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Gao Y, Yang X, Li H, Ding DW. A knowledge-enhanced interpretable network for early recurrence prediction of hepatocellular carcinoma via multi-phase CT imaging. Int J Med Inform 2024; 189:105509. [PMID: 38851131 DOI: 10.1016/j.ijmedinf.2024.105509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/20/2024] [Accepted: 05/28/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND Predicting early recurrence (ER) of hepatocellular carcinoma (HCC) accurately can guide treatment decisions and further enhance survival. Computed tomography (CT) imaging, analyzed by deep learning (DL) models combining domain knowledge, has been employed for the prediction. However, these DL models utilized late fusion, restricting the interaction between domain knowledge and images during feature extraction, thereby limiting the prediction performance and compromising decision-making interpretability. METHODS We propose a novel Vision Transformer (ViT)-based DL network, referred to as Dual-Style ViT (DSViT), to augment the interaction between domain knowledge and images and the effective fusion among multi-phase CT images for improving both predictive performance and interpretability. We apply the DSViT to develop pre-/post-operative models for predicting ER. Within DSViT, to balance the utilization between domain knowledge and images within DSViT, we propose an adaptive self-attention mechanism. Moreover, we present an attention-guided supervised learning module for balancing the contributions of multi-phase CT images to prediction and a domain knowledge self-supervision module for enhancing the fusion between domain knowledge and images, thereby further improving predictive performance. Finally, we provide the interpretability of the DSViT decision-making. RESULTS Experiments on our multi-phase data demonstrate that DSViTs surpass the existing models across multiple performance metrics and provide the decision-making interpretability. Additional validation on a publicly available dataset underscores the generalizability of DSViT. CONCLUSIONS The proposed DSViT can significantly improve the performance and interpretability of ER prediction, thereby fortifying the trustworthiness of artificial intelligence tool for HCC ER prediction in clinical settings.
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Affiliation(s)
- Yu Gao
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China
| | - Xue Yang
- First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China; Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China.
| | - Da-Wei Ding
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China.
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2
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Altaf A, Endo Y, Munir MM, Khan MMM, Rashid Z, Khalil M, Guglielmi A, Ratti F, Marques H, Cauchy F, Lam V, Poultsides G, Kitago M, Popescu I, Martel G, Gleisner A, Hugh T, Shen F, Endo I, Pawlik TM. Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma. HPB (Oxford) 2024; 26:1040-1050. [PMID: 38796346 DOI: 10.1016/j.hpb.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 05/09/2024] [Accepted: 05/12/2024] [Indexed: 05/28/2024]
Abstract
OBJECTIVE We sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR). METHODS HCC patients who underwent HR between 2000-2020 were identified from a multi-institutional database. NTR was defined as recurrence beyond Milan Criteria. Different machine learning (ML) and deep learning (DL) techniques were used to develop and validate two prediction models for NTR, one using only preoperative factors and a second using both preoperative and postoperative factors. RESULTS Overall, 1763 HCC patients were included. Among 877 patients with recurrence, 364 (41.5%) patients developed NTR. An ensemble AI model demonstrated the highest area under ROC curves (AUC) of 0.751 (95% CI: 0.719-0.782) and 0.717 (95% CI:0.653-0.782) in the training and testing cohorts, respectively which improved to 0.858 (95% CI: 0.835-0.884) and 0.764 (95% CI: 0.704-0.826), respectively after incorporation of postoperative pathologic factors. Radiologic tumor burden score and pathological microvascular invasion were the most important preoperative and postoperative factors, respectively to predict NTR. Patients predicted to develop NTR had overall 1- and 5-year survival of 75.6% and 28.2%, versus 93.4% and 55.9%, respectively, among patients predicted to not develop NTR (p < 0.0001). CONCLUSION The AI preoperative model may help inform decision of HR versus LT for HCC, while the combined AI model can frame individualized postoperative care (https://altaf-pawlik-hcc-ntr-calculator.streamlit.app/).
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Affiliation(s)
- Abdullah Altaf
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Yutaka Endo
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Muhammad M Munir
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Muhammad Muntazir M Khan
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Zayed Rashid
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Mujtaba Khalil
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | | | | | - Hugo Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | - François Cauchy
- Department of Hepatobiliopancreatic Surgery, APHP, Beaujon Hospital, Clichy, France
| | - Vincent Lam
- Department of Surgery, Westmead Hospital, Sydney, NSW, Australia
| | - George Poultsides
- Department of Surgery, Stanford University, Stanford, CA, United States
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | - Irinel Popescu
- Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania
| | | | - Ana Gleisner
- Department of Surgery, University of Colorado, Aurora, CO, United States
| | - Tom Hugh
- Department of Surgery, School of Medicine, The University of Sydney, Sydney, NSW, Australia
| | - Feng Shen
- Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Itaru Endo
- Department of Surgery, Yokohama City University School of Medicine, Yokohama, Japan
| | - Timothy M Pawlik
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
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Rabindranath M, Naghibzadeh M, Zhao X, Holdsworth S, Brudno M, Sidhu A, Bhat M. Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold? Transplantation 2024; 108:1700-1708. [PMID: 39042768 DOI: 10.1097/tp.0000000000004876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.
