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Malik S, Das R, Thongtan T, Thompson K, Dbouk N. AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease. J Clin Med 2024; 13:7833. [PMID: 39768756 PMCID: PMC11678868 DOI: 10.3390/jcm13247833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2024] [Revised: 12/13/2024] [Accepted: 12/19/2024] [Indexed: 01/11/2025] Open
Abstract
The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI harnesses vast datasets and complex algorithms to enhance clinical decision making and patient outcomes. AI's applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.
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Affiliation(s)
- Sheza Malik
- Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, USA;
| | - Rishi Das
- Division of Digestive Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA; (R.D.); (T.T.)
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Thanita Thongtan
- Division of Digestive Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA; (R.D.); (T.T.)
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Kathryn Thompson
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
| | - Nader Dbouk
- Division of Digestive Diseases, Emory University School of Medicine, Atlanta, GA 30322, USA; (R.D.); (T.T.)
- Department of Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA;
- Emory Transplant Center, Emory University School of Medicine, Atlanta, GA 30322, USA
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Bai Z, Yin Y, Xu W, Cheng G, Qi X. Predictive model of in-hospital mortality in liver cirrhosis patients with hyponatremia: an artificial neural network approach. Sci Rep 2024; 14:28719. [PMID: 39567595 PMCID: PMC11579295 DOI: 10.1038/s41598-024-73256-4] [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: 04/25/2024] [Accepted: 09/16/2024] [Indexed: 11/22/2024] Open
Abstract
Hyponatremia can worsen the outcomes of patients with liver cirrhosis. However, it remains unclear about how to predict the risk of death in cirrhotic patients with hyponatremia. Patients with liver cirrhosis and hyponatremia were screened. Eligible patients were randomly divided into the training (n = 472) and validation (n = 471) cohorts. In the training cohort, the independent predictors for in-hospital death were identified by logistic regression analyses. Odds ratios (ORs) were calculated. An artificial neural network (ANN) model was established in the training cohort. Areas under curve (AUCs) of ANN model, Child-Pugh, model for end-stage liver disease (MELD), and MELD-Na scores were calculated by receiver operating characteristic curve analyses. In multivariate logistic regression analyses, ascites (OR = 2.705, P = 0.042), total bilirubin (OR = 1.004, P = 0.003), serum creatinine (OR = 1.004, P = 0.017), and international normalized ratio (OR = 1.457, P = 0.005) were independently associated with in-hospital death. Based on the four variables, an ANN model was established. Its AUC was 0.865 and 0.810 in the training and validation cohorts, respectively, which was significantly larger than those of Child-Pugh (AUC = 0.757), MELD (AUC = 0.765), and MELD-Na (AUC = 0.769) scores. An ANN model has been developed and validated for the prediction of in-hospital death in patients with liver cirrhosis and hyponatremia.
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Affiliation(s)
- Zhaohui Bai
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command (Teaching Hospital of Shenyang Pharmaceutical University), Shenyang, China
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing, China
| | - Yuhang Yin
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command (Teaching Hospital of Shenyang Pharmaceutical University), Shenyang, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Wentao Xu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command (Teaching Hospital of Shenyang Pharmaceutical University), Shenyang, China
| | - Gang Cheng
- NMPA Key Laboratory for Research and Evaluation of Drug Regulatory Technology, Shenyang Pharmaceutical University, Shenyang, China.
| | - Xingshun Qi
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command (Teaching Hospital of Shenyang Pharmaceutical University), Shenyang, China.
- NMPA Key Laboratory for Research and Evaluation of Drug Regulatory Technology, Shenyang Pharmaceutical University, Shenyang, China.
- Department of Gastroenterology, General Hospital of Northern Theater Command Shenyang (Teaching Hospital of Shenyang Pharmaceutical University), Shenyang, China.
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3
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Bektaş M, Chia CM, Burchell GL, Daams F, Bonjer HJ, van der Peet DL. Artificial intelligence-aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now? Surg Endosc 2024; 38:4869-4879. [PMID: 39160306 PMCID: PMC11362182 DOI: 10.1007/s00464-024-11130-0] [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: 04/02/2024] [Accepted: 07/28/2024] [Indexed: 08/21/2024]
Abstract
BACKGROUND Artificial intelligence (AI) models have been applied in various medical imaging modalities and surgical disciplines, however the current status and progress of ultrasound-based AI models within hepatopancreatobiliary surgery have not been evaluated in literature. Therefore, this review aimed to provide an overview of ultrasound-based AI models used for hepatopancreatobiliary surgery, evaluating current advancements, validation, and predictive accuracies. METHOD Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using AI models on ultrasound for patients undergoing hepatopancreatobiliary surgery. To be eligible for inclusion, studies needed to apply AI methods on ultrasound imaging for patients undergoing hepatopancreatobiliary surgery. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. RESULTS AI models have been primarily used within hepatopancreatobiliary surgery, to predict tumor recurrence, differentiate between tumoral tissues, and identify lesions during ultrasound imaging. Most studies have combined radiomics with convolutional neural networks, with AUCs up to 0.98. CONCLUSION Ultrasound-based AI models have demonstrated promising accuracies in predicting early tumoral recurrence and even differentiating between tumoral tissue types during and after hepatopancreatobiliary surgery. However, prospective studies are required to evaluate if these results will remain consistent and externally valid.
