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Li H, Alves VV, Pednekar A, Manhard MK, Greer J, Trout AT, He L, Dillman JR. Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features. J Comput Assist Tomogr 2024:00004728-990000000-00352. [PMID: 39190703 DOI: 10.1097/rct.0000000000001648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
OBJECTIVE This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques. METHODS Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses. RESULTS According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues (P < 0.001). CONCLUSIONS MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.
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Zheng T, Qu Y, Chen J, Yang J, Yan H, Jiang H, Song B. Noninvasive diagnosis of liver cirrhosis: qualitative and quantitative imaging biomarkers. Abdom Radiol (NY) 2024; 49:2098-2115. [PMID: 38372765 DOI: 10.1007/s00261-024-04225-8] [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: 10/30/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/20/2024]
Abstract
A diagnosis of cirrhosis initiates a shift in the management of chronic liver disease and affects the diagnostic workflow and treatment decision of primary liver cancer. Liver biopsy remains the gold standard for cirrhosis diagnosis, but it is invasive and susceptible to sampling bias and observer variability. Various qualitative and quantitative imaging biomarkers based on ultrasound, CT and MRI have been proposed for noninvasive diagnosis of cirrhosis. Qualitative imaging features are easy to apply but have moderate diagnostic sensitivity. Elastography techniques allow quantitative assessment of liver stiffness and are highly accurate for cirrhosis diagnosis. Ultrasound elastography are widely used in clinical practice, while MR elastography has narrower availability. Although not applicable in clinical practice yet, other quantitative imaging features, including liver surface nodularity, linear and volumetric measurement, extracellular volume fraction, liver enhancement on hepatobiliary phase, and parameters derived from diffusion-weighted imaging, can provide additional information of liver morphology, perfusion, and function, thus may increase diagnosis performance. The introduction of radiomics and deep learning has further improved diagnostic accuracy while reducing subjectivity. Several imaging features may also help to assess liver function and outcomes in patients with cirrhosis. In this review, we summarize the qualitative and quantitative imaging biomarkers for noninvasive cirrhosis diagnosis, and the assessment of liver function and outcomes, and discuss the challenges and future directions in this field.
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Affiliation(s)
- Tianying Zheng
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China
| | - Yali Qu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China
| | - Jie Chen
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China
| | - Jie Yang
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hualin Yan
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, Sichuan, 610041, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan, Chengdu, Sichuan, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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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] [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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Fu H, Shen Z, Lai R, Zhou T, Huang Y, Zhao S, Mo R, Cai M, Jiang S, Wang J, Du B, Qian C, Chen Y, Yan F, Xiang X, Li R, Xie Q. Clinic-radiomics model using liver magnetic resonance imaging helps predict chronicity of drug-induced liver injury. Hepatol Int 2023; 17:1626-1636. [PMID: 37188998 DOI: 10.1007/s12072-023-10539-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 04/08/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND AND AIMS Some drug-induced liver injury (DILI) cases may become chronic, even after drug withdrawal. Radiomics can predict liver disease progression. We established and validated a predictive model incorporating the clinical characteristics and radiomics features for predicting chronic DILI. METHODS One hundred sixty-eight DILI patients who underwent liver gadolinium-diethylenetriamine pentaacetate-enhanced magnetic resonance imaging were recruited. The patients were clinically diagnosed using the Roussel Uclaf causality assessment method. Patients who progressed to chronicity or recovery were randomly divided into the training (70%) and validation (30%) cohorts, respectively. Hepatic T1-weighted images were segmented to extract 1672 radiomics features. Least absolute shrinkage and selection operator regression was used for feature selection, and Rad-score was constructed using support vector machines. Multivariable logistic regression analysis was performed to build a clinic-radiomics model incorporating clinical characteristics and Rad-scores. The clinic-radiomics model was evaluated for its discrimination, calibration, and clinical usefulness in the independent validation set. RESULTS Of 1672 radiomics features, 28 were selected to develop the Rad-score. Cholestatic/mixed patterns and Rad-score were independent risk factors of chronic DILI. The clinic-radiomics model, including the Rad-score and injury patterns, distinguished chronic from recovered DILI patients in the training (area under the receiver operating characteristic curve [AUROC]: 0.89, 95% confidence interval [95% CI]: 0.87-0.92) and validation (AUROC: 0.88, 95% CI: 0.83-0.91) cohorts with good calibration and great clinical utility. CONCLUSION The clinic-radiomics model yielded sufficient accuracy for predicting chronic DILI, providing a practical and non-invasive tool for managing DILI patients.