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Affiliation(s)
- Madhumitha Rabindranath
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Maryam Naghibzadeh
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Xun Zhao
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Sandra Holdsworth
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Michael Brudno
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Aman Sidhu
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
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4
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Han JW, Lee SK, Kwon JH, Nam SW, Yang H, Bae SH, Kim JH, Nam H, Kim CW, Lee HL, Kim HY, Lee SW, Lee A, Chang UI, Song DS, Kim SH, Song MJ, Sung PS, Choi JY, Yoon SK, Jang JW. A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma. Clin Cancer Res 2024; 30:2812-2821. [PMID: 38639918 DOI: 10.1158/1078-0432.ccr-23-3978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/29/2024] [Accepted: 04/16/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment. EXPERIMENTAL DESIGN We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C). RESULTS The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively-significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab-bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup. CONCLUSIONS ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.
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Affiliation(s)
- Ji W Han
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Soon K Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Jung H Kwon
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Soon W Nam
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea
| | - Hyun Yang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Si H Bae
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Ji H Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Heechul Nam
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Chang W Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea
| | - Hae L Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Hee Y Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Sung W Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Ahlim Lee
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - U I Chang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Do S Song
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Seok-Hwan Kim
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon, Republic of Korea
| | - Myeong J Song
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon, Republic of Korea
| | - Pil S Sung
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jong Y Choi
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung K Yoon
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jeong W Jang
- The Catholic University Liver Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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5
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Hahn M, Herber A, Berg T, Seehofer D, van Bömmel F. Enhancing HCC recurrence prediction after liver transplantation: From DCP plus AFP-L3 model to GALAD score. J Hepatol 2024; 80:e271-e272. [PMID: 38110004 DOI: 10.1016/j.jhep.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 12/09/2023] [Indexed: 12/20/2023]
Affiliation(s)
- Magdalena Hahn
- Division of Hepatology, Department of Medicine II, Leipzig University Medical Center, 04103 Leipzig, Germany.
| | - Adam Herber
- Division of Hepatology, Department of Medicine II, Leipzig University Medical Center, 04103 Leipzig, Germany
| | - Thomas Berg
- Division of Hepatology, Department of Medicine II, Leipzig University Medical Center, 04103 Leipzig, Germany; University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, 04103 Leipzig, Germany
| | - Daniel Seehofer
- University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, 04103 Leipzig, Germany; Department of Visceral, Transplant, Thoracic and Vascular Surgery, Leipzig University Medical Center, 04103 Leipzig, Germany
| | - Florian van Bömmel
- Division of Hepatology, Department of Medicine II, Leipzig University Medical Center, 04103 Leipzig, Germany; University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, 04103 Leipzig, Germany
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6
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Dajti G, Germinario G, Prosperi E, Siniscalchi A, Vasuri F, Valente S, Odaldi F, Maroni L, Serenari M, Bertuzzo V, Laurenzi A, Del Gaudio M, Cescon M, Ravaioli M. The role of cold ischemia time and hypothermic perfusion in predicting early hepatocellular carcinoma recurrences after liver transplantation. Artif Organs 2024; 48:619-625. [PMID: 38270476 DOI: 10.1111/aor.14715] [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: 09/23/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 01/26/2024]
Abstract
AIM The aim of the study was to identify predictors of early tumor recurrence in patients with hepatocellular carcinoma (HCC) after liver transplantation (LT). METHODS Retrospective cohort study in 237 consecutive liver recipients with HCC between 2016 and 2021. Multivariate logistic analysis was performed to identify predictors of early HCC recurrences. The impact of hypothermic-oxygenated perfusion (HOPE) on outcome was analyzed after propensity score weighting. RESULTS Early recurrences were observed in 15 cases. Microvascular invasion (OR 3.737, 95% CI 1.246-11.206, p = 0.019) and cold ischemia time (OR 1.155, 95% CI 1.001-1.333, p = 0.049) were independently associated with a lower risk of HCC recurrences. After balancing for relevant variables, patients in the HOPE group had lower rates of tumor recurrence (weighted OR 0.126, 95% CI 0.016-0.989, p = 0.049) and higher recurrence free survival (weighted HR 0.132, 95% CI 0.017-0.999, p = 0.050). CONCLUSION Reducing cold ischemia time and graft perfusion with HOPE can lead to lower rates of early HCC recurrences and higher recurrence-free survival.
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Affiliation(s)
- Gerti Dajti
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
| | - Giuliana Germinario
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Enrico Prosperi
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Antonio Siniscalchi
- Intensive Care Unit, IRCSS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Francesco Vasuri
- Department of Specialized, Experimental and Diagnostic Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Sabrina Valente
- Department of Specialized, Experimental and Diagnostic Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Federica Odaldi
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Lorenzo Maroni
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Matteo Serenari
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Valentina Bertuzzo
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Andrea Laurenzi
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Massimo Del Gaudio
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Matteo Cescon
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Matteo Ravaioli
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
- General Surgery and Transplantation Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
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7
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Lerut J. Liver transplantation and liver resection as alternative treatments for primary hepatobiliary and secondary liver tumors: Competitors or allies? Hepatobiliary Pancreat Dis Int 2024; 23:111-116. [PMID: 38195351 DOI: 10.1016/j.hbpd.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 12/28/2023] [Indexed: 01/11/2024]
Affiliation(s)
- Jan Lerut
- Institute for Experimental and Clinical Research (IREC), Université catholique Louvain (UCL), Avenue Hippocrate 56, 1200 Woluwe Saint Pierre, Brussels, Belgium.