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Affiliation(s)
- Mustafa Bektaş
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Catherine M Chia
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, The Netherlands
| | - George L Burchell
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Medical Library, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Freek Daams
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
| | - H Jaap Bonjer
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Donald L van der Peet
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
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Juneja D. Artificial intelligence: Applications in critical care gastroenterology. Artif Intell Gastrointest Endosc 2024; 5:89138. [DOI: 10.37126/aige.v5.i1.89138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/07/2023] [Accepted: 12/26/2023] [Indexed: 02/20/2024] Open
Abstract
Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.
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Affiliation(s)
- Deven Juneja
- Department of Critical Care Medicine, Max Super Speciality Hospital, New Delhi 110017, India
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Lee KH, Choi GH, Yun J, Choi J, Goh MJ, Sinn DH, Jin YJ, Kim MA, Yu SJ, Jang S, Lee SK, Jang JW, Lee JS, Kim DY, Cho YY, Kim HJ, Kim S, Kim JH, Kim N, Kim KM. Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study. NPJ Digit Med 2024; 7:2. [PMID: 38182886 PMCID: PMC10770025 DOI: 10.1038/s41746-023-00976-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024] Open
Abstract
The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell's C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions.
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Affiliation(s)
- Kyung Hwa Lee
- Department of Radiation Oncology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Gwang Hyeon Choi
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University, Seongnam, Republic of Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jonggi Choi
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Myung Ji Goh
- Department of Internal Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Dong Hyun Sinn
- Department of Internal Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Young Joo Jin
- Department of Internal Medicine, Inha University Hospital, Incheon, Republic of Korea
| | - Minseok Albert Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University, Seoul, Republic of Korea
| | - Su Jong Yu
- Department of Internal Medicine, Seoul National University Hospital, Seoul National University, Seoul, Republic of Korea
| | - Sangmi Jang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University, Seongnam, Republic of Korea
- Department of Internal Medicine, Inha University Hospital, Incheon, Republic of Korea
| | - Soon Kyu Lee
- Department of Internal Medicine, Seoul St. Mary's Hospital, Seoul, Republic of Korea
- Department of Internal Medicine, Incheon St. Mary's Hospital, Incheon, Republic of Korea
| | - Jeong Won Jang
- Department of Internal Medicine, Seoul St. Mary's Hospital, Seoul, Republic of Korea
| | - Jae Seung Lee
- Department of Internal Medicine, Seoul Severance Hospital, Seoul, Republic of Korea
| | - Do Young Kim
- Department of Internal Medicine, Seoul Severance Hospital, Seoul, Republic of Korea
| | - Young Youn Cho
- Department of Internal Medicine, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Hyung Joon Kim
- Department of Internal Medicine, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Sehwa Kim
- Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
- Department of Internal Medicine, Bundang Jesaeng General Hospital, Seongnam, Republic of Korea
| | - Ji Hoon Kim
- Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Kang Mo Kim
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/25/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Zeng J, Zeng J, Lin K, Lin H, Wu Q, Guo P, Zhou W, Liu J. Development of a machine learning model to predict early recurrence for hepatocellular carcinoma after curative resection. Hepatobiliary Surg Nutr 2022; 11:176-187. [PMID: 35464276 PMCID: PMC9023817 DOI: 10.21037/hbsn-20-466] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 06/17/2020] [Indexed: 08/01/2023]
Abstract
Background Early recurrence is common for hepatocellular carcinoma (HCC) after surgical resection, being the leading cause of death. Traditionally, the COX proportional hazard (CPH) models based on linearity assumption have been used to predict early recurrence, but predictive performance is limited. Machine learning models offer a novel methodology and have several advantages over CPH models. Hence, the purpose of this study was to compare random survival forests (RSF) model with CPH models in prediction of early recurrence for HCC patients after curative resection. Methods A total of 4,758 patients undergoing curative resection from two medical centers were included. Fifteen features including age, gender, etiology, platelet count, albumin, total bilirubin, AFP, tumor size, tumor number, microvascular invasion, macrovascular invasion, Edmondson-Steiner grade, tumor capsular, satellite nodules and liver cirrhosis were used to construct the RSF model in training cohort. Discrimination, calibration, clinical usefulness and overall performance were assessed and compared with other models. Results Five hundred survival trees were used to generate the RFS model. The five highest Variable Importance (VIMP) were tumor size, macrovascular invasion, microvascular invasion, tumor number and AFP. In training, internal and external validation cohort, the C-index of RSF model were 0.725 [standard errors (SE) =0.005], 0.762 (SE =0.011) and 0.747 (SE =0.016), respectively; the Gönen & Heller's K of RSF model were 0.684 (SE =0.005), 0.711 (SE =0.008) and 0.697 (SE =0.014), respectively; the time-dependent AUC (2 years) of RSF model were 0.818 (SE =0.008), 0.823 (SE =0.014) and 0.785 (SE =0.025), respectively. The RSF model outperformed early recurrence after surgery for liver tumor (ERASL) model, Korean model, American Joint Committee on Cancer tumor-node-metastasis (AJCC TNM) stage, Barcelona Clinic Liver Cancer (BCLC) stage and Chinese stage. The RSF model is capable of stratifying patients into three different risk groups (low-risk, intermediate-risk, high-risk groups) in the training and two validation cohorts (all P<0.0001). A web-based prediction tool was built to facilitate clinical application (https://recurrenceprediction.shinyapps.io/surgery_predict/). Conclusions The RSF model is a reliable tool to predict early recurrence for patients with HCC after curative resection because it exhibited superior performance compared with other models. This novel model will be helpful to guide postoperative follow-up and adjuvant therapy.