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Affiliation(s)
- Haoshuang Fu
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhehan Shen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rongtao Lai
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Tianhui Zhou
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yan Huang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shuang Zhao
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Ruidong Mo
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Minghao Cai
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Shaowen Jiang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jiexiao Wang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Bingying Du
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Cong Qian
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yaoxing Chen
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaogang Xiang
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Qing Xie
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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Li Z, Li H, Ralescu AL, Dillman JR, Parikh NA, He L. A novel collaborative self-supervised learning method for radiomic data. Neuroimage 2023; 277:120229. [PMID: 37321358 PMCID: PMC10440826 DOI: 10.1016/j.neuroimage.2023.120229] [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: 03/19/2023] [Revised: 05/19/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on labeling radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity of information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method will have the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.
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Affiliation(s)
- Zhiyuan Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Anca L Ralescu
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, U niversity of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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Zhang CY, Liu S, Yang M. Treatment of liver fibrosis: Past, current, and future. World J Hepatol 2023; 15:755-774. [PMID: 37397931 PMCID: PMC10308286 DOI: 10.4254/wjh.v15.i6.755] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/01/2023] [Accepted: 04/18/2023] [Indexed: 06/25/2023] Open
Abstract
Liver fibrosis accompanies the progression of chronic liver diseases independent of etiologies, such as hepatitis viral infection, alcohol consumption, and metabolic-associated fatty liver disease. It is commonly associated with liver injury, inflammation, and cell death. Liver fibrosis is characterized by abnormal accumulation of extracellular matrix components that are expressed by liver myofibroblasts such as collagens and alpha-smooth actin proteins. Activated hepatic stellate cells contribute to the major population of myofibroblasts. Many treatments for liver fibrosis have been investigated in clinical trials, including dietary supplementation (e.g., vitamin C), biological treatment (e.g., simtuzumab), drug (e.g., pegbelfermin and natural herbs), genetic regulation (e.g., non-coding RNAs), and transplantation of stem cells (e.g., hematopoietic stem cells). However, none of these treatments has been approved by Food and Drug Administration. The treatment efficacy can be evaluated by histological staining methods, imaging methods, and serum biomarkers, as well as fibrosis scoring systems, such as fibrosis-4 index, aspartate aminotransferase to platelet ratio, and non-alcoholic fatty liver disease fibrosis score. Furthermore, the reverse of liver fibrosis is slowly and frequently impossible for advanced fibrosis or cirrhosis. To avoid the life-threatening stage of liver fibrosis, anti-fibrotic treatments, especially for combined behavior prevention, biological treatment, drugs or herb medicines, and dietary regulation are needed. This review summarizes the past studies and current and future treatments for liver fibrosis.
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Affiliation(s)
- Chun-Ye Zhang
- Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, United States
| | - Shuai Liu
- Department of Radiology,The First Affiliated Hospital, Zhejiang University, Hangzhou 310006, Zhejiang Province, China
| | - Ming Yang
- Department of Surgery, University of Missouri, Columbia, MO 65211, United States
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Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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Yang Y, Zhang X, Zhao L, Mao H, Cai TN, Guo WL. Development of an MRI-Based Radiomics-Clinical Model to Diagnose Liver Fibrosis Secondary to Pancreaticobiliary Maljunction in Children. J Magn Reson Imaging 2022. [PMID: 36583731 DOI: 10.1002/jmri.28586] [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: 09/16/2022] [Revised: 12/09/2022] [Accepted: 12/10/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Preoperative diagnosis of liver fibrosis in children with pancreaticobiliary maljunction (PBM) is needed to guide clinical decision-making and improve patient prognosis. PURPOSE To develop and validate an MR-based radiomics-clinical nomogram for identifying liver fibrosis in children with PBM. STUDY TYPE Retrospective. POPULATION A total of 136 patients with PBM from two centers (center A: 111 patients; center B: 25 patients). Cases from center A were randomly divided into training (74 patients) and internal validation (37 patients) sets. Cases from center B were assigned to the external validation set. Liver fibrosis was determined by histopathological examination. FIELD STRENGTH/SEQUENCE A 3.0 T (two vendors)/T1-weighted imaging and T2-weighted imaging. ASSESSMENT Clinical factors associated with liver fibrosis were evaluated. A total of 3562 radiomics features were extracted from segmented liver parenchyma. Maximum relevance minimum redundancy and least absolute shrinkage and selection operator were recruited to screen radiomics features. Based on the selected variables, multivariate logistic regression was used to construct the clinical model, radiomics model, and combined model. The combined model was visualized as a nomogram to show the impact of the radiomics signature and key clinical factors on the individual risk of developing liver fibrosis. STATISTICAL TESTS Mann-Whitney U and chi-squared tests were used to compare clinical factors. P < 0.05 was considered statistically significant in the final models. RESULTS Two clinical factors and four radiomics features were selected as they were associated with liver fibrosis in the training (AUC, 0.723, 0.927), internal validation (AUC, 0.718, 0.885), and external validation (AUC, 0.737, 0.865) sets. The radiomics-clinical nomogram yielded the best performance in the training (AUC, 0.977), internal validation (AUC, 0.921), and external validation (AUC, 0.878) sets, with good calibration (P > 0.05). DATA CONCLUSION Our radiomic-based nomogram is a noninvasive, accurate, and preoperative diagnostic tool that is able to detect liver fibrosis in PBM children. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Yang Yang
