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Kong F, Wang X, Xiang J, Yang S, Wang X, Yue M, Zhang J, Zhao J, Han X, Dong Y, Zhu B, Wang F, Liu Y. Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading. Comput Struct Biotechnol J 2024; 23:1439-1449. [PMID: 38623561 PMCID: PMC11016961 DOI: 10.1016/j.csbj.2024.03.028] [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: 01/14/2024] [Revised: 03/29/2024] [Accepted: 03/29/2024] [Indexed: 04/17/2024] Open
Abstract
Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.
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Affiliation(s)
- Fei Kong
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Xiyue Wang
- College of Biomedical Engineering, Sichuan University, Chengdu, 610065, China
| | | | - Sen Yang
- AI Lab, Tencent, Shenzhen, 518057, China
| | - Xinran Wang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050035, China
| | - Meng Yue
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050035, China
| | - Jun Zhang
- AI Lab, Tencent, Shenzhen, 518057, China
| | - Junhan Zhao
- Massachusetts General Hospital, Boston, MA, 02114, United States
- Harvard T.H. Chan School of Public Health, Boston, MA, 02115, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, United States
| | - Xiao Han
- AI Lab, Tencent, Shenzhen, 518057, China
| | - Yuhan Dong
- Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Biyue Zhu
- Department of Pharmacy, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China
| | - Fang Wang
- Department of Pathology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264000, China
| | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050035, China
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Ma K, Yi X, Yang ST, Zhu H, Liu TY, Jia SS, Fan JH, Hu DJ, Lv GP, Huang H. Isolation, purification, and structural characterization of polysaccharides from Codonopsis pilosula and its therapeutic effects on non-alcoholic fatty liver disease in vitro and in vivo. Int J Biol Macromol 2024; 265:130988. [PMID: 38518942 DOI: 10.1016/j.ijbiomac.2024.130988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 03/01/2024] [Accepted: 03/16/2024] [Indexed: 03/24/2024]
Abstract
Codonopsis pilosula is a famous edible and medicinal plants, in which polysaccharides are recognized as one of the important active ingredients. A neutral polysaccharide (CPP-1) was purified from C. pilosula. The structure was characterized by HPSEC-MALLS-RID, UV, FT-IR, GC-MS, methylation analysis, and NMR. The results showed that CPP-1 was a homogeneous pure polysaccharide, mainly containing fructose and glucose, and a small amount of arabinose. Methylation analysis showed that CPP-1 composed of →1)-Fruf-(2→, Fruf-(1→ and Glcp-(1→ residues. Combined the NMR results the structure of CPP-1 was confirmed as α-D-Glcp-(1 → [2)-β-D-Fruf-(1 → 2)-β-D-Fruf-(1]26 → 2)-β-D-Fruf with the molecular weight of 4.890 × 103 Da. The model of AML12 hepatocyte fat damage was established in vitro. The results showed that CPP-1 could increase the activity of SOD and CAT antioxidant enzymes and reduce the content of MDA, thus protecting cells from oxidative damage. Subsequently, the liver protective effect of CPP-1 was studied in the mouse model of nonalcoholic fatty liver disease (NAFLD) induced by the high-fat diet. The results showed that CPP-1 significantly reduced the body weight, liver index, and body fat index of NAFLD mice, and significantly improved liver function. Therefore, CPP-1 should be a potential candidate for the treatment of NAFLD.
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Affiliation(s)
- Kai Ma
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Xin Yi
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Shu-Ting Yang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Hua Zhu
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Tian-Yu Liu
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Si-Si Jia
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - Jia-Hao Fan
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
| | - De-Jun Hu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, PR China
| | - Guang-Ping Lv
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China.
| | - He Huang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, PR China
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Zheng TL, Sha JC, Deng Q, Geng S, Xiao SY, Yang WJ, Byrne CD, Targher G, Li YY, Wang XX, Wu D, Zheng MH. Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning. Liver Int 2024; 44:330-343. [PMID: 38014574 DOI: 10.1111/liv.15799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/02/2023] [Accepted: 11/12/2023] [Indexed: 11/29/2023]
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.