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Affiliation(s)
- Jianxing Zeng
- Department of Hepatic Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
- The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Jinhua Zeng
- Department of Hepatic Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
- The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China
| | - Kongying Lin
- Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Haitao Lin
- Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Qionglan Wu
- Department of Pathology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Pengfei Guo
- Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Weiping Zhou
- The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jingfeng Liu
- Department of Hepatic Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
- The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
- The Liver Center of Fujian Province, Fujian Medical University, Fuzhou, China
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Li F, Zhou X, Gao X, Zhao H, Niu S. 3D Vision Transformer for Postoperative Recurrence Risk Prediction of Liver Cancer. LECTURE NOTES IN ELECTRICAL ENGINEERING 2022:163-172. [DOI: 10.1007/978-981-16-6963-7_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Performance of artificial intelligence for prognostic prediction with the albumin-bilirubin and platelet-albumin-bilirubin for cirrhotic patients with acute variceal bleeding undergoing early transjugular intrahepatic portosystemic shunt. Eur J Gastroenterol Hepatol 2021; 33:e153-e160. [PMID: 33177378 DOI: 10.1097/meg.0000000000001989] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE The aim of this study was to validate and compare the prognostic performance of the albumin-bilirubin (ALBI) grade, platelet-albumin-bilirubin (PALBI) grade, Child-Pugh (CP) grade, and Model for End-Stage Liver Disease (MELD) score in predicting the 1-year variceal rebleeding probability using artificial intelligence for patients with cirrhosis and variceal bleeding undergoing early transjugular intrahepatic portosystemic shunt (TIPS) procedures. MATERIALS AND METHODS This dual-center retrospective study included two cohorts, with patients enrolled between January 2016 and September 2018 in the training cohort and January 2017 and September 2018 in the validation cohort. In the training cohort, independent risk factors associated with the 1-year variceal rebleeding probability were identified using univariate and multivariate logistic analyses. ALBI-, PALBI-, Child-Pugh-, and MELD-based nomograms and an artificial neural network (ANN) model were established and validated internally in the training cohort and externally in the validation cohort, which included patients with variceal bleeding who were treated with preventive TIPS. RESULTS A total of 259 patients were included. The median follow-up periods were 24.1 and 18.9 months, and the 1-year variceal rebleeding rates were 12.3% (14/114) and 10.3% (15/145) in the training and validation cohorts, respectively. In the training cohort, all four variables were identified as independent risk factors. Four nomograms were then established and showed comparable prognostic performances after internal (C-index: 0.879, 0.829, 0.874, and 0.798) and external (C-index: 0.720, 0.719, 0.718, and 0.703) validation. The ANN demonstrated that these four variables had comparable importance in predicting the 1-year variceal rebleeding probability. CONCLUSION None of the four variables are optimal in predicting the 1-year variceal rebleeding probability for patients with cirrhosis and variceal bleeding undergoing early TIPS.
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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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11
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Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2:42-55. [DOI: 10.35712/aig.v2.i2.42] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/25/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most commonly diagnosed type of liver cancer and the fourth leading cause of cancer-related mortality worldwide. The early identification of HCC and effective treatments for it have been challenging. Due to the sufficient compensatory ability of early patients and its nonspecific symptoms, HCC is more likely to escape diagnosis in the incipient stage, during which patients can achieve a more satisfying overall survival if they undergo resection or liver transplantation. Patients at advanced stages can profit from radical therapies in a limited way. In order to improve the unfavorable prognosis of HCC, diagnostic ability and treatment efficiency must be improved. The past decade has seen rapid advancements in artificial intelligence, underlying its unique usefulness in almost every field, including that of medicine. Herein, we sought and reviewed studies that put emphasis on artificial intelligence and HCC.
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Affiliation(s)
- Peng-Sheng Yi
- Department of Hepato-Biliary-Pancreas II, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chen-Jun Hu
- Department of Hepato-Biliary-Pancreas II, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chen-Hui Li
- Department of Obstetrics and Gynecology, Nanchong Traditional Chinese Medicine Hospital, Nanchong 637000, Sichuan Province, China
| | - Fei Yu
- Department of Radiology, Yingshan County People’s Hospital, Nanchong 610041, Sichuan Province, China
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Lim B, Lee KS, Lee YH, Kim S, Min C, Park JY, Lee HS, Cho JS, Kim SI, Chung BH, Kim CS, Koo KC. External Validation of the Long Short-Term Memory Artificial Neural Network-Based SCaP Survival Calculator for Prediction of Prostate Cancer Survival. Cancer Res Treat 2021; 53:558-566. [PMID: 33070560 PMCID: PMC8053858 DOI: 10.4143/crt.2020.637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/05/2020] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. MATERIALS AND METHODS The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)-free survival, cancer-specific survival (CSS), and overall survival (OS). RESULTS Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. CONCLUSION The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.