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8
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Wiest IC, Gilbert S, Kather JN. From research to reality: The role of artificial intelligence applications in HCC care. Clin Liver Dis (Hoboken) 2024; 23:e0136. [PMID: 38567094 PMCID: PMC10986906 DOI: 10.1097/cld.0000000000000136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/12/2024] [Indexed: 04/04/2024] Open
Affiliation(s)
- Isabella C. Wiest
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephen Gilbert
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jakob N. Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
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9
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Lai Q, De Stefano C, Emond J, Bhangui P, Ikegami T, Schaefer B, Hoppe‐Lotichius M, Mrzljak A, Ito T, Vivarelli M, Tisone G, Agnes S, Ettorre GM, Rossi M, Tsochatzis E, Lo CM, Chen C, Cillo U, Ravaioli M, Lerut JP. Development and validation of an artificial intelligence model for predicting post-transplant hepatocellular cancer recurrence. Cancer Commun (Lond) 2023; 43:1381-1385. [PMID: 37904670 PMCID: PMC10693300 DOI: 10.1002/cac2.12468] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 06/25/2023] [Accepted: 07/13/2023] [Indexed: 11/01/2023] Open
Affiliation(s)
- Quirino Lai
- General Surgery and Organ Transplantation Unit, AOU Policlinico Umberto ISapienza University of RomeRomeItaly
| | | | - Jean Emond
- Division of Liver Transplantation and Hepatobiliary Surgery, Department of SurgeryWeill Cornell Medicine‐Columbia UniversityNew YorkUS
| | - Prashant Bhangui
- Medanta Institute of Liver Transplantation and Regenerative MedicineMedanta‐The MedicityGurgaonIndia
| | - Toru Ikegami
- Department of Surgery and ScienceKyushu UniversityFukuokaJapan
| | - Benedikt Schaefer
- Department of Medicine I, Gastroenterology, Hepatology and EndocrinologyMedical University of InnsbruckInnsbruckAustria
| | - Maria Hoppe‐Lotichius
- Klinik für Allgemein‐, Viszeral‐ und TransplantationschirurgieUniversitätsmedizin MainzMainzGermany
| | - Anna Mrzljak
- Liver Transplant CentreMerkur University of ZagrebZagrebCroatia
| | - Takashi Ito
- Division of Hepato‐Biliary‐Pancreatic and Transplant Surgery, Department of SurgeryGraduate School of MedicineKyotoJapan
| | - Marco Vivarelli
- Unit of Hepatobiliary Surgery and Transplantation, AOU Ospedali RiunitiPolytechnic University of MarcheAnconaItaly
| | - Giuseppe Tisone
- Department of Surgical Sciences and Medical Sciences University of Rome‐Tor VergataRomeItaly
| | - Salvatore Agnes
- Liver Unit, Department of SurgeryCatholic University‐Fondazione Policlinico Universitario Agostino Gemelli IRCCSRomeItaly
| | | | - Massimo Rossi
- General Surgery and Organ Transplantation Unit, AOU Policlinico Umberto ISapienza University of RomeRomeItaly
| | - Emmanuel Tsochatzis
- UCL Institute for Liver and Digestive Health and Royal Free Sheila Sherlock Liver CentreRoyal Free HospitalLondonUK
| | - Chung Mau Lo
- Hong Kong University–Department of SurgeryQueen Mary Hospital, University of Hong KongHong KongP. R. China
| | - Chao‐Long Chen
- Department of SurgeryKaohsiung Chang Gung Memorial Hospital, Chang Gung University College of MedicineKaohsiung, TaiwanP. R. China
| | - Umberto Cillo
- Department of Surgery, Oncology and GastroenterologyUniversity of PaduaPaduaItaly
| | - Matteo Ravaioli
- Department of General Surgery and TransplantationIRCCS, Azienda Ospedaliero‐Universitaria di Bologna, Bologna UniversityBolognaItaly
| | - Jan Paul Lerut
- Institut de Recherche CliniqueUniversité catholique de LouvainBrusselsBelgium
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10
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Bezjak M, Kocman B, Jadrijević S, Filipec Kanižaj T, Antonijević M, Dalbelo Bašić B, Mikulić D. Use of machine learning models for identification of predictors of survival and tumour recurrence in liver transplant recipients with hepatocellular carcinoma. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:345. [PMID: 37675331 PMCID: PMC10477658 DOI: 10.21037/atm-22-6469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/19/2023] [Indexed: 09/08/2023]
Abstract
Background Hepatocellular carcinoma (HCC) is one of the leading indications for liver transplantation (LT) however, selection criteria remain controversial. We aimed to identify survival factors and predictors for tumour recurrence using machine learning (ML) methods. We also compared ML models to the Cox regression model. Methods Thirty pretransplant donor and recipient general and tumour specific parameters were analysed from 170 patients who underwent orthotopic liver transplantation for HCC between March 2013 and December 2019 at the University Hospital Merkur, Zagreb. Survival rates were calculated using the Kaplan-Meier method and multivariate analysis was performed using the Cox proportional hazards regression model. Data was also processed through Coxnet (a regularized Cox regression model), Random Survival Forest (RSF), Survival Support Vector Machine (SVM) and Survival Gradient Boosting models, which included pre-processing, variable selection, imputation of missing data, training and cross-validation of the models. The cross-validated concordance index (CI) was used as an evaluation metric and to determine the best performing model. Results Kaplan-Meier curves for 5-year survival time showed survival probability of 80% for recipient survival and 82% for graft survival. The 5-year HCC recurrence was observed in 19% of patients. The best predictive accuracy was observed in the RSF model with CI of 0.72, followed by the Survival SVM model (CI 0.70). Overall ML models outperform the Cox regression model with respect to their limitations. Random Forest analysis provided several relevant outcome predictors: alpha fetoprotein (AFP), donor C-reactive protein (CRP), recipient age and neutrophil to lymphocyte ratio (NLR). Cox multivariate analysis showed similarities with RSF models in identifying detrimental variables. Some variables such as donor age and number of transarterial chemoembolization treatments (TACE) were pointed out, but these were not influential in our RSF model. Conclusions Using ML methods in addition to classical statistical analysis, it is possible to develop sufficient prognostic models, which, compared to established risk scores, could help us quantify survival probability and make changes in organ utilization.
