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Wong HPN, Selvakumar SV, Loh PY, Liau JYJ, Liau MYQ, Shelat VG. Ethical frontiers in liver transplantation. World J Transplant 2024; 14:96687. [DOI: 10.5500/wjt.v14.i4.96687] [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: 05/13/2024] [Revised: 08/26/2024] [Accepted: 09/10/2024] [Indexed: 09/20/2024] Open
Abstract
Liver transplantation represents a pivotal intervention in the management of end-stage liver disease, offering a lifeline to countless patients. Despite significant strides in surgical techniques and organ procurement, ethical dilemmas and debates continue to underscore this life-saving procedure. Navigating the ethical terrain surrounding this complex procedure is hence paramount. Dissecting the nuances of ethical principles of justice, autonomy and beneficence that underpin transplant protocols worldwide, we explore the modern challenges that plaques the world of liver transplantation. We investigate the ethical dimensions of organ transplantation, focusing on allocation, emerging technologies, and decision-making processes. PubMed, Scopus, Web of Science, Embase and Central were searched from database inception to February 29, 2024 using the following keywords: “liver transplant”, “transplantation”, “liver donation”, “liver recipient”, “organ donation” and “ethics”. Information from relevant articles surrounding ethical discussions in the realm of liver transplantation, especially with regards to organ recipients and allocation, organ donation, transplant tourism, new age technologies and developments, were extracted. From the definition of death to the long term follow up of organ recipients, liver transplantation has many ethical quandaries. With new transplant techniques, societal acceptance and perceptions also play a pivotal role. Cultural, religious and regional factors including but not limited to beliefs, wealth and accessibility are extremely influential in public attitudes towards donation, xenotransplantation, stem cell research, and adopting artificial intelligence. Understanding and addressing these perspectives whilst upholding bioethical principles is essential to ensure just distribution and fair allocation of resources. Robust regulatory oversight for ethical sourcing of organs, ensuring good patient selection and transplant techniques, and high-quality long-term surveillance to mitigate risks is essential. Efforts to promote equitable access to transplantation as well as prioritizing patients with true needs are essential to address disparities. In conclusion, liver transplantation is often the beacon of hope for individuals suffering from end-stage liver disease and improves quality of life. The ethics related to transplantation are complex and multifaceted, considering not just the donor and the recipient, but also the society as a whole.
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Affiliation(s)
- Hoi Pong Nicholas Wong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Surya Varma Selvakumar
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Pei Yi Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Jovan Yi Jun Liau
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Matthias Yi Quan Liau
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Vishalkumar Girishchandra Shelat
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Surgical Science Training Centre, Tan Tock Seng Hospital, Singapore 308433, Singapore
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Shukla A, Chaudhary R, Nayyar N. Role of artificial intelligence in gastrointestinal surgery. Artif Intell Cancer 2024; 5:97317. [DOI: 10.35713/aic.v5.i2.97317] [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: 05/28/2024] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 09/05/2024] Open
Abstract
Artificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.
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Affiliation(s)
- Ankit Shukla
- Department of Surgery, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
| | - Rajesh Chaudhary
- Department of Renal Transplantation, Dr Rajendra Prasad Government Medical College, Kangra 176001, India
| | - Nishant Nayyar
- Department of Radiology, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
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Amygdalos I, Truhn D, Vondran FWR. Outcome prediction after resection of colorectal cancer liver metastases: out with the old, in with the new? Hepatobiliary Surg Nutr 2024; 13:732-735. [PMID: 39175724 PMCID: PMC11336544 DOI: 10.21037/hbsn-24-187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/19/2024] [Indexed: 08/24/2024]
Affiliation(s)
- Iakovos Amygdalos
- Department of General, Visceral, Pediatric and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Florian W. R. Vondran
- Department of General, Visceral, Pediatric and Transplantation Surgery, University Hospital RWTH Aachen, Aachen, Germany
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Bianchi V, Giambusso M, De Iacob A, Chiarello MM, Brisinda G. Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review. Updates Surg 2024; 76:783-792. [PMID: 38472633 PMCID: PMC11129994 DOI: 10.1007/s13304-024-01801-x] [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: 02/06/2024] [Accepted: 02/24/2024] [Indexed: 03/14/2024]
Abstract
Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.
