumitha Rabindranath
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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