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Affiliation(s)
- Miran Bezjak
- Division of Abdominal Surgery and Organ Transplantation, Department of Surgery, University Hospital Merkur, Zagreb, Croatia
| | - Branislav Kocman
- Division of Abdominal Surgery and Organ Transplantation, Department of Surgery, University Hospital Merkur, Zagreb, Croatia
| | - Stipislav Jadrijević
- Division of Abdominal Surgery and Organ Transplantation, Department of Surgery, University Hospital Merkur, Zagreb, Croatia
| | - Tajana Filipec Kanižaj
- Division of Gastroenterology, Department of Internal Medicine, University Hospital Merkur, Zagreb, Croatia
| | | | - Bojana Dalbelo Bašić
- Faculty of Electrical Engineering and Computing, Department of Electronics, Microelectronics, Computer and Intelligent Systems, Zagreb, Croatia
| | - Danko Mikulić
- Division of Abdominal Surgery and Organ Transplantation, Department of Surgery, University Hospital Merkur, Zagreb, Croatia
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11
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Lee JS, Kim J, Rhu J, Choi GS, Joh JW. Long-Term Outcomes of Liver Transplantation in Hepatocellular Carcinoma with Bile Duct Tumor Thrombus: A Comparison with Portal Vein Tumor Thrombus. Cancers (Basel) 2023; 15:4225. [PMID: 37686500 PMCID: PMC10486955 DOI: 10.3390/cancers15174225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Liver transplantation (LT) in patients with hepatocellular carcinoma (HCC) with bile duct tumor thrombus (BDTT) remains controversial. This study analyzed the recurrence and overall survival rates through long-term results after LT in HCC patients with BDTT and compared the results after LT in HCC patients with portal vein tumor thrombus (PVTT). We performed a retrospective study of 45 patients with PVTT, 16 patients with BDTT, and 11 patients with coexisting PVTT and BDTT among HCC patients who underwent LT at a single center from 1999 to 2020. The HCC recurrence rates were 40.4% at 1 year, 30.3.3% at 2 years, and 27.6% at 3 years in the PVTT group; 66.7%, 53.3%, and 46.7% in the BDTT group; and 22.2%, 22.2%, and 0% in the coexisting group (p = 0.183). Overall patient survival rates were 68.4% at 1 year, 54.3% at 2 years, and 41.7% at 3 years in the PVTT group; 81.3%, 62.5%, and 48.2% in the BDTT group; and 63.6%, 27.3%, and 0% in the coexisting group (p = 0.157). In the multivariate analysis, the pre-transplantation model for tumor recurrence after liver transplantation (MoRAL) score and model for end-stage liver disease (MELD) score were found to be independent risk factors for recurrence and survival in all groups. HCC patients with BDTT showed no difference in recurrence and survival compared with HCC patients with PVTT at the long-term follow-up after LT.
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Affiliation(s)
- Ji Soo Lee
- Department of Surgery, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul 05355, Republic of Korea;
| | - Jongman Kim
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.R.); (G.-S.C.)
| | - Jinsoo Rhu
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.R.); (G.-S.C.)
| | - Gyu-Seong Choi
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.R.); (G.-S.C.)
| | - Jae-Won Joh
- Department of Surgery, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Republic of Korea;
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12
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Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol 2023; 78:1216-1233. [PMID: 37208107 DOI: 10.1016/j.jhep.2023.01.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 05/21/2023]
Abstract
Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.