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Affiliation(s)
- Valentina Bianchi
- Emergency Surgery and Trauma Center, Department of Abdominal and Endocrine Metabolic Medical and Surgical Sciences, IRCCS, Fondazione Policlinico Universitario A Gemelli, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Mauro Giambusso
- General Surgery Operative Unit, Vittorio Emanuele Hospital, 93012, Gela, Italy
| | - Alessandra De Iacob
- Emergency Surgery and Trauma Center, Department of Abdominal and Endocrine Metabolic Medical and Surgical Sciences, IRCCS, Fondazione Policlinico Universitario A Gemelli, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Maria Michela Chiarello
- Department of Surgery, General Surgery Operative Unit, Azienda Sanitaria Provinciale Cosenza, 87100, Cosenza, Italy
| | - Giuseppe Brisinda
- Emergency Surgery and Trauma Center, Department of Abdominal and Endocrine Metabolic Medical and Surgical Sciences, IRCCS, Fondazione Policlinico Universitario A Gemelli, Largo Agostino Gemelli 8, 00168, Rome, Italy.
- Catholic School of Medicine, University Department of Translational Medicine and Surgery, 00168, Rome, Italy.
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Gairola S, Solanki SL, Patkar S, Goel M. Artificial Intelligence in Perioperative Planning and Management of Liver Resection. Indian J Surg Oncol 2024; 15:186-195. [PMID: 38818006 PMCID: PMC11133260 DOI: 10.1007/s13193-024-01883-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/16/2024] [Indexed: 06/01/2024] Open
Abstract
Artificial intelligence (AI) is a speciality within computer science that deals with creating systems that can replicate the intelligence of a human mind and has problem-solving abilities. AI includes a diverse array of techniques and approaches such as machine learning, neural networks, natural language processing, robotics, and expert systems. An electronic literature search was conducted using the databases of "PubMed" and "Google Scholar". The period for the search was from 2000 to June 2023. The search terms included "artificial intelligence", "machine learning", "liver cancers", "liver tumors", "hepatectomy", "perioperative" and their synonyms in various combinations. The search also included all MeSH terms. The extracted articles were further reviewed in a step-wise manner for identification of relevant studies. A total of 148 articles were identified after the initial literature search. Initial review included screening of article titles for relevance and identifying duplicates. Finally, 65 articles were reviewed for this review article. The future of AI in liver cancer planning and management holds immense promise. AI-driven advancements will increasingly enable precise tumour detection, location, and characterisation through enhanced image analysis. ML algorithms will predict patient-specific treatment responses and complications, allowing for tailored therapies. Surgical robots and AI-guided procedures will enhance the precision of liver resections, reducing risks and improving outcomes. AI will also streamline patient monitoring, better hemodynamic management, enabling early detection of recurrence or complications. Moreover, AI will facilitate data-driven research, accelerating the development of novel treatments and therapies. Ultimately, AI's integration will revolutionise liver cancer care, offering personalised, efficient and effective solutions, improving patients' quality of life and survival rates.
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Affiliation(s)
- Shruti Gairola
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Sohan Lal Solanki
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Shraddha Patkar
- Division of Hepatobiliary Surgical Oncology, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Mahesh Goel
- Division of Hepatobiliary Surgical Oncology, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
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Lopez-Lopez V, Morise Z, Albaladejo-González M, Gavara CG, Goh BKP, Koh YX, Paul SJ, Hilal MA, Mishima K, Krürger JAP, Herman P, Cerezuela A, Brusadin R, Kaizu T, Lujan J, Rotellar F, Monden K, Dalmau M, Gotohda N, Kudo M, Kanazawa A, Kato Y, Nitta H, Amano S, Valle RD, Giuffrida M, Ueno M, Otsuka Y, Asano D, Tanabe M, Itano O, Minagawa T, Eshmuminov D, Herrero I, Ramírez P, Ruipérez-Valiente JA, Robles-Campos R, Wakabayashi G. Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study. Surg Endosc 2024; 38:2411-2422. [PMID: 38315197 PMCID: PMC11078826 DOI: 10.1007/s00464-024-10681-6] [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: 08/07/2023] [Accepted: 01/02/2024] [Indexed: 02/07/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. METHODS We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. RESULTS Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time." CONCLUSION We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.
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Affiliation(s)
- Victor Lopez-Lopez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Zeniche Morise
- Department of Surgery, Fujita Health University School of Medicine Okazaki Medical Center, Okazaki, Aichi, Japan
| | | | - Concepción Gomez Gavara
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Brian K P Goh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Ye Xin Koh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Sijberden Jasper Paul
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Mohammed Abu Hilal
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Department of Surgery, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Kohei Mishima
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
| | - Jaime Arthur Pirola Krürger
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Paulo Herman
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Alvaro Cerezuela
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Roberto Brusadin
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Takashi Kaizu
- Department of General, Pediatric and Hepatobiliary-Pancreatic Surgery, Kitasato University School of Medicine, Sagamihara, Japan
| | - Juan Lujan
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Fernando Rotellar
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Kazuteru Monden
- Department of Surgery, Fukuyama City Hospital, Hiroshima, Japan
| | - Mar Dalmau
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Naoto Gotohda
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Masashi Kudo
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Akishige Kanazawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka City General Hospital, Osaka, Japan
| | - Yutaro Kato
- Department of Surgery, Fujita Health University, Toyoake, Japan
| | - Hiroyuki Nitta
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | - Satoshi Amano
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | | | - Mario Giuffrida
- General Surgery Unit, Parma University Hospital, Parma, Italy
| | - Masaki Ueno
- Second Department of Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama City, Wakayama, Japan
| | | | - Daisuke Asano
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Minoru Tanabe
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Osamu Itano
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Takuya Minagawa
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Dilmurodjon Eshmuminov
- Department of Surgery and Transplantation, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Irene Herrero
- Department of Surgery, Getafe University Hospital, Madrid, Spain
| | - Pablo Ramírez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | | | - Ricardo Robles-Campos
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Go Wakabayashi
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
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Koch DT, Horné F, Fabritius MP, Werner J, Ilmer M. Hepatocellular Carcinoma: The Role of Surgery in Liver Cirrhosis. Visc Med 2024; 40:20-29. [PMID: 38312365 PMCID: PMC10836947 DOI: 10.1159/000535782] [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: 08/19/2023] [Accepted: 12/11/2023] [Indexed: 02/06/2024] Open
Abstract
Background Liver surgery is an essential component of hepatocellular carcinoma (HCC) treatment. Advances in surgical techniques and perioperative care have improved outcomes and have helped to expand surgical indications. However, liver fibrosis and cirrhosis still remain major problems for liver surgery due to the relevant impact on liver regeneration of the future liver remnant (FLR) after surgery. Especially in patients with clinically significant portal hypertension due to liver cirrhosis, surgery is limited. Despite recent efforts in developing predictive models, estimating the postoperative hepatic function remains difficult. Summary In this review, we focus on the role of surgery in the treatment of HCC in structurally altered livers. The importance of assessing FLR with techniques such as contrast-enhanced CT, e.g., with the help of artificial intelligence is highlighted. Moreover, strategies for increasing the FLR with approaches like portal vein embolization and liver vein deprivation prior to surgery are discussed. Patient selection, minimally invasive liver surgery including robotic techniques, and perioperative concepts like the Enhanced Recovery After Surgery (ERAS) guidelines are identified as crucial parts of avoiding posthepatectomy liver failure. Key Message The need for ongoing research to optimize patient selection criteria and perioperative care and to develop innovative biomarkers for outcome prediction is emphasized.
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Affiliation(s)
- Dominik T. Koch
- Department of General, Visceral and Transplantation Surgery, LMU University Hospital, LMU, Munich, Germany
| | - Fabian Horné
- Department of General, Visceral and Transplantation Surgery, LMU University Hospital, LMU, Munich, Germany
| | | | - Jens Werner
- Department of General, Visceral and Transplantation Surgery, LMU University Hospital, LMU, Munich, Germany
| | - Matthias Ilmer
- Department of General, Visceral and Transplantation Surgery, LMU University Hospital, LMU, Munich, Germany
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Kang CM, Ku HJ, Moon HH, Kim SE, Jo JH, Choi YI, Shin DH. Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence. J Clin Med 2024; 13:381. [PMID: 38256518 PMCID: PMC10816299 DOI: 10.3390/jcm13020381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/28/2023] [Accepted: 01/03/2024] [Indexed: 01/24/2024] Open
Abstract
(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient's liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.
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Affiliation(s)
- Chol Min Kang
- Department of Applied Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21287, USA;
| | - Hyung June Ku
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
| | - Hyung Hwan Moon
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
- Division of Hepatobiliary-Pancreas and Transplantation, Department of Surgery, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
| | - Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea;
| | - Ji Hoon Jo
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
- Division of Hepatobiliary-Pancreas and Transplantation, Department of Surgery, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
| | - Young Il Choi
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
- Division of Hepatobiliary-Pancreas and Transplantation, Department of Surgery, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
| | - Dong Hoon Shin
- Chang Kee-Ryo Memorial Liver Institute, Kosin University College of Medicine, Busan 49267, Republic of Korea; (H.J.K.); (J.H.J.); (Y.I.C.); (D.H.S.)
- Division of Hepatobiliary-Pancreas and Transplantation, Department of Surgery, Kosin University Gospel Hospital, Busan 49267, Republic of Korea
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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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10
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Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching? MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121743. [PMID: 36556945 PMCID: PMC9783019 DOI: 10.3390/medicina58121743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/16/2022] [Accepted: 11/25/2022] [Indexed: 11/30/2022]
Abstract
Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered "unbalanced." In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D-R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.
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Wang J, Zheng T, Liao Y, Geng S, Li J, Zhang Z, Shang D, Liu C, Yu P, Huang Y, Liu C, Liu Y, Liu S, Wang M, Liu D, Miao H, Li S, Zhang B, Huang A, Zhang Y, Qi X, Chen S. Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study. Front Oncol 2022; 12:986867. [PMID: 36408144 PMCID: PMC9667038 DOI: 10.3389/fonc.2022.986867] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 10/14/2022] [Indexed: 09/16/2023] Open
Abstract
Introduction Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF. Methods A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models. Results The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models. Conclusion A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.
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Affiliation(s)
- Jitao Wang
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Tianlei Zheng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China
| | - Yong Liao
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
| | - Shi Geng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Jinlong Li
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
| | - Zhanguo Zhang
- Department of Hepatobiliary Surgery, Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Dong Shang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Chengyu Liu
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
| | - Peng Yu
- Department of Hepatobiliary Surgery, Fifth Medical Center of People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Yifei Huang
- Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China
| | - Chuan Liu
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Yanna Liu
- Department of Microbiology and Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Shanghao Liu
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Mingguang Wang
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Dengxiang Liu
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Hongrui Miao
- Department of Hepatobiliary Surgery, Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shuang Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Biao Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Anliang Huang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yewei Zhang
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, Jiangsu, China
| | - Shubo Chen
- Xingtai Key Laboratory of Precision Medicine for Liver Cirrhosis and Portal Hypertension, Xingtai People’s Hospital, Xingtai, Hebei, China
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Khalid MU, Laplante S, Madani A. Machines with vision for intraoperative guidance during gastrointestinal cancer surgery. Front Med (Lausanne) 2022; 9:1025382. [PMID: 36250078 PMCID: PMC9561352 DOI: 10.3389/fmed.2022.1025382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 09/15/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - Simon Laplante
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada
| | - Amin Madani
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Surgical Artificial Intelligence Research Academy, University Health Network, Toronto, ON, Canada
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Taha A, Ochs V, Kayhan LN, Enodien B, Frey DM, Krähenbühl L, Taha-Mehlitz S. Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58040459. [PMID: 35454298 PMCID: PMC9029673 DOI: 10.3390/medicina58040459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/14/2022] [Accepted: 03/18/2022] [Indexed: 02/06/2023]
Abstract
Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.
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Affiliation(s)
- Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, 4123 Allschwil, Switzerland
- Correspondence:
| | - Vincent Ochs
- Roche Innovation Center Basel, Department of Pharma Research & Early Development, 4070 Basel, Switzerland;
| | - Leos N. Kayhan
- Department of Surgery, Canntonal Hospital Luzern, 6004 Luzern, Switzerland;
| | - Bassey Enodien
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | - Daniel M. Frey
- Department of Surgery, Wetzikon Hospital, 8620 Wetzikon, Switzerland; (B.E.); (D.M.F.)
| | | | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital Basel, 4002 Basel, Switzerland;
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