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
| | - Xinxian Zhang
- Department of Radiology, Xuzhou Children's Hospital, Xuzhou, China
| | - Lian Zhao
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
| | - Huimin Mao
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
| | - Tian-Na Cai
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
| | - Wan-Liang Guo
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, China
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Artificial Intelligence in NAFLD: Will Liver Biopsy Still Be Necessary in the Future? Healthcare (Basel) 2022; 11:healthcare11010117. [PMID: 36611577 PMCID: PMC9818843 DOI: 10.3390/healthcare11010117] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [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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Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1147111. [PMID: 36619303 PMCID: PMC9812615 DOI: 10.1155/2022/1147111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [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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Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen. J Imaging 2022; 8:jimaging8100277. [PMID: 36286371 PMCID: PMC9605113 DOI: 10.3390/jimaging8100277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 12/03/2022] Open
Abstract
Background: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients with cirrhosis to those without cirrhosis. Methods: This retrospective study compared MR-derived radiomic features between patients with cirrhosis undergoing hepatocellular carcinoma screening and patients without cirrhosis undergoing intraductal papillary mucinous neoplasm surveillance between 2015 and 2018 using the same imaging protocol. Secondary analyses stratified the cirrhosis cohort by liver disease severity using clinical compensation/decompensation and Model for End-Stage Liver Disease (MELD). Results: Of 167 patients, 90 had cirrhosis with 68.9% compensated and median MELD 8. Combined liver and spleen radiomic features generated an AUC 0.94 for detecting cirrhosis, with shape and texture components contributing more than size. Discrimination of cirrhosis remained high after stratification by liver disease severity. Conclusions: MR-based liver and spleen radiomic features had high accuracy in identifying cirrhosis, after stratification by clinical compensation/decompensation and MELD. Shape and texture features performed better than size features. These findings will inform radiomic-based applications for cirrhosis diagnosis and severity.
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12
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Dillman JR, Somasundaram E, Brady SL, He L. Current and emerging artificial intelligence applications for pediatric abdominal imaging. Pediatr Radiol 2022; 52:2139-2148. [PMID: 33844048 DOI: 10.1007/s00247-021-05057-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 01/25/2021] [Accepted: 03/16/2021] [Indexed: 12/12/2022]
Abstract
Artificial intelligence (AI) uses computers to mimic cognitive functions of the human brain, allowing inferences to be made from generally large datasets. Traditional machine learning (e.g., decision tree analysis, support vector machines) and deep learning (e.g., convolutional neural networks) are two commonly employed AI approaches both outside and within the field of medicine. Such techniques can be used to evaluate medical images for the purposes of automated detection and segmentation, classification tasks (including diagnosis, lesion or tissue characterization, and prediction), and image reconstruction. In this review article we highlight recent literature describing current and emerging AI methods applied to abdominal imaging (e.g., CT, MRI and US) and suggest potential future applications of AI in the pediatric population.
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Affiliation(s)
- Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA. .,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Elan Somasundaram
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Samuel L Brady
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH, 45229, USA.,Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Registered Trials of Artificial Intelligence Conducted on Chronic Liver Disease: A Cross-Sectional Study on ClinicalTrials.gov. DISEASE MARKERS 2022; 2022:6847073. [PMID: 36193490 PMCID: PMC9526577 DOI: 10.1155/2022/6847073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 11/24/2022]
Abstract
Background Artificial intelligence (AI) has been widely applied in the diagnosis and therapy of chronic liver disease (CLD), but there is currently little insight into the trials registered on ClinicalTrials.gov. Thus, this cross-sectional study was focused on analyzing the progress in the use of AI in CLD. Methods Registered trials of AI applied in CLD on ClinicalTrials.gov were searched firstly. All available information was downloaded to Excel (Microsoft Excel, Rong, Rong, China), and duplicates were removed. We extracted the data of the included trials, then analyzed the characteristics of them finally. Results Up to the 27th of May 2021, 6835 trials were identified following an initial search, and 20 registered trials were included after screening for inclusion and exclusion criteria. Among those trials, hepatocellular carcinoma (HCC, 40.0%) and nonalcoholic fatty liver disease (NAFLD, 20.0%) were the most widely applied CLDs for AI. Trials started in 2013 until 2021, with 17 trials (85%) registered after 2016. There was a large trend in trial enrolment, with 40% of them including samples more than 500. Five trials (25%) have been completed, but only one of these had available results. The most frequent sponsors and collaborators were both hospitals at 55%, followed by universities at 35% and institutes at 11%, respectively. Of the 20 trials included, 35% (7 trials) were interventional trials and 65% (13 trials) were observational trials. Among 7 interventional trials, most trials were for diagnosis purpose (42.86%, 3 trials); 4 trials (57.14%) were randomized; 3 trials (42.86%) applied behavioral intervention, 1 trial (14.29%) was in device intervention, 2 trials (28.57%) were in diagnostic test, and 1 trial intervention was unknown. Among 13 observational trials, 8 (61.54%) were cohort studies; 6 (46.15%) were prospective studies, 4 (30.77%) were retrospective studies, 2 (15.38%) were cross-sectional studies, and 1 (7.69%) did not involve a temporal perspective. Conclusion The study is the first to focus on AI registration trials in CLD, which will aid relevant scholars in understanding the current state of the subject. This study demonstrates that additional research on AI used in the diagnosis and treatment of CLD is required, and timely publication of accessible results from registered trials is essential.