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Affiliation(s)
- Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
| | - Madhumitha Rabindranath
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Beatriz Sordi Chara
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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13
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Calderon-Martinez E, Landazuri-Navas S, Vilchez E, Cantu-Hernandez R, Mosquera-Moscoso J, Encalada S, Al lami Z, Zevallos-Delgado C, Cinicola J. Prognostic Scores and Survival Rates by Etiology of Hepatocellular Carcinoma: A Review. J Clin Med Res 2023; 15:200-207. [PMID: 37187717 PMCID: PMC10181349 DOI: 10.14740/jocmr4902] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common cancer and ranks sixth among all malignancies worldwide. Risk factors for HCC can be classified as infectious or behavioral. Viral hepatitis and alcohol abuse are currently the most common risk factors for HCC; however, nonalcoholic liver disease is expected to become the most common cause of HCC in upcoming years. HCC survival rates vary according to the causative risk factors. As in any malignancy, staging is crucial in making therapeutic decisions. The selection of a specific score should be individualized according to patient characteristics. In this review, we summarize the current data on epidemiology, risk factors, prognostic scores, and survival in HCC.
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Affiliation(s)
- Evelyn Calderon-Martinez
- Department of Internal Medicine, UPMC Harrisburg, Harrisburg, PA, USA
- Corresponding Author: Evelyn Calderon-Martinez, Department of Internal Medicine, UPMC Harrisburg, Harrisburg, PA 17104, USA.
| | | | | | - Raul Cantu-Hernandez
- Department of Internal Medicine, Monterrey Institute of Technology and Higher Studies, Mexico
| | | | - Sebastian Encalada
- Department of Internal Medicine, University of the Americas, Quito, Ecuador
| | - Zahraa Al lami
- Department of Internal Medicine, University of Baghdad, College of Medicine, Iraq
| | | | - John Cinicola
- Department of Internal Medicine, UPMC Harrisburg, Harrisburg, PA, USA
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14
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Fung J, DiSabato D, Liao CY, Ahmed O, Pillai A. Perspective: Advances in liver transplantation for hepatocellular carcinoma - A prototype for transplant oncology. Hepatobiliary Pancreat Dis Int 2023; 22:4-6. [PMID: 36163102 DOI: 10.1016/j.hbpd.2022.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/05/2022] [Indexed: 02/07/2023]
Affiliation(s)
- John Fung
- Liver Tumor Center, The University of Chicago Medicine, Chicago 60637, IL, USA.
| | - Diego DiSabato
- Liver Tumor Center, The University of Chicago Medicine, Chicago 60637, IL, USA
| | - Chih-Yi Liao
- Liver Tumor Center, The University of Chicago Medicine, Chicago 60637, IL, USA
| | - Osmanuddin Ahmed
- Liver Tumor Center, The University of Chicago Medicine, Chicago 60637, IL, USA
| | - Anjana Pillai
- Liver Tumor Center, The University of Chicago Medicine, Chicago 60637, IL, USA
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15
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Lu Y, Ren S, Jiang J. Development and validation of a nomogram for survival prediction in hepatocellular carcinoma after partial hepatectomy. BMC Surg 2023; 23:27. [PMID: 36717904 PMCID: PMC9885608 DOI: 10.1186/s12893-023-01922-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 01/19/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND The prognosis for hepatocellular carcinoma (HCC) is complex due to its high level of heterogeneity, even after radical resection. This study was designed to develop and validate a prognostic nomogram for predicting the postoperative prognosis for HCC patients following partial hepatectomy. PATIENTS AND METHODS We extracted data on HCC patients and randomly divided them into two groups (primary and validation cohorts), using the Surveillance, Epidemiology and End Results (SEER) database. We developed the prediction model based on the data of the primary cohort and prognostic factors were evaluated using univariate and multivariate Cox regression analysis. A nomogram was constructed for predicting the 1-, 3-, and 5-year survival probability of HCC patients after surgery based on the results of the multivariate Cox regression analysis. The performance of the nomogram was evaluated in terms of its discrimination and calibration. To validated the model, discrimination and calibration were also evaluated in the validation cohort. Decision curve analysis (DCA) was performed to assess the clinical utility of the nomogram. RESULTS A total of 890 patients who underwent partial hepatectomy for HCC were included in the study. The primary cohort enrolled 628 patients with a median follow-up time of 39 months, the 1-, 3-, and 5-year survival rate were 95.4%, 52.7% and 25.8% during follow-up. Multivariate Cox regression analysis showed that differentiation, tumor size, AFP and fibrosis were independently association with the prognosis of HCC patients after partial hepatectomy. The nomogram showed a moderate discrimination ith a C-index of 0.705 (95% CI 0.669 to 0.742), and good calibration. Similar discrimination with a C-index of 0.681 (95% CI 0.625 to 0.737), and calibration were also observed in the validation cohort. Decision curve analysis showed that the nomogram could be useful to predicting the prognosis in HCC patients following partial hepatectomy. CONCLUSIONS The proposed nomogram is highly predictive and has moderate calibration and discrimination, potentially contributing to the process of managing HCC patients after partial hepatectomy in an individualized way.