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Affiliation(s)
- Tian-Lei Zheng
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jun-Cheng Sha
- Department of Interventional Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Qian Deng
- Department of Histopathology, Ningbo Clinical Pathology Diagnosis Center, Ningbo, China
| | - Shi Geng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Shu-Yuan Xiao
- Department of Pathology, University of Chicago Medicine, Chicago, Illinois, USA
| | - Wen-Jun Yang
- Department of Pathology, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Christopher D Byrne
- Southampton National Institute for Health and Care Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, and University of Southampton, Southampton, UK
| | - Giovanni Targher
- Department of Medicine, University of Verona, Verona, Italy
- IRCSS Sacro Cuore - Don Calabria Hospital, Negrar di Valpolicella, Italy
| | - Yang-Yang Li
- Department of Pathology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiang-Xue Wang
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Di Wu
- Department of Pathology, Xuzhou Central Hospital, Xuzhou, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
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Chen B, Lu Q, Hu B, Sun D, Ying T. Protocol of quantitative ultrasound techniques for noninvasive assessing of hepatic steatosis after bariatric surgery. Front Surg 2024; 10:1244199. [PMID: 38239667 PMCID: PMC10794322 DOI: 10.3389/fsurg.2023.1244199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/27/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction Roux-en-Y gastric bypass surgery can effectively improve steatosis, necroinflammatory activity, and hepatic fibrosis in individuals diagnosed with morbid obesity or nonalcoholic steatohepatitis (NASH). Common methods such as body mass index (BMI) to evaluate the postoperative effect of clinical bariatric surgery cannot differentiate subcutaneous fats from visceral fats and muscles. Several Quantitative ultrasound (QUS)-based approaches have been developed to quantify hepatic steatosis. QUS techniques (tissue attenuation imaging (TAI), tissue scatter distribution imaging (TSI)) from radio frequency (RF) data analysis as a means for the detection and grading of hepatic steatosis has been posited as an objective and noninvasive approach. The implementation and standardization of QUS techniques (TAI, TSI) in assessing hepatic steatosis quantitatively after bariatric surgery is of high-priority. Our study is aimed to assess hepatic steatosis with QUS techniques (TAI, TSI) in morbidly obese individuals before and after bariatric surgery, and to compare with anthropometric measurements, laboratory assessments and other imaging methods. Methods and analysis The present investigation, a self-discipline examination of navigational capacity devoid of visual cues, is designed as a single-site, forward-looking evaluation of efficacy with the imprimatur of the institutional review board. The duration of the study has been provisionally determined to span from 1 January 2023 through 31 December 2025. Our cohort shall encompass one hundred participants, who was scheduled to undergo Roux-en-Y gastric bypass (RYGB) at Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. All patients will undergo anthropometric measurements, blood-based biochemical analyses, ultrasonic examination and magnetic resonance imaging proton density fat fraction (MRI-PDFF). The primary endpoint is the analysis of evaluating the efficacy of QUS techniques assessing hepatic steatosis compared to other methods before and after bariatric surgery. Results Prior to the fomal study, we recruited 21 obese Chinese participants who received ultrasonic examination (TAI, TSI) and MRI-PDFF. AC-TAI showed moderate correlations with MRI-PDFF (adjusted r = 0.632; P < 0.05). For MRI-PDFF ≥10%, SC-TSI showed moderate correlations with MRI-PDFF (adjusted r = 0.677; P < 0.05). Conclusion Our pre-experiment results signified that using QUS techniques for postoperative evaluation of bariatric surgery is promising. QUS techniques will be signed a widespread availability, real-time functionality, and low-cost approach for assessing hepatic steatosis before and after bariatric surgery in obese individuals, thus is capable for subsequent scale-up liver fat quantification. Ethics and dissemination The present research endeavor has been bestowed with the imprimatur of the Ethics Committee of the Hospital, as indicated by its Approval Number: 2023-KY-015. In due course, upon completion of the study, we intend to disseminate our findings by publishing them in a suitable academic journal, thereby facilitating their widespread utilization. Registration The trial is duly registered with the Chinese Clinical Trial Registry, and with a unique Trial Registration Number, ChiCTR2300069892, approved on March 28, 2023.
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Affiliation(s)
- Bin Chen
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qijie Lu
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Hu
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Sun
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Ying
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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