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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] [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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15
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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: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [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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16
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Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis. Can J Gastroenterol Hepatol 2022; 2022:2249447. [PMID: 35775068 PMCID: PMC9239804 DOI: 10.1155/2022/2249447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [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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Dana J, Venkatasamy A, Saviano A, Lupberger J, Hoshida Y, Vilgrain V, Nahon P, Reinhold C, Gallix B, Baumert TF. Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease. Hepatol Int 2022; 16:509-522. [PMID: 35138551 PMCID: PMC9177703 DOI: 10.1007/s12072-022-10303-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/17/2022] [Indexed: 12/14/2022]
Abstract
Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.
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Affiliation(s)
- Jérémy Dana
- Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France.
- Institut Hospitalo-Universitaire (IHU), Strasbourg, France.
- Université de Strasbourg, Strasbourg, France.
- Department of Diagnostic Radiology, McGill University, Montreal, Canada.
| | - Aïna Venkatasamy
- Institut Hospitalo-Universitaire (IHU), Strasbourg, France
- Streinth Lab (Stress Response and Innovative Therapies), Inserm UMR_S 1113 IRFAC, Interface Recherche Fondamentale et Appliquée à la Cancérologie, 3 Avenue Moliere, Strasbourg, France
- Department of Radiology Medical Physics, Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Killianstrasse 5a, 79106, Freiburg, Germany
| | - Antonio Saviano
- Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France
- Université de Strasbourg, Strasbourg, France
- Pôle Hépato-Digestif, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Joachim Lupberger
- Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France
- Université de Strasbourg, Strasbourg, France
| | - Yujin Hoshida
- Liver Tumor Translational Research Program, Division of Digestive and Liver Diseases, Department of Internal Medicine, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, USA
| | - Valérie Vilgrain
- Radiology Department, Hôpital Beaujon, Université de Paris, CRI, INSERM 1149, APHP. Nord, Paris, France
| | - Pierre Nahon
- Liver Unit, Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Paris Seine Saint-Denis, Bobigny, France
- Université Sorbonne Paris Nord, 93000, Bobigny, France
- Inserm, UMR-1138 "Functional Genomics of Solid Tumors", Paris, France
| | - Caroline Reinhold
- Department of Diagnostic Radiology, McGill University, Montreal, Canada
- Augmented Intelligence and Precision Health Laboratory, Research Institute of McGill University Health Centre, Montreal, Canada
- Montreal Imaging Experts Inc., Montreal, Canada
| | - Benoit Gallix
- Institut Hospitalo-Universitaire (IHU), Strasbourg, France
- Université de Strasbourg, Strasbourg, France
- Department of Diagnostic Radiology, McGill University, Montreal, Canada
| | - Thomas F Baumert
- Institut de Recherche sur les Maladies Virales et Hépatiques, Institut National de la Santé et de la Recherche Médicale (Inserm), U1110, 3 Rue Koeberlé, 67000, Strasbourg, France.
- Université de Strasbourg, Strasbourg, France.
- Pôle Hépato-Digestif, Hôpitaux Universitaires de Strasbourg, Strasbourg, France.
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18
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Du Q, Bel-Brunon A, Lambert SA, Hamila N. Numerical simulation of wave propagation through interfaces using the extended finite element method for magnetic resonance elastography. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2022; 151:3481. [PMID: 35649898 PMCID: PMC9381142 DOI: 10.1121/10.0011392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Magnetic resonance elastography (MRE) is an elasticity imaging technique for quantitatively assessing the stiffness of human tissues. In MRE, finite element method (FEM) is widely used for modeling wave propagation and stiffness reconstruction. However, in front of inclusions with complex interfaces, FEM can become burdensome in terms of the model partition and computationally expensive. In this work, we implement a formulation of FEM, known as the eXtended finite element method (XFEM), which is a method used for modeling discontinuity like crack and heterogeneity. Using a level-set method, it makes the interface independent of the mesh, thus relieving the meshing efforts. We investigate this method in two studies: wave propagation across an oblique linear interface and stiffness reconstruction of a random-shape inclusion. In the first study, numerical results by XFEM and FEM models revealing the wave conversion rules at linear interface are presented and successfully compared to the theoretical predictions. The second study, investigated in a pseudo-practical application, demonstrates further the applicability of XFEM in MRE and the convenience, accuracy, and speed of XFEM with respect to FEM. XFEM can be regarded as a promising alternative to FEM for inclusion modeling in MRE.