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Affiliation(s)
- Bumjin Lim
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul,
Korea
| | - Kwang Suk Lee
- Department of Urology, Yonsei University College of Medicine, Seoul,
Korea
| | - Young Hwa Lee
- Department of Urology, Yonsei University College of Medicine, Seoul,
Korea
| | | | | | - Ju-Young Park
- Biostatistics Collaboration Unit, Yonsei University, Seoul,
Korea
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University, Seoul,
Korea
| | - Jin Seon Cho
- Department of Urology, Hallym University College of Medicine, Chuncheon,
Korea
| | - Sun Il Kim
- Department of Urology, Ajou University School of Medicine, Suwon,
Korea
| | - Byung Ha Chung
- Department of Urology, Yonsei University College of Medicine, Seoul,
Korea
| | - Choung-Soo Kim
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul,
Korea
| | - Kyo Chul Koo
- Department of Urology, Yonsei University College of Medicine, Seoul,
Korea
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13
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Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021; 12:31. [PMID: 33675433 PMCID: PMC7936998 DOI: 10.1186/s13244-021-00977-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/15/2021] [Indexed: 12/24/2022] Open
Abstract
With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.
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Affiliation(s)
- Zhi-Min Zou
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China
| | - De-Hua Chang
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Hui Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China
| | - Yu-Dong Xiao
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China.
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14
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Zhang Y, Yu H, Dong R, Ji X, Li F. Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5592472. [PMID: 33763475 PMCID: PMC7952150 DOI: 10.1155/2021/5592472] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/20/2021] [Accepted: 02/24/2021] [Indexed: 12/25/2022]
Abstract
Myasthenia gravis (MG) is a chronic autoimmune disease of the nervous system, which is still incurable. In recent years, with the progress of immunosuppressive and supportive treatment, the therapeutic effect of MG in the acute stage is satisfactory, and the mortality rate has been greatly reduced. However, there is still no consensus on how to conduct long-term management of stable MG, such as guiding patients to identify relapses, practice exercise, return to work and school, etc. In the international consensus guidance for management of myasthenia gravis published by the Myasthenia Gravis Foundation of America (MGFA) in 2020, for the first time, "the role of physical training/exercise in MG" was identified as the topic of discussion. Finally, due to a lack of high-quality evidence on physical training/exercise in patients with MG, the topic was excluded after the literature review. Therefore, this paper reviewed the current status of MG rehabilitation research and the difficulties faced by stable MG patients in self-management. It is suggested that we should take advantage of artificial intelligence (AI) and leverage it to develop the data-driven decision support platforms for MG management which can be used for adverse event monitoring, disease education, chronic management, and a wide variety of data collection and analysis.
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Affiliation(s)
- Ying Zhang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongmei Yu
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Rui Dong
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xuan Ji
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Fujun Li
- Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
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15
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Lai Q, Spoletini G, Mennini G, Laureiro ZL, Tsilimigras DI, Pawlik TM, Rossi M. Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review. World J Gastroenterol 2020; 26:6679-6688. [PMID: 33268955 PMCID: PMC7673961 DOI: 10.3748/wjg.v26.i42.6679] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/14/2020] [Accepted: 10/01/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Prediction of survival after the treatment of hepatocellular carcinoma (HCC) has been widely investigated, yet remains inadequate. The application of artificial intelligence (AI) is emerging as a valid adjunct to traditional statistics due to the ability to process vast amounts of data and find hidden interconnections between variables. AI and deep learning are increasingly employed in several topics of liver cancer research, including diagnosis, pathology, and prognosis. AIM To assess the role of AI in the prediction of survival following HCC treatment. METHODS A web-based literature search was performed according to the Preferred Reporting Items for Systemic Reviews and Meta-Analysis guidelines using the keywords "artificial intelligence", "deep learning" and "hepatocellular carcinoma" (and synonyms). The specific research question was formulated following the patient (patients with HCC), intervention (evaluation of HCC treatment using AI), comparison (evaluation without using AI), and outcome (patient death and/or tumor recurrence) structure. English language articles were retrieved, screened, and reviewed by the authors. The quality of the papers was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool. Data were extracted and collected in a database. RESULTS Among the 598 articles screened, nine papers met the inclusion criteria, six of which had low-risk rates of bias. Eight articles were published in the last decade; all came from eastern countries. Patient sample size was extremely heterogenous (n = 11-22926). AI methodologies employed included artificial neural networks (ANN) in six studies, as well as support vector machine, artificial plant optimization, and peritumoral radiomics in the remaining three studies. All the studies testing the role of ANN compared the performance of ANN with traditional statistics. Training cohorts were used to train the neural networks that were then applied to validation cohorts. In all cases, the AI models demonstrated superior predictive performance compared with traditional statistics with significantly improved areas under the curve. CONCLUSION AI applied to survival prediction after HCC treatment provided enhanced accuracy compared with conventional linear systems of analysis. Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.
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Affiliation(s)
- Quirino Lai
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | - Gabriele Spoletini
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario A Gemelli IRCCS, Rome 00100, Italy
| | - Gianluca Mennini
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | - Zoe Larghi Laureiro
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | | | | | - Massimo Rossi
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
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16
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Yang L, Wei C, Li Y, He X, He M. miR-224 is an early-stage biomarker of hepatocellular carcinoma with miR-224 and miR-125b as prognostic biomarkers. Biomark Med 2020; 14:1485-1500. [PMID: 33155836 DOI: 10.2217/bmm-2020-0099] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Aim: The aim was to systematically investigate the miRNA biomarkers for early diagnosis of hepatocellular carcinoma (HCC). Materials & methods: A systematic review and meta-analysis of miRNA expression in HCC were performed. Results: A total of 4903 cases from 30 original studies were comprehensively analyzed. The sensitivity and specificity of miR-224 in discriminating early-stage HCC patients from benign lesion patients were 0.868 and 0.792, which were superior to α-fetoprotein. Combined miR-224 with α-fetoprotein, the sensitivity and specificity were increased to 0.882 and 0.808. Prognostic survival analysis showed low expression of miR-125b and high expression of miR-224 were associated with poor prognosis. Conclusion: miR-224 had a prominent diagnostic efficiency in early-stage HCC, with miR-224 and miR-125b being valuable in the prognostic diagnosis.