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Affiliation(s)
- Yang Lu
- grid.412594.f0000 0004 1757 2961Department of Infectious Diseases, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021 Guangxi China ,grid.413431.0Department of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China
| | - Shuang Ren
- grid.413431.0Department of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, China
| | - Jianning Jiang
- grid.412594.f0000 0004 1757 2961Department of Infectious Diseases, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021 Guangxi China
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16
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Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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17
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Wang F, Chen Q, Chen Y, Zhu Y, Zhang Y, Cao D, Zhou W, Liang X, Yang Y, Lin L, Hu H. A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2023; 49:156-164. [PMID: 36333180 DOI: 10.1016/j.ejso.2022.08.036] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/22/2022] [Accepted: 08/30/2022] [Indexed: 01/24/2023]
Abstract
BACKGROUND Accurate preoperative identification of the microvascular invasion (MVI) can relieve the pressure from personalized treatment adaptation and improve the poor prognosis for hepatocellular carcinoma (HCC). This study aimed to develop and validate a novel multimodal deep learning (DL) model for predicting MVI based on multi-parameter magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CT). METHODS A total of 397 HCC patients underwent both CT and MRI examinations before surgery. We established the radiological models (RCT, RMRI) by support vector machine (SVM), DL models (DLCT_ALL, DLMRI_ALL, DLCT + MRI) by ResNet18. The comprehensive model (CALL) involving multi-modality DL features and clinical and radiological features was constructed using SVM. Model performance was quantified by the area under the receiver operating characteristic curve (AUC) and compared by net reclassification index (NRI) and integrated discrimination improvement (IDI). RESULTS The DLCT + MRI model exhibited superior predicted efficiency over single-modality models, especially over the DLCT_ALL model (AUC: 0.819 vs. 0.742, NRI > 0, IDI > 0). The DLMRI_ALL model improved the performance over the RMRI model (AUC: 0.794 vs. 0.766, NRI > 0, IDI < 0), but no such difference was found between the DLCT_ALL model and RCT model (AUC: 0.742 vs. 0.710, NRI < 0, IDI < 0). Furthermore, both the DLCT + MRI and CALL models revealed the prognostic power in recurrence-free survival stratification (P < 0.001). CONCLUSION The proposed DLCT + MRI model showed robust capability in predicting MVI and outcomes for HCC. Besides, the identification ability of the multi-modality DL model was better than any single modality, especially for CT.
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Affiliation(s)
- Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, PR China.
| | - Qingqing Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, PR China
| | - Yinan Chen
- SenseTime Research, 200030, Shanghai, PR China
| | - Yajing Zhu
- SenseTime Research, 200030, Shanghai, PR China
| | - Yuanyuan Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, PR China; Medical College, Shaoxing University, 312000, Shaoxing, PR China
| | - Dan Cao
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, PR China; Department of Radiology, The Fourth Affiliated Hospital, International Institutes of Medicine, Zhejiang University School of Medicine, Yiwu, PR China
| | - Wei Zhou
- Department of Radiology, Huzhou Central Hospital, Affiliated to Huzhou University, 313000, Huzhou, PR China
| | - Xiao Liang
- Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, PR China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital, Wenzhou Medical University, 325000, Wenzhou, PR China.
| | - Lanfen Lin
- College of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, PR China.
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, PR China.
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18
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Kim SJ, Kim JM. Prediction models of hepatocellular carcinoma recurrence after liver transplantation: A comprehensive review. Clin Mol Hepatol 2022; 28:739-753. [PMID: 35468711 PMCID: PMC9597239 DOI: 10.3350/cmh.2022.0060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/15/2022] [Indexed: 01/05/2023] Open
Abstract
Liver transplantation (LT) is one of the most effective treatments for hepatocellular carcinoma (HCC). Although LT eliminates HCC and greatly reduces recurrence, some patients experience recurrence after LT. Criteria and models for screening patients with a high probability of HCC recurrence after LT, starting with the Milan criteria, have been published. These models have changed over time, but a standard has not been established. We summarized HCC prediction models after LT by focusing on the application of radiologic, serologic, and pathologic factors and recent trends. This review will look at studies that are based on living donor LT and deceased donor LT, as well as studies that downstaging procedures have been performed preoperatively. This ultimately aims to help make decisions for evaluating the HCC state and selecting candidates for LT according to the circumstances of each transplantation center.
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Affiliation(s)
- Sang Jin Kim
- Department of Surgery, Korea University College of Medicine, Seoul, Korea,Division of Hepatobiliopancreas and Transplant Surgery, Korea University Ansan Hospital, Republic of Korea, Ansan, Korea
| | - Jong Man Kim
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea,Corresponding author : Jong Man Kim Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea Tel: +82-2-3410-1719, Fax: +82-2-3410-0040, E-mail:
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19
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The impact of biological features for a better prediction of posttransplant hepatocellular cancer recurrence. Curr Opin Organ Transplant 2022; 27:305-311. [PMID: 36354256 DOI: 10.1097/mot.0000000000000955] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
PURPOSE OF REVIEW Morphological criteria (i.e., Milan Criteria) have been considered for a long time to be the best tool for selecting patients with hepatocellular cancer (HCC) waiting for liver transplantation (LT). In the last ten years, a refinement of the selection criteria has been observed, with the introduction of biological tumor characteristics enabling to enlarge the number of potential transplant candidates and to select LT candidates with a lower risk of posttransplant recurrence. RECENT FINDINGS Several biological tumor aspects have been explored and validated in international cohorts to expand the ability to predict patients at high risk for recurrence. Alpha-fetoprotein, radiological response to locoregional treatments, and other more recently proposed markers have been principally explored. Moreover, more complex statistical approaches (i.e., deep learning) have been advocated to explore the nonlinear intercorrelations between the investigated features. SUMMARY The addition of biological aspects to morphology has improved the ability to discriminate among high- and low-risk patients for recurrence. New prognostic algorithms based on the more sophisticated artificial intelligence approach are further improving the capability to select LT candidates with HCC.