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Affiliation(s)
- Quanshangze Du
- Univ Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, 69621 Villeurbanne, France
| | - Aline Bel-Brunon
- Univ Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, 69621 Villeurbanne, France
- Electronic mail:
| | - Simon Auguste Lambert
- Université de Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Ecole Centrale de Lyon, CNRS, Ampère UMR5005, Villeurbanne, France
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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; 46:505-513. [PMID: 35483092 DOI: 10.1097/rct.0000000000001308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [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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20
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Wu T, Simonetto DA, Halamka JD, Shah VH. The digital transformation of hepatology: The patient is logged in. Hepatology 2022; 75:724-739. [PMID: 35028960 PMCID: PMC9531185 DOI: 10.1002/hep.32329] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/14/2022]
Abstract
The rise in innovative digital health technologies has led a paradigm shift in health care toward personalized, patient-centric medicine that is reaching beyond traditional brick-and-mortar facilities into patients' homes and everyday lives. Digital solutions can monitor and detect early changes in physiological data, predict disease progression and health-related outcomes based on individual risk factors, and manage disease intervention with a range of accessible telemedicine and mobile health options. In this review, we discuss the unique transformation underway in the care of patients with liver disease, specifically examining the digital transformation of diagnostics, prediction and clinical decision-making, and management. Additionally, we discuss the general considerations needed to confirm validity and oversight of new technologies, usability and acceptability of digital solutions, and equity and inclusivity of vulnerable populations.
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Affiliation(s)
- Tiffany Wu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A. Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - John D. Halamka
- Mayo Clinic Platform, Mayo Clinic, Rochester, Minnesota, USA
| | - Vijay H. Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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21
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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] [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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22
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Moore MM, Iyer RS, Sarwani NI, Sze RW. Artificial intelligence development in pediatric body magnetic resonance imaging: best ideas to adapt from adults. Pediatr Radiol 2022; 52:367-373. [PMID: 33851261 PMCID: PMC8043435 DOI: 10.1007/s00247-021-05072-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 02/09/2021] [Accepted: 03/22/2021] [Indexed: 12/22/2022]
Abstract
Emerging manifestations of artificial intelligence (AI) have featured prominently in virtually all industries and facets of our lives. Within the radiology literature, AI has shown great promise in improving and augmenting radiologist workflow. In pediatric imaging, while greatest AI inroads have been made in musculoskeletal radiographs, there are certainly opportunities within thoracoabdominal MRI for AI to add significant value. In this paper, we briefly review non-interpretive and interpretive data science, with emphasis on potential avenues for advancement in pediatric body MRI based on similar work in adults. The discussion focuses on MRI image optimization, abdominal organ segmentation, and osseous lesion detection encountered during body MRI in children.
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Affiliation(s)
- Michael M Moore
- Department of Radiology, Penn State Children's Hospital, Penn State Health, 500 University Drive, H066, Hershey, PA, 17033, USA.
| | - Ramesh S Iyer
- Seattle Children's Hospital, University of Washington, Seattle, WA, USA
| | | | - Raymond W Sze
- Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
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23
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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 PMCID: PMC8637225 DOI: 10.1148/ryai.2021200274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [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)
| | | | - Stephen S. Cai
- From the 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
- From the 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
- From the 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
- From the 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
- From the 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
- From the 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
- From the 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] [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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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Machine Learning Evaluation of Biliary Atresia Patients to Predict Long-Term Outcome after the Kasai Procedure. Bioengineering (Basel) 2021; 8:bioengineering8110152. [PMID: 34821718 PMCID: PMC8615125 DOI: 10.3390/bioengineering8110152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/16/2021] [Accepted: 10/19/2021] [Indexed: 11/17/2022] Open
Abstract
Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly.
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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] [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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Sabottke CF, Spieler BM, Moawad AW, Elsayes KM. Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives. Magn Reson Imaging Clin N Am 2021; 29:451-463. [PMID: 34243929 DOI: 10.1016/j.mric.2021.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Here we review artificial intelligence (AI) models which aim to assess various aspects of chronic liver disease. Despite the clinical importance of hepatocellular carcinoma in the setting of chronic liver disease, we focus this review on AI models which are not lesion-specific and instead review models developed for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. Optimization of these models offers the opportunity to potentially reduce the need for invasive procedures such as catheterization to measure hepatic venous pressure gradient or biopsy to assess fibrosis and steatosis. We compare the performance of these AI models amongst themselves as well as to radiomics approaches and alternate modality assessments. We conclude that these models show promising performance and merit larger-scale evaluation. We review artificial intelligence models that aim to assess various aspects of chronic liver disease aside from hepatocellular carcinoma. We focus this review on models for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. We conclude that these models show promising performance and merit a larger scale evaluation.