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Affiliation(s)
- Lichao Yang
- School of Public Health, Guangxi Medical University, Nanning 530021, China
| | - Chunmeng Wei
- Nanning Municipal Center for Disease Control & Prevention, Nanning 530021, China
| | - Yasi Li
- College of Global Public Health, New York University, NY 10003, USA
| | - Xiao He
- School of Public Health, Guilin Medical School, Guilin 541100, China
| | - Min He
- School of Public Health, Guangxi Medical University, Nanning 530021, China.,Key Laboratory of High-Incidence Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education, Nanning 530021, China.,Laboratory Animal Center, Guangxi Medical University, Nanning 530021, China
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17
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Koo KC, Lee KS, Kim S, Min C, Min GR, Lee YH, Han WK, Rha KH, Hong SJ, Yang SC, Chung BH. Long short-term memory artificial neural network model for prediction of prostate cancer survival outcomes according to initial treatment strategy: development of an online decision-making support system. World J Urol 2020; 38:2469-2476. [PMID: 31925552 DOI: 10.1007/s00345-020-03080-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 01/03/2020] [Indexed: 01/23/2023] Open
Abstract
PURPOSE The delivery of precision medicine is a primary objective for both clinical and translational investigators. Patients with newly diagnosed prostate cancer (PCa) face the challenge of deciding among multiple initial treatment modalities. The purpose of this study is to utilize artificial neural network (ANN) modeling to predict survival outcomes according to initial treatment modality and to develop an online decision-making support system. METHODS Data were collected retrospectively from 7267 patients diagnosed with PCa between January 1988 and December 2017. The analyses included 19 pretreatment clinicopathological covariates. Multilayer perceptron (MLP), MLP for N-year survival prediction (MLP-N), and long short-term memory (LSTM) ANN models were used to analyze progression to castration-resistant PCa (CRPC)-free survival, cancer-specific survival (CSS), and overall survival (OS), according to initial treatment modality. The performances of the ANN and the Cox-proportional hazards regression models were compared using Harrell's C-index. RESULTS The ANN models provided higher predictive power for 5- and 10-year progression to CRPC-free survival, CSS, and OS compared to the Cox-proportional hazards regression model. The LSTM model achieved the highest predictive power, followed by the MLP-N, and MLP models. We developed an online decision-making support system based on the LSTM model to provide individualized survival outcomes at 5 and 10 years, according to the initial treatment strategy. CONCLUSION The LSTM ANN model may provide individualized survival outcomes of PCa according to initial treatment strategy. Our online decision-making support system can be utilized by patients and health-care providers to determine the optimal initial treatment modality and to guide survival predictions.
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Affiliation(s)
- Kyo Chul Koo
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea
| | - Kwang Suk Lee
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea
| | - Suah Kim
- Selvas AI, Seoul, Republic of Korea
| | | | - Gyu Rang Min
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea
| | - Young Hwa Lee
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea
| | - Woong Kyu Han
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea
| | - Koon Ho Rha
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea
| | - Sung Joon Hong
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea
| | - Seung Choul Yang
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea
| | - Byung Ha Chung
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea.
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18
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Choi GH, Yun J, Choi J, Lee D, Shim JH, Lee HC, Chung YH, Lee YS, Park B, Kim N, Kim KM. Development of machine learning-based clinical decision support system for hepatocellular carcinoma. Sci Rep 2020; 10:14855. [PMID: 32908183 PMCID: PMC7481788 DOI: 10.1038/s41598-020-71796-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 08/04/2020] [Indexed: 12/29/2022] Open
Abstract
There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision support system (CDSS) for recommending initial treatment option in HCC and predicting overall survival (OS). From hospital records of 1,021 consecutive patients with HCC treated at a single centre in Korea between January 2010 and October 2010, we collected information on 61 pretreatment variables, initial treatment, and survival status. Twenty pretreatment key variables were finally selected. We developed the CDSS from the derivation set (N = 813) using random forest method and validated it in the validation set (N = 208). Among the 1,021 patients (mean age: 56.9 years), 81.8% were male and 77.0% had positive hepatitis B BCLC stages 0, A, B, C, and D were observed in 13.4%, 26.0%, 18.0%, 36.6%, and 6.3% of patients, respectively. The six multi-step classifier model was developed for treatment decision in a hierarchical manner, and showed good performance with 81.0% of accuracy for radiofrequency ablation (RFA) or resection versus not, 88.4% for RFA versus resection, and 76.8% for TACE or not. We also developed seven survival prediction models for each treatment option. Our newly developed HCC-CDSS model showed good performance in terms of treatment recommendation and OS prediction and may be used as a guidance in deciding the initial treatment option for HCC.