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20
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Shi HY, Lee KT, Chiu CC, Wang JJ, Sun DP, Lee HH. 5-year recurrence prediction after hepatocellular carcinoma resection: deep learning vs. Cox regression models. Am J Cancer Res 2022; 12:2876-2890. [PMID: 35812048 PMCID: PMC9251698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023] Open
Abstract
Deep learning algorithms have yet to be used for predicting clinical prognosis after cancer surgery. Therefore, this study compared performance indices and permutation importance of potential confounders in three models for predicting 5-year recurrence after hepatocellular carcinoma (HCC) resection: a deep-learning deep neural network (DNN) model, a recurrent neural network (RNN) model, and a Cox proportional hazard (CPH) regression model. Data for 725 patients who had received HCC resection at three medical centers in southern Taiwan between April, 2011, and December, 2015, were randomly divided into three datasets: a training dataset containing data for 507 subjects was used for model development, a testing dataset containing data for 109 subjects was used for internal validation, and a validating dataset containing data for 109 subjects was used for external validation. Feature importance analysis was also performed to identify potential predictors of recurrence after HCC resection. Univariate Cox proportional hazards regression analyses were performed to identify potential significant predictors of 5-year recurrence after HCC resection, which were included in the forecasting models (P < 0.05). All performance indices for the DNN model were significantly higher than those for the RNN model and the conventional CPH model (P < 0.001). The most important potential predictor of 5-year recurrence after HCC resection was surgeon volume followed by, in order of importance, hospital volume, preoperative Beck Depression Scale score, preoperative Beck Anxiety Scale score, co-residence with family, tumor stage, and tumor size. The feature importance analysis performed to investigate interpretability in this study elucidated the potential use of deep learning models for predicting recurrence after HCC resection and for identifying predictors of recurrence. Further experiments using the proposed DNN model would clarify its potential uses for developing, promoting, and improving health policies for treating HCC patients after surgery.
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Affiliation(s)
- Hon-Yi Shi
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical UniversityKaohsiung 80708, Taiwan
- Department of Business Management, National Sun Yat-sen UniversityKaohsiung 80420, Taiwan
- Department of Medical Research, Kaohsiung Medical University HospitalKaohsiung 80708, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical UniversityTaichung 40402, Taiwan
| | - King-The Lee
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical UniversityKaohsiung 80708, Taiwan
- Hepatobiliary-Pancreatic Surgery, Park One International HospitalKaohsiung 81357, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer HospitalKaohsiung 82445, Taiwan
- School of Medicine, College of Medicine, I-Shou UniversityKaohsiung 82445, Taiwan
| | - Jhi-Joung Wang
- Department of Medical Research, Chi Mei Medical CenterYongkang, Tainan 71004, Taiwan
- Allied AI Biomed Center, Southern Taiwan University of Science and TechnologyTainan 71005, Taiwan
| | - Ding-Ping Sun
- Department of Surgery, Chi Mei Medical CenterYongkang, Tainan 71004, Taiwan
| | - Hao-Hsien Lee
- Department of Surgery, Chi Mei Medical CenterLiouying, Tainan 73658, Taiwan
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21
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Tran J, Sharma D, Gotlieb N, Xu W, Bhat M. Application of machine learning in liver transplantation: a review. Hepatol Int 2022; 16:495-508. [PMID: 35020154 DOI: 10.1007/s12072-021-10291-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 12/15/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk. AIM In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation. METHODS Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. CONCLUSION ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
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Affiliation(s)
- Jason Tran
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Divya Sharma
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Wei Xu
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
- Division of Gastroenterology, Department of Medicine, University of Toronto, 585 University Avenue, Toronto, ON, M5G 2N2, Canada.
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22
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Calderaro J, Seraphin TP, Luedde T, Simon TG. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma. J Hepatol 2022; 76:1348-1361. [PMID: 35589255 PMCID: PMC9126418 DOI: 10.1016/j.jhep.2022.01.014] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 12/26/2021] [Accepted: 01/14/2022] [Indexed: 12/13/2022]
Abstract
Hepatocellular carcinoma (HCC) currently represents the fifth most common malignancy and the third-leading cause of cancer-related death worldwide, with incidence and mortality rates that are increasing. Recently, artificial intelligence (AI) has emerged as a unique opportunity to improve the full spectrum of HCC clinical care, by improving HCC risk prediction, diagnosis, and prognostication. AI approaches include computational search algorithms, machine learning (ML) and deep learning (DL) models. ML consists of a computer running repeated iterations of models, in order to progressively improve performance of a specific task, such as classifying an outcome. DL models are a subtype of ML, based on neural network structures that are inspired by the neuroanatomy of the human brain. A growing body of recent data now apply DL models to diverse data sources - including electronic health record data, imaging modalities, histopathology and molecular biomarkers - to improve the accuracy of HCC risk prediction, detection and prediction of treatment response. Despite the promise of these early results, future research is still needed to standardise AI data, and to improve both the generalisability and interpretability of results. If such challenges can be overcome, AI has the potential to profoundly change the way in which care is provided to patients with or at risk of HCC.