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Affiliation(s)
- Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, 1501 N. Campbell, P.O. Box 245067, Tucson, AZ 85724-5067, USA.
| | - Bradley M Spieler
- Department of Radiology, Louisiana State University Health Sciences Center, 1542 Tulane Avenue, Rm 343, New Orleans, LA 70112, USA
| | - Ahmed W Moawad
- Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Unit 1472, P.O. Box 301402, Houston, TX 77230-1402, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas, MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA
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Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021; 73:2546-2563. [PMID: 33098140 DOI: 10.1002/hep.31603] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 09/15/2020] [Accepted: 09/29/2020] [Indexed: 12/11/2022]
Abstract
Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.
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Affiliation(s)
- Joseph C Ahn
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| | | | | | | | - Vijay H Shah
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
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30
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Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27:1664-1690. [PMID: 33967550 PMCID: PMC8072192 DOI: 10.3748/wjg.v27.i16.1664] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/11/2021] [Accepted: 03/17/2021] [Indexed: 02/06/2023] Open
Abstract
Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.
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Affiliation(s)
- Jia-Sheng Cao
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Zi-Yi Lu
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Ming-Yu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Bin Zhang
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Sarun Juengpanich
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Jia-Hao Hu
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Shi-Jie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Win Topatana
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xue-Yin Zhou
- School of Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
| | - Xu Feng
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Ji-Liang Shen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Yu Liu
- College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xiu-Jun Cai
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
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He T, Fong JN, Moore LW, Ezeana CF, Victor D, Divatia M, Vasquez M, Ghobrial RM, Wong STC. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer. Comput Med Imaging Graph 2021; 89:101894. [PMID: 33725579 PMCID: PMC8054468 DOI: 10.1016/j.compmedimag.2021.101894] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/04/2021] [Accepted: 03/08/2021] [Indexed: 01/27/2023]
Abstract
INTRODUCTION Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients with small HCC (<5 cm) are given priority over others for transplantation due to clinical allocation policies based on tumor size. Attempting to shift from the prevalent paradigm that successful transplantation and longer disease-free survival can only be achieved in patients with small HCC to expanding the transplantation option to patients with HCC of the highest tumor burden (>5 cm), we developed a convergent artificial intelligence (AI) model that combines transient clinical data with quantitative histologic and radiomic features for more objective risk assessment of liver transplantation for HCC patients. METHODS Patients who received a LT for HCC between 2008-2019 were eligible for inclusion in the analysis. All patients with post-LT recurrence were included, and those without recurrence were randomly selected for inclusion in the deep learning model. Pre- and post-transplant magnetic resonance imaging (MRI) scans and reports were compressed using CapsNet networks and natural language processing, respectively, as input for a multiple feature radial basis function network. We applied a histological image analysis algorithm to detect pathologic areas of interest from explant tissue of patients who recurred. The multilayer perceptron was designed as a feed-forward, supervised neural network topology, with the final assessment of recurrence risk. We used area under the curve (AUC) and F-1 score to assess the predictability of different network combinations. RESULTS A total of 109 patients were included (87 in the training group, 22 in the testing group), of which 20 were positive for cancer recurrence. Seven models (AUC; F-1 score) were generated, including clinical features only (0.55; 0.52), magnetic resonance imaging (MRI) only (0.64; 0.61), pathological images only (0.64; 0.61), MRI plus pathology (0.68; 0.65), MRI plus clinical (0.78, 0.75), pathology plus clinical (0.77; 0.73), and a combination of clinical, MRI, and pathology features (0.87; 0.84). The final combined model showed 80 % recall and 89 % precision. The total accuracy of the implemented model was 82 %. CONCLUSION We validated that the deep learning model combining clinical features and multi-scale histopathologic and radiomic image features can be used to discover risk factors for recurrence beyond tumor size and biomarker analysis. Such a predictive, convergent AI model has the potential to alter the LT allocation system for HCC patients and expand the transplantation treatment option to patients with HCC of the highest tumor burden.
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Affiliation(s)
- Tiancheng He
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - Joy Nolte Fong
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Linda W Moore
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States; Center for Outcomes Research, Houston Methodist Research Institute, Houston, TX, 77030, United States
| | - Chika F Ezeana
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - David Victor
- JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX, 77030, United States; Department of Medicine, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Mukul Divatia
- Department of Clinical Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Matthew Vasquez
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - R Mark Ghobrial
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States; JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX, 77030, United States.
| | - Stephen T C Wong
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States.
