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Yuan HY, Tong XF, Ren YY, Li YY, Wang XL, Chen LL, Chen SD, Jin XZ, Wang XD, Targher G, Byrne CD, Wei L, Wong VWS, Tai D, Sanyal AJ, You H, Zheng MH. AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study. Liver Int 2024; 44:2572-2582. [PMID: 38963299 DOI: 10.1111/liv.16025] [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: 03/15/2024] [Revised: 06/09/2024] [Accepted: 06/23/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND AND AIMS Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention-induced fibrosis changes in MASH. METHODS We examined unstained sections from paired liver biopsies (baseline and end-of-intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions. RESULTS About 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI-induced liver fibrosis regression was concentrated in the periportal region. CONCLUSION Using digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.
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Affiliation(s)
- Hai-Yang Yuan
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiao-Fei Tong
- Liver Research Center, Beijing Friendship Hospital, Beijing Key Laboratory of Translational Medicine on Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Capital Medical University, Beijing, China
| | - Ya-Yun Ren
- HistoIndex Pte Ltd, Singapore, Singapore
| | - Yang-Yang Li
- Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | | | - Li-Li Chen
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Sui-Dan Chen
- Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiao-Zhi Jin
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiao-Dong Wang
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
| | - Giovanni Targher
- Department of Medicine, University of Verona, Verona, Italy
- Metabolic Diseases Research Unit, IRCCS Sacro Cuore-Don Calabria Hospital, Negrar di Valpolicella, Verona, Italy
| | - Christopher D Byrne
- Southampton National Institute for Health and Care Research Biomedical Research Centre, University Hospital Southampton and University of Southampton, Southampton General Hospital, Southampton, UK
| | - Lai Wei
- Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Vincent W-S Wong
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Dean Tai
- HistoIndex Pte Ltd, Singapore, Singapore
| | - Arun J Sanyal
- Stravitz-Sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
| | - Hong You
- Liver Research Center, Beijing Friendship Hospital, Beijing Key Laboratory of Translational Medicine on Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Capital Medical University, Beijing, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
- Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
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Deng J, Ji W, Liu H, Li L, Wang Z, Hu Y, Wang Y, Zhou Y. Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk. BMC Public Health 2024; 24:2545. [PMID: 39294603 PMCID: PMC11412026 DOI: 10.1186/s12889-024-19882-z] [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: 10/26/2023] [Accepted: 08/26/2024] [Indexed: 09/20/2024] Open
Abstract
BACKGROUND The existing predictive models for metabolic-associated fatty liver disease (MAFLD) possess certain limitations that render them unsuitable for extensive population-wide screening. This study is founded upon population health examination data and employs a comparison of eight distinct machine learning (ML) algorithms to construct the optimal screening model for identifying high-risk individuals with MAFLD in China. METHODS We collected physical examination data from 5,171,392 adults residing in the northwestern region of China, during the year 2021. Feature selection was conducted through the utilization of the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Additionally, class balancing parameters were incorporated into the models, accompanied by hyperparameter tuning, to effectively address the challenges posed by imbalanced datasets. This study encompassed the development of both tree-based ML models (including Classification and Regression Trees, Random Forest, Adaptive Boosting, Light Gradient Boosting Machine, Extreme Gradient Boosting, and Categorical Boosting) and alternative ML models (specifically, k-Nearest Neighbors and Artificial Neural Network) for the purpose of identifying individuals with MAFLD. Furthermore, we visualized the importance scores of each feature on the selected model. RESULTS The average age (standard deviation) of the 5,171,392 participants was 51.12 (15.00) years, with 52.47% of the participants being females. MAFLD was diagnosed by specialized physicians. 20 variables were finally included for analyses after LASSO regression model. Following ten rounds of cross-validation and parameter optimization for each algorithm, the CatBoost algorithm exhibited the best performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.862. The ranking of feature importance indicates that age, BMI, triglyceride, fasting plasma glucose, waist circumference, occupation, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, ethnicity and cardiovascular diseases are the top 13 crucial factors for MAFLD screening. CONCLUSION This study utilized a large-scale, multi-ethnic physical examination data from the northwestern region of China to establish a more accurate and effective MAFLD risk screening model, offering a new perspective for the prediction and prevention of MAFLD.
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Affiliation(s)
- Jiale Deng
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Weidong Ji
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Hongze Liu
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Lin Li
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Zhe Wang
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China
| | - Yurong Hu
- School of Computer Science, China University of Geosciences, Wuhan, Beihe, 430074, China
| | - Yushan Wang
- People's Hospital of Xinjiang Uygur Autonomous Region, 91 Tianchi Road, Urumqi, 830054, Xinjiang, China.
| | - Yi Zhou
- Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China.
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Grignaffini F, Barbuto F, Troiano M, Piazzo L, Simeoni P, Mangini F, De Stefanis C, Onetti Muda A, Frezza F, Alisi A. The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review. Diagnostics (Basel) 2024; 14:388. [PMID: 38396427 PMCID: PMC10887838 DOI: 10.3390/diagnostics14040388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.
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Affiliation(s)
- Flavia Grignaffini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Francesco Barbuto
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Maurizio Troiano
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | - Lorenzo Piazzo
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Patrizio Simeoni
- National Transport Authority (NTA), D02 WT20 Dublin, Ireland;
- Faculty of Lifelong Learning, South East Technological University (SETU), R93 V960 Carlow, Ireland
| | - Fabio Mangini
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Cristiano De Stefanis
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
| | | | - Fabrizio Frezza
- Department of Information Engineering, Electronics and Telecommunications (DIET), “La Sapienza”, University of Rome, 00184 Rome, Italy; (F.G.); (F.B.); (L.P.); (F.M.); (F.F.)
| | - Anna Alisi
- Research Unit of Genetics of Complex Phenotypes, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (M.T.); (C.D.S.)
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