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Affiliation(s)
- Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Henri Mondor University Hospital, Department of Pathology, Créteil, France; Inserm U955 and Univ Paris Est Creteil, INSERM, IMRB, 94010, Creteil, France
| | - Tobias Paul Seraphin
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
| | - Tom Luedde
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Duesseldorf, Medical Faculty at Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
| | - Tracey G. Simon
- Liver Center, Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Clinical and Translational Epidemiology Unit (CTEU), Massachusetts General Hospital, Boston, MA, USA
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23
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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: 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: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [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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24
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Kim SJ, Kim JM, Yi NJ, Choi GS, Lee KW, Suh KS, Joh JW. Validation for models for tumor recurrence after liver transplantation in hepatectomy patients. Ann Surg Treat Res 2022; 102:131-138. [PMID: 35317356 PMCID: PMC8914523 DOI: 10.4174/astr.2022.102.3.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/13/2022] [Accepted: 01/18/2022] [Indexed: 11/30/2022] Open
Affiliation(s)
- Sung Joon Kim
- Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jong Man Kim
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Nam-Joon Yi
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Gyu-Seong Choi
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Kwang-Woong Lee
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Kyung-Suk Suh
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Jae-Won Joh
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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25
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Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13:2039-2051. [PMID: 35070007 PMCID: PMC8727204 DOI: 10.4254/wjh.v13.i12.2039] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.
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Affiliation(s)
- Joseph C Ahn
- Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55904, United States
| | - Touseef Ahmad Qureshi
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Amit G Singal
- Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Ju-Dong Yang
- Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
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26
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Simsek C, Can Guven D, Koray Sahin T, Emir Tekin I, Sahan O, Yasemin Balaban H, Yalcin S. Artificial intelligence method to predict overall survival of hepatocellular carcinoma. HEPATOLOGY FORUM 2021; 2:64-68. [PMID: 35783900 PMCID: PMC9138921 DOI: 10.14744/hf.2021.2021.0017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/06/2021] [Indexed: 11/20/2022]
Abstract
Background and Aim Hepatocellular carcinoma (HCC) is a complex disease with heterogenous outcomes influenced by disease- and patient-related factors. The prediction of outcomes requires a comprehensive approach, and artificial intelligence could provide a feasible means of estimating HCC outcomes. This study was designed to assess the viability of a machine learning model to predict survival in HCC patients. Materials and Methods HCC patient data with at least 5 years of follow-up were retrospectively reviewed. Patients with accessible data on the primary liver disease, tumor and laboratory values at the time of diagnosis, and length of survival were included. A gradient boosting machine learning algorithm was constructed to predict patient survival at 6 time points. Results A total of 100 HCC patients (80% male) with a median overall survival of 43 months (range: 0.7-256 months) were included. The survival rate for 6, 12, 24, 36, 60, and 120 months was 88%, 81%, 67%, 60%, 40%, and 11%, respectively. The mean area under the curve of the model prediction was 0.92 (0.061) for >6 months, 0.81 (0.107) for >1 year, 0.78 (0.11) for >2 years, 0.81 (0.083) for >3 years, 0.82 (0.079) for >5 years, 0.81 (0.96) for >8 years, and 0.66 (0.14) for >10 years. Conclusion The machine learning model successfully predicted short- and long-term survival of patients with HCC.
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Affiliation(s)
- Cem Simsek
- Department of Gastroenterology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Deniz Can Guven
- Department of Medical Oncology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Taha Koray Sahin
- Department of Internal Medicine, Hacettepe University School of Medicine, Ankara, Turkey
| | - Ibrahim Emir Tekin
- Department of Internal Medicine, Hacettepe University School of Medicine, Ankara, Turkey
| | - Ozlem Sahan
- Department of Internal Medicine, Hacettepe University School of Medicine, Ankara, Turkey
| | | | - Suayib Yalcin
- Department of Medical Oncology, Hacettepe University School of Medicine, Ankara, Turkey
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27
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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: 16] [Impact Index Per Article: 5.3] [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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28
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Moldogazieva NT, Mokhosoev IM, Zavadskiy SP, Terentiev AA. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines 2021; 9:biomedicines9020159. [PMID: 33562077 PMCID: PMC7914649 DOI: 10.3390/biomedicines9020159] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 01/27/2021] [Accepted: 02/02/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.
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Affiliation(s)
- Nurbubu T. Moldogazieva
- Laboratory of Bioinformatics, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
- Correspondence: or
| | - Innokenty M. Mokhosoev
- Department of Biochemistry and Molecular Biology, N.I. Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (I.M.M.); (A.A.T.)
| | - Sergey P. Zavadskiy
- Department of Pharmacology, A.P. Nelyubin Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia;
| | - Alexander A. Terentiev
- Department of Biochemistry and Molecular Biology, N.I. Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (I.M.M.); (A.A.T.)
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