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Cui S, Tang T, Su Q, Wang Y, Shu Z, Yang W, Gong X. Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study. Cancer Imaging 2021; 21:26. [PMID: 33750453 PMCID: PMC7942000 DOI: 10.1186/s40644-021-00395-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 02/26/2021] [Indexed: 12/15/2022] Open
Abstract
Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-021-00395-6.
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Affiliation(s)
- Sijia Cui
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, 310000, China.,The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Tianyu Tang
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Pancreatic Disease, Hangzhou, China
| | - Qiuming Su
- Department of General Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yajie Wang
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, 310000, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, 310000, China
| | - Wei Yang
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, 310000, China.,Bengbu Medical College, Bengbu, 233000, China
| | - Xiangyang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, 158 Shangtang Road, Hangzhou, 310000, China. .,Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, China.
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Nitsch J, Sack J, Halle MW, Moltz JH, Wall A, Rutherford AE, Kikinis R, Meine H. MRI-based radiomic feature analysis of end-stage liver disease for severity stratification. Int J Comput Assist Radiol Surg 2021; 16:457-466. [PMID: 33646521 PMCID: PMC7946682 DOI: 10.1007/s11548-020-02295-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 11/11/2020] [Indexed: 01/15/2023]
Abstract
Purpose We aimed to develop a predictive model of disease severity for cirrhosis using MRI-derived radiomic features of the liver and spleen and compared it to the existing disease severity metrics of MELD score and clinical decompensation. The MELD score is compiled solely by blood parameters, and so far, it was not investigated if extracted image-based features have the potential to reflect severity to potentially complement the calculated score. Methods This was a retrospective study of eligible patients with cirrhosis (\documentclass[12pt]{minimal}
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\begin{document}$$n=90$$\end{document}n=90) who underwent a contrast-enhanced MR screening protocol for hepatocellular carcinoma (HCC) screening at a tertiary academic center from 2015 to 2018. Radiomic feature analyses were used to train four prediction models for assessing the patient’s condition at time of scan: MELD score, MELD score \documentclass[12pt]{minimal}
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\begin{document}$$\ge $$\end{document}≥ 9 (median score of the cohort), MELD score \documentclass[12pt]{minimal}
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\begin{document}$$\ge $$\end{document}≥ 15 (the inflection between the risk and benefit of transplant), and clinical decompensation. Liver and spleen segmentations were used for feature extraction, followed by cross-validated random forest classification. Results Radiomic features of the liver and spleen were most predictive of clinical decompensation (AUC 0.84), which the MELD score could predict with an AUC of 0.78. Using liver or spleen features alone had slightly lower discrimination ability (AUC of 0.82 for liver and AUC of 0.78 for spleen features only), although this was not statistically significant on our cohort. When radiomic prediction models were trained to predict continuous MELD scores, there was poor correlation. When stratifying risk by splitting our cohort at the median MELD 9 or at MELD 15, our models achieved AUCs of 0.78 or 0.66, respectively. Conclusions We demonstrated that MRI-based radiomic features of the liver and spleen have the potential to predict the severity of liver cirrhosis, using decompensation or MELD status as imperfect surrogate measures for disease severity.
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Affiliation(s)
- Jennifer Nitsch
- Fraunhofer MEVIS Institute for Digital Medicine, Bremen, Germany.
- Medical Image Computing Group, University of Bremen, Bremen, Germany.
- Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Jordan Sack
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael W Halle
- Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jan H Moltz
- Fraunhofer MEVIS Institute for Digital Medicine, Bremen, Germany
| | - April Wall
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Anna E Rutherford
- Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ron Kikinis
- Fraunhofer MEVIS Institute for Digital Medicine, Bremen, Germany
- Medical Image Computing Group, University of Bremen, Bremen, Germany
- Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hans Meine
- Fraunhofer MEVIS Institute for Digital Medicine, Bremen, Germany
- Medical Image Computing Group, University of Bremen, Bremen, Germany
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DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults. Pediatr Radiol 2021; 51:392-402. [PMID: 33048183 PMCID: PMC8675279 DOI: 10.1007/s00247-020-04854-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 07/24/2020] [Accepted: 09/13/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Although MR elastography allows for quantitative evaluation of liver stiffness to assess chronic liver diseases, it has associated drawbacks related to additional scanning time, patient discomfort, and added costs. OBJECTIVE To develop a machine learning model that can categorically classify the severity of liver stiffness using both anatomical T2-weighted MRI and clinical data for children and young adults with known or suspected pediatric chronic liver diseases. MATERIALS AND METHODS We included 273 subjects with known or suspected chronic liver disease. We extracted data including axial T2-weighted fast spin-echo fat-suppressed images, clinical data (e.g., demographic/anthropomorphic data, particular medical diagnoses, laboratory values) and MR elastography liver stiffness measurements. We propose DeepLiverNet (a deep transfer learning model) to classify patients into one of two groups: no/mild liver stiffening (<3 kPa) or moderate/severe liver stiffening (≥3 kPa). We conducted internal cross-validation using 178 subjects, and external validation using an independent cohort of 95 subjects. We assessed diagnostic performance using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AuROC). RESULTS In the internal cross-validation experiment, the combination of clinical and imaging data produced the best performance (AuROC=0.86) compared to clinical (AuROC=0.83) or imaging (AuROC=0.80) data alone. Using both clinical and imaging data, the DeepLiverNet correctly classified patients with accuracy of 88.0%, sensitivity of 74.3% and specificity of 94.6%. In our external validation experiment, this same deep learning model achieved an accuracy of 80.0%, sensitivity of 61.1%, specificity of 91.5% and AuROC of 0.79. CONCLUSION A deep learning model that incorporates clinical data and anatomical T2-weighted MR images might provide a means of risk-stratifying liver stiffness and directing the use of MR elastography.
