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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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Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27:6191-6223. [PMID: 34712027 PMCID: PMC8515803 DOI: 10.3748/wjg.v27.i37.6191] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/06/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.
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Affiliation(s)
- Chrysanthos D Christou
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Organ Transplant Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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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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Lai Q, Spoletini G, Mennini G, Laureiro ZL, Tsilimigras DI, Pawlik TM, Rossi M. Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review. World J Gastroenterol 2020; 26:6679-6688. [PMID: 33268955 PMCID: PMC7673961 DOI: 10.3748/wjg.v26.i42.6679] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/14/2020] [Accepted: 10/01/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Prediction of survival after the treatment of hepatocellular carcinoma (HCC) has been widely investigated, yet remains inadequate. The application of artificial intelligence (AI) is emerging as a valid adjunct to traditional statistics due to the ability to process vast amounts of data and find hidden interconnections between variables. AI and deep learning are increasingly employed in several topics of liver cancer research, including diagnosis, pathology, and prognosis.
AIM To assess the role of AI in the prediction of survival following HCC treatment.
METHODS A web-based literature search was performed according to the Preferred Reporting Items for Systemic Reviews and Meta-Analysis guidelines using the keywords “artificial intelligence”, “deep learning” and “hepatocellular carcinoma” (and synonyms). The specific research question was formulated following the patient (patients with HCC), intervention (evaluation of HCC treatment using AI), comparison (evaluation without using AI), and outcome (patient death and/or tumor recurrence) structure. English language articles were retrieved, screened, and reviewed by the authors. The quality of the papers was assessed using the Risk of Bias In Non-randomized Studies of Interventions tool. Data were extracted and collected in a database.
RESULTS Among the 598 articles screened, nine papers met the inclusion criteria, six of which had low-risk rates of bias. Eight articles were published in the last decade; all came from eastern countries. Patient sample size was extremely heterogenous (n = 11-22926). AI methodologies employed included artificial neural networks (ANN) in six studies, as well as support vector machine, artificial plant optimization, and peritumoral radiomics in the remaining three studies. All the studies testing the role of ANN compared the performance of ANN with traditional statistics. Training cohorts were used to train the neural networks that were then applied to validation cohorts. In all cases, the AI models demonstrated superior predictive performance compared with traditional statistics with significantly improved areas under the curve.
CONCLUSION AI applied to survival prediction after HCC treatment provided enhanced accuracy compared with conventional linear systems of analysis. Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.
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Affiliation(s)
- Quirino Lai
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | - Gabriele Spoletini
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario A Gemelli IRCCS, Rome 00100, Italy
| | - Gianluca Mennini
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | - Zoe Larghi Laureiro
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
| | | | | | - Massimo Rossi
- Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
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Iwahashi S, Ghaibeh AA, Shimada M, Morine Y, Imura S, Ikemoto T, Saito Y, Hirose J. Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method. Mol Clin Oncol 2020; 13:46. [PMID: 32874576 DOI: 10.3892/mco.2020.2116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 04/27/2020] [Indexed: 12/18/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a highly lethal tumor and the majority of postoperative patients experience recurrence. In the present study, we focus on the predictability of postoperative recurrence on HCC through the data mining method. In total, 323 patients with HCC who underwent hepatic resection were included in the present study, 156 of whom suffered from cancer recurrence. Clinicopathological data including prognosis were analyzed using the data mining method for the predictability of postoperative recurrence on HCC. The resulting alternating decision tree (ADT) was described using data mining method. This tree was validated using a 10-fold cross validation process. The average and standard deviation of the accuracy, sensitivity, and specificity were 69.0±8.2, 59.7±14.5 and 77.7±10.2%, respectively. The identified postoperative recurrence factors were age, viral hepatitis, stage, GOT and T-cholesterol. Data mining method could identify the factors associated at different levels of significance with postoperative recurrence of HCC. These factors could help to predict the postoperative recurrence of HCC.
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Affiliation(s)
- Shuichi Iwahashi
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - A Ammar Ghaibeh
- Department of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Mitsuo Shimada
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Yuji Morine
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Satoru Imura
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Tetsuya Ikemoto
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Yu Saito
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Jun Hirose
- Department of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
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