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Affiliation(s)
- Gwang Hyeon Choi
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Jihye Yun
- Department of Convergence Medicine and Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jonggi Choi
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Danbi Lee
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Ju Hyun Shim
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Han Chu Lee
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Young-Hwa Chung
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Yung Sang Lee
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Beomhee Park
- Department of Convergence Medicine and Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Department of Convergence Medicine and Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Kang Mo Kim
- Department of Gastroenterology, Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
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Hou Y, Zhang Q, Gao F, Mao D, Li J, Gong Z, Luo X, Chen G, Li Y, Yang Z, Sun K, Wang X. Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure. BMC Gastroenterol 2020; 20:75. [PMID: 32188419 PMCID: PMC7081680 DOI: 10.1186/s12876-020-01191-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 02/11/2020] [Indexed: 02/08/2023] Open
Abstract
Background This study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF) through artificial neural network (ANN) systems. Methods Six hundred and eight-four cases of consecutive HBV-ACLF patients were retrospectively reviewed. Four hundred and twenty-three cases were used for training and constructing ANN models, and the remaining 261 cases were for validating the established models. Predictors associated with mortality were determined by univariate analysis and were then included in ANN models for predicting prognosis of mortality. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of the ANN models in comparison with various current prognostic models. Results Variables with statistically significant difference or important clinical characteristics were input in the ANN training process, and eight independent risk factors, including age, hepatic encephalopathy, serum sodium, prothrombin activity, γ-glutamyltransferase, hepatitis B e antigen, alkaline phosphatase and total bilirubin, were eventually used to establish ANN models. For 28-day mortality in the training cohort, the model’s predictive accuracy (AUR 0.948, 95% CI 0.925–0.970) was significantly higher than that of the Model for End-stage Liver Disease (MELD), MELD-sodium (MELD-Na), Chronic Liver Failure-ACLF (CLIF-ACLF), and Child-Turcotte-Pugh (CTP) (all p < 0.001). In the validation cohorts the predictive accuracy of ANN model (AUR 0.748, 95% CI: 0.673–0.822) was significantly higher than that of MELD (p = 0.0099) and insignificantly higher than that of MELD-Na, CTP and CLIF-ACLF (p > 0.05). For 90-day mortality in the training cohort, the model’s predictive accuracy (AUR 0.913, 95% CI 0.887–0.938) was significantly higher than that of MELD, MELD-Na, CTP and CLIF-ACLF (all p < 0.001). In the validation cohorts, the prediction accuracy of the ANN model (AUR 0.754, 95% CI: 0.697–0.812 was significantly higher than that of MELD (p = 0.019) and insignificantly higher than MELD-Na, CTP and CLIF-ACLF (p > 0.05). Conclusions The established ANN models can more accurately predict short-term mortality risk in patients with HBV- ACLF. The main content has been postered as an abstract at the AASLD Hepatology Conference (10.1002/hep.30257).
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Affiliation(s)
- Yixin Hou
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China
| | - Qianqian Zhang
- Department of Hepatology, The First Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People's Republic of China
| | - Fangyuan Gao
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China
| | - Dewen Mao
- Department of Hepatology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, 530021, People's Republic of China
| | - Jun Li
- Center of Integrative Medicine, Beijing 302 Hospital, Beijing, 100039, People's Republic of China
| | - Zuojiong Gong
- Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, People's Republic of China
| | - Xinla Luo
- Department of Hepatology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhuan, Hubei, 430061, People's Republic of China
| | - Guoliang Chen
- Department of Hepatology, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, Fujian, 361009, People's Republic of China
| | - Yong Li
- Department of Hepatology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250014, People's Republic of China
| | - Zhiyun Yang
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China.
| | - Kewei Sun
- Department of Hepatology, The First Hospital Affiliated to Hunan University of Chinese Medicine, Changsha, Hunan, 410007, People's Republic of China.
| | - Xianbo Wang
- Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, People's Republic of China.
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20
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Mähringer-Kunz A, Wagner F, Hahn F, Weinmann A, Brodehl S, Schotten S, Hinrichs JB, Düber C, Galle PR, Pinto Dos Santos D, Kloeckner R. Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study. Liver Int 2020; 40:694-703. [PMID: 31943703 DOI: 10.1111/liv.14380] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 12/20/2019] [Accepted: 01/08/2020] [Indexed: 02/13/2023]
Abstract
BACKGROUND AND AIMS Deciding when to repeat and when to stop transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC) can be difficult even for experienced investigators. Our aim was to develop a survival prediction model for such patients undergoing TACE using novel machine learning algorithms and to compare it to conventional prediction scores, ART, ABCR and SNACOR. METHODS For this retrospective analysis, 282 patients who underwent TACE for HCC at our tertiary referral centre between January 2005 and December 2017 were included in the final analysis. We built an artificial neural network (ANN) including all parameters used by the aforementioned risk scores and other clinically meaningful parameters. Following an 80:20 split, the first 225 patients were used for training; the more recently treated 20% were used for validation. RESULTS The ANN had a promising performance at predicting 1-year survival, with an area under the ROC curve (AUC) of 0.77 ± 0.13. Internal validation yielded an AUC of 0.83 ± 0.06, a positive predictive value of 87.5% and a negative predictive value of 68.0%. The sensitivity was 77.8% and specificity 81.0%. In a head-to-head comparison, the ANN outperformed the aforementioned scoring systems, which yielded lower AUCs (SNACOR 0.73 ± 0.07, ABCR 0.70 ± 0.07 and ART 0.54 ± 0.08). This difference reached significance for ART (P < .001); for ABCR and SNACOR significance was not reached (P = .143 and P = .201). CONCLUSIONS Artificial neural networks could be better at predicting patient survival after TACE for HCC than traditional scoring systems. Once established, such prediction models could easily be deployed in clinical routine and help determine optimal patient care.
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Affiliation(s)
- Aline Mähringer-Kunz
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Franziska Wagner
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Felix Hahn
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Arndt Weinmann
- Department of Internal Medicine, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.,Clinical Registry Unit (CRU), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sebastian Brodehl
- Institute for Informatics, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sebastian Schotten
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Jan B Hinrichs
- Department of Interventional and Diagnostic Radiology, Hannover Medical School, Hanover, Germany
| | - Christoph Düber
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Peter R Galle
- Department of Internal Medicine, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | | | - Roman Kloeckner
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
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Zhong BY, Ni CF, Ji JS, Yin GW, Chen L, Zhu HD, Guo JH, He SC, Deng G, Zhang Q, Li PC, Yu H, Song JJ, Teng GJ. Nomogram and Artificial Neural Network for Prognostic Performance on the Albumin-Bilirubin Grade for Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization. J Vasc Interv Radiol 2019; 30:330-338. [PMID: 30819473 DOI: 10.1016/j.jvir.2018.08.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 08/07/2018] [Accepted: 08/12/2018] [Indexed: 02/07/2023] Open
Abstract
PURPOSE To construct the albumin-bilirubin (ALBI) grade and the Child-Turcotte-Pugh (CTP) score based on nomograms, as well as to develop an artificial neural network (ANN) to compare the prognostic performance of the 2 scores for hepatocellular carcinoma (HCC) that has undergone transarterial chemoembolization. MATERIALS AND METHODS This multicentric retrospective study included patients with HCC who underwent transarterial chemoembolization monotherapy as an initial treatment at 4 institutions between January 2008 and December 2016. In the training cohort, significant risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The prognostic nomograms and ANN were established and then validated in 2 validation cohorts. RESULTS A total of 838 patients (548, 115, and 175 in the training cohort and validation cohorts 1 and 2, respectively) were included. The median OS was 10.4, 15.7, and 9.2 months in the training cohort and validation cohorts 1 and 2, respectively. In the training cohort, both ALBI grade and CTP score were identified as significant risk factors. The ALBI grade and CTP score based on nomograms were established separately and showed similar prognostic performance when assessed externally in validation cohorts (C-index in validation cohort 1: 0.823 vs 0.802, P = .417; in validation cohort 2: 0.716 vs 0.729, P = .793). ANN showed that ALBI grade had higher importance on survival prediction than CTP score. CONCLUSIONS ALBI grade performs at least no worse than CTP score regarding survival prediction for HCC receiving transarterial chemoembolization. Considering the easy application, ALBI grade has the potential to be regarded as an alternative to CTP score.
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Affiliation(s)
- Bin-Yan Zhong
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China
| | - Cai-Fang Ni
- Department of Interventional Radiology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jian-Song Ji
- Department of Interventional Radiology, Zhejiang University Lishui Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China
| | - Guo-Wen Yin
- Department of Interventional Radiology, Cancer Hospital of Jiangsu Province, Cancer Institution of Jiangsu Province, Nanjing, China
| | - Li Chen
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China
| | - Hai-Dong Zhu
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China
| | - Jin-He Guo
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China
| | - Shi-Cheng He
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China
| | - Gang Deng
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China
| | - Qi Zhang
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China
| | - Pei-Cheng Li
- Department of Interventional Radiology, First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hui Yu
- Department of Interventional Radiology, Cancer Hospital of Jiangsu Province, Cancer Institution of Jiangsu Province, Nanjing, China
| | - Jing-Jing Song
- Department of Interventional Radiology, Zhejiang University Lishui Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China
| | - Gao-Jun Teng
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China.
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Wang KF, Chen YD, Mo LQ, Zhang Z, Liu YJ, Chen JX, Sui XB, Xie T, Wu SX. Integrated traditional Chinese and Western medicine in hepatocellular carcinoma treatment. Shijie Huaren Xiaohua Zazhi 2019; 27:459-466. [DOI: 10.11569/wcjd.v27.i7.459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
As the branches of oncology become more and more detailed, its deficiencies gradually appear in clinical work in recent years. With the development of modern medicine, individualized treatment of hepatocellular carcinoma (HCC) has already been more emphasized in clinical work. This article reviews the diagnosis and treatment of HCC, which can be regarded as an organic systemic disease, based on a concept of integrated medicine. It is suggested that simply eliminating cancer lesions does not mean curing HCC. In clinical practice, it is necessary to use integrative thoughts such as basic study combined with clinical practice, medicine with pharmacy, traditional Chinese medicine with Western medicine, local with whole, etc, so as to find new integrative methods for diagnosis and treatment of HCC.
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Affiliation(s)
- Kai-Feng Wang
- Department of Abdominal Oncology, Hangzhou Cancer Hospital, Hangzhou 310002, Zhejiang Province, China
| | - Yi-Dan Chen
- Department of Abdominal Oncology, Hangzhou Cancer Hospital, Hangzhou 310002, Zhejiang Province, China
| | - Li-Qin Mo
- Department of Abdominal Oncology, Hangzhou Cancer Hospital, Hangzhou 310002, Zhejiang Province, China
| | - Zhen Zhang
- Department of Abdominal Oncology, Hangzhou Cancer Hospital, Hangzhou 310002, Zhejiang Province, China
| | - Ya-Juan Liu
- Department of Abdominal Oncology, Hangzhou Cancer Hospital, Hangzhou 310002, Zhejiang Province, China
| | - Jiang-Xiang Chen
- Institute of Integrative Medicine, Hangzhou Normal University, Hangzhou 310002, Zhejiang Province, China
| | - Xin-Bing Sui
- Institute of Integrative Medicine, Hangzhou Normal University, Hangzhou 310002, Zhejiang Province, China
| | - Tian Xie
- Institute of Integrative Medicine, Hangzhou Normal University, Hangzhou 310002, Zhejiang Province, China
| | - Shi-Xiu Wu
- Department of Abdominal Oncology, Hangzhou Cancer Hospital, Hangzhou 310002, Zhejiang Province, China
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23
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Artificial Neural Networking Model for the Prediction of Early Occlusion of Bilateral Plastic Stent Placement for Inoperable Hilar Cholangiocarcinoma. Surg Laparosc Endosc Percutan Tech 2018; 28:e54-e58. [PMID: 29252936 DOI: 10.1097/sle.0000000000000502] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND This study aimed to determine whether the back-propagation artificial neural network (BP-ANN) model could be constructed to accurately in predicting early occlusion of bilateral plastic stent placement for inoperable hilar cholangiocarcinoma (HCA). METHODS A total of 288 patients from the An Hui provincial Hospital were randomly divided into the training cohort (80%) and the internal testing cohort (20%). The predictive accuracy of the BP-ANN for predicting early occlusion of bilateral plastic stent placement of inoperable HCA was measured by the area under the curve (AUC) on receiver operating characteristic (ROC) curve analysis. The results were compared with those obtained using the conventional multivariate logistic regression analysis. RESULTS Multivariate analysis revealed that cancer stage (P=0.005) and Bismuth stage (P=0.003) were independently and significantly associated with early stent occlusion. In the training cohort, BP-ANN had larger AUC than the multivariate logistic regression model (P=0.00049). In the internal testing cohort, the AUC of the BP-ANN had larger AUC than the multivariate logistic regression model (P=0.02142). CONCLUSIONS This study showed that the BP-ANN model is a good predictive tool. It performed better than the conventional and commonly used statistical model in predicting early occlusion of bilateral plastic stent placement for inoperable HCA.
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Zhang X, Li J, Shen F, Lau WY. Significance of presence of microvascular invasion in specimens obtained after surgical treatment of hepatocellular carcinoma. J Gastroenterol Hepatol 2018; 33:347-354. [PMID: 28589639 DOI: 10.1111/jgh.13843] [Citation(s) in RCA: 186] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 06/04/2017] [Indexed: 02/06/2023]
Abstract
Partial hepatectomy and liver transplantation are potentially curative treatments in selected patients with hepatocellular carcinoma (HCC). Unfortunately, a high postoperative tumor recurrence rate significantly decreases long-term survival outcomes. Among multiple prognostic factors, the presence of microvascular invasion (MVI) has increasingly been recognized to reflect enhanced abilities of local invasion and distant metastasis of HCC. Unfortunately, MVI can only currently be identified through histopathological studies on resected surgical specimens. Accurate preoperative tests to predict the presence of MVI are urgently needed. This paper reviews the current studies on incidence, pathological diagnosis, and classification of MVI; possible mechanisms of MVI formation; and preoperative prediction of the presence of MVI. Furthermore, focusing on how the postoperative management can be improved on histopathologically confirmed patients with HCC with MVI, and the potential roles of using predictive tests to estimate the risk of presence of MVI, helps in preoperative therapeutic decision-making in patients with HCC.
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Affiliation(s)
- Xiaofeng Zhang
- Department of Hepatic Surgery, the Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Jun Li
- Department of Hepatic Surgery, the Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Feng Shen
- Department of Hepatic Surgery, the Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Wan Yee Lau
- Department of Hepatic Surgery, the Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China.,Faculty of Medicine, the Chinese University of Hong Kong, Sha Tin, Hong Kong
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Han X, Ge M, Dong J, Wang Z, He J, Yang R. The relationship between left ventricle myocardial performance index of healthy women and geographical factors. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2015; 59:1557-1565. [PMID: 25663471 DOI: 10.1007/s00484-015-0962-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Revised: 01/08/2015] [Accepted: 01/08/2015] [Indexed: 06/04/2023]
Abstract
The study focused on the relationship between geographical factors and left ventricular myocardial performance index (MPI)reference value, analyed the different distribution of MPI, and then provided a scientific basis for clinical examination. This study collected MPI reference values of 2545 healthy women from 91 cities in China, used the Moran's index to determin the spatial relationship, selected 25 geographical factors, examined the significance between MPI and geographical factors by correlation analysis, through the significance test, and extracted seven significant factors to build the artificial neural network (ANN) model and principal component analysis (PCA) model. Through calculating the relative error, the ANN model was chosen as the better model to predict the values. By normality test for the predicted values, the geographical distribution was made by disjunctive kriging interpolation. The predicted values decrease from north to south. If geographical factors are obtained in one location, the MPI of healthy women in this area can be predicted by the ANN model. Synthesizing the influence of physiological and geographical could be more scientific to formulate the MPI reference value.
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Affiliation(s)
- Xiao Han
- College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China.
| | - Miao Ge
- College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China.
| | - Jie Dong
- College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Zixuan Wang
- College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Jinwei He
- College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
| | - Rongrong Yang
- College of Tourist and Environment Science, Shaanxi Normal University, Xi'an, 710119, Shaanxi, China
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