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Zhu X. Editorial for "Radiomics Approaches for Predicting Liver Fibrosis With Nonenhanced T1-Weighted Imaging: Comparison of Different Radiomics Models". J Magn Reson Imaging 2020; 53:1090-1091. [PMID: 33135268 DOI: 10.1002/jmri.27425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 10/16/2020] [Accepted: 10/19/2020] [Indexed: 11/06/2022] Open
Affiliation(s)
- Xucheng Zhu
- Department of radiology and biomedical imaging, UCSF, San Francisco, California, USA.,GE Healthcare, Menlo Park, California, USA
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36
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Schawkat K, Ciritsis A, von Ulmenstein S, Honcharova-Biletska H, Jüngst C, Weber A, Gubler C, Mertens J, Reiner CS. Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology. Eur Radiol 2020; 30:4675-4685. [PMID: 32270315 DOI: 10.1007/s00330-020-06831-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 03/11/2020] [Accepted: 03/24/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES To compare the diagnostic accuracy of texture analysis (TA)-derived parameters combined with machine learning (ML) of non-contrast-enhanced T1w and T2w fat-saturated (fs) images with MR elastography (MRE) for liver fibrosis quantification. METHODS In this IRB-approved prospective study, liver MRIs of participants with suspected chronic liver disease who underwent liver biopsy between August 2015 and May 2018 were analyzed. Two readers blinded to clinical and histopathological findings performed TA. The participants were categorized into no or low-stage (0-2) and high-stage (3-4) fibrosis groups. Confusion matrices were calculated using a support vector machine combined with principal component analysis. The diagnostic accuracy of ML-based TA of liver fibrosis and MRE was assessed by area under the receiver operating characteristic curves (AUC). Histopathology served as reference standard. RESULTS A total of 62 consecutive participants (40 men; mean age ± standard deviation, 48 ± 13 years) were included. The accuracy of TA and ML on T1w was 85.7% (95% confidence interval [CI] 63.7-97.0) and 61.9% (95% CI 38.4-81.9) on T2w fs for classification of liver fibrosis into low-stage and high-stage fibrosis. The AUC for TA on T1w was similar to MRE (0.82 [95% CI 0.59-0.95] vs. 0.92 [95% CI 0.71-0.99], p = 0.41), while the AUC for T2w fs was significantly lower compared to MRE (0.57 [95% CI 0.34-0.78] vs. 0.92 [95% CI 0.71-0.99], p = 0.008). CONCLUSION Our results suggest that liver fibrosis can be quantified with TA-derived parameters of T1w when combined with a ML algorithm with similar accuracy compared to MRE. KEY POINTS • Liver fibrosis can be categorized into low-stage fibrosis (0-2) and high-stage fibrosis (3-4) using texture analysis-derived parameters of T1-weighted images with a machine learning approach. • For the differentiation of low-stage fibrosis and high-stage fibrosis, the diagnostic accuracy of texture analysis on T1-weighted images combined with a machine learning algorithm is similar compared to MR elastography.
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Affiliation(s)
- Khoschy Schawkat
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.,Division of Abdominal Imaging, Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,University of Zurich, Zurich, Switzerland
| | - Alexander Ciritsis
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Sophie von Ulmenstein
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Hanna Honcharova-Biletska
- University of Zurich, Zurich, Switzerland.,Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Christoph Jüngst
- University of Zurich, Zurich, Switzerland.,Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Achim Weber
- University of Zurich, Zurich, Switzerland.,Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Christoph Gubler
- University of Zurich, Zurich, Switzerland.,Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Joachim Mertens
- University of Zurich, Zurich, Switzerland.,Department of Gastroenterology and Hepatology, University Hospital Zurich, Zurich, Switzerland
| | - Caecilia S Reiner
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland. .,University of Zurich, Zurich, Switzerland.
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