Prognostic Role of Artificial Intelligence Among Patients with Hepatocellular Cancer: A Systematic Review

Overview

Journal World J Gastroenterol

Publisher Baishideng Publishing Group

Specialty Gastroenterology

Date 2020 Dec 3

PMID 33268955

Citations 29

Authors

Quirino Lai

Gabriele Spoletini

Gianluca Mennini

Zoe Larghi Laureiro

Diamantis I Tsilimigras

Timothy Michael Pawlik

Massimo Rossi

Affiliations

Soon will be listed here.

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 ( = 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.

Citing Articles

Predicting hepatocellular carcinoma survival with artificial intelligence.

Seven I, Bayram D, Arslan H, Kos F, Gumuslu K, Akturk Esen S Sci Rep. 2025; 15(1):6226.

PMID: 39979406 PMC: 11842547. DOI: 10.1038/s41598-025-90884-6.

F-FDG PET/CT-based intratumoral and peritumoral radiomics combining ensemble learning for prognosis prediction in hepatocellular carcinoma: a multi-center study.

Sui C, Chen K, Ding E, Tan R, Li Y, Shen J BMC Cancer. 2025; 25(1):300.

PMID: 39972270 PMC: 11841186. DOI: 10.1186/s12885-025-13649-4.

AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.

Malik S, Das R, Thongtan T, Thompson K, Dbouk N J Clin Med. 2025; 13(24.

PMID: 39768756 PMC: 11678868. DOI: 10.3390/jcm13247833.

F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.

Sui C, Su Q, Chen K, Tan R, Wang Z, Liu Z BMC Cancer. 2024; 24(1):1457.

PMID: 39604895 PMC: 11600565. DOI: 10.1186/s12885-024-13206-5.

Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis.

Huang Y, Qian H J Hepatocell Carcinoma. 2024; 11:2159-2168.

PMID: 39525830 PMC: 11546143. DOI: 10.2147/JHC.S493227.

References

Ho W, Lee K, Chen H, Ho T, Chiu H . Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network. PLoS One. 2012; 7(1):e29179. PMC: 3250424. DOI: 10.1371/journal.pone.0029179. View

Shan Q, Hu H, Feng S, Peng Z, Chen S, Zhou Q . CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging. 2019; 19(1):11. PMC: 6391838. DOI: 10.1186/s40644-019-0197-5. View

Cucchetti A, Piscaglia F, Grigioni A, Ravaioli M, Cescon M, Zanello M . Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study. J Hepatol. 2010; 52(6):880-8. DOI: 10.1016/j.jhep.2009.12.037. View

Senanayake S, White N, Graves N, Healy H, Baboolal K, Kularatna S . Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models. Int J Med Inform. 2019; 130:103957. DOI: 10.1016/j.ijmedinf.2019.103957. View

Rodriguez-Luna H, Vargas H, Byrne T, Rakela J . Artificial neural network and tissue genotyping of hepatocellular carcinoma in liver-transplant recipients: prediction of recurrence. Transplantation. 2005; 79(12):1737-40. DOI: 10.1097/01.tp.0000161794.32007.d1. View

Chen B, Garmire L, Calvisi D, Chua M, Kelley R, Chen X . Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2020; 17(4):238-251. PMC: 7401304. DOI: 10.1038/s41575-019-0240-9. View

Sterne J, Hernan M, Reeves B, Savovic J, Berkman N, Viswanathan M . ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016; 355:i4919. PMC: 5062054. DOI: 10.1136/bmj.i4919. View

Yamashita R, Mittendorf A, Zhu Z, Fowler K, Santillan C, Sirlin C . Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study. Abdom Radiol (NY). 2019; 45(1):24-35. PMC: 6946904. DOI: 10.1007/s00261-019-02306-7. View

Qiao G, Li J, Huang A, Yan Z, Lau W, Shen F . Artificial neural networking model for the prediction of post-hepatectomy survival of patients with early hepatocellular carcinoma. J Gastroenterol Hepatol. 2014; 29(12):2014-20. DOI: 10.1111/jgh.12672. View

10.

Abbod M, Catto J, Linkens D, Hamdy F . Application of artificial intelligence to the management of urological cancer. J Urol. 2007; 178(4 Pt 1):1150-6. DOI: 10.1016/j.juro.2007.05.122. View

11.

Tsilimigras D, Mehta R, Moris D, Sahara K, Bagante F, Paredes A . Utilizing Machine Learning for Pre- and Postoperative Assessment of Patients Undergoing Resection for BCLC-0, A and B Hepatocellular Carcinoma: Implications for Resection Beyond the BCLC Guidelines. Ann Surg Oncol. 2019; 27(3):866-874. DOI: 10.1245/s10434-019-08025-z. View

12.

Hamamoto I, Okada S, Hashimoto T, Wakabayashi H, Maeba T, Maeta H . Prediction of the early prognosis of the hepatectomized patient with hepatocellular carcinoma with a neural network. Comput Biol Med. 1995; 25(1):49-59. DOI: 10.1016/0010-4825(95)98885-h. View

13.

Bartosch-Harlid A, Andersson B, Aho U, Nilsson J, Andersson R . Artificial neural networks in pancreatic disease. Br J Surg. 2008; 95(7):817-26. DOI: 10.1002/bjs.6239. View

14.

Gauglitz G . Artificial vs. human intelligence in analytics : Do computers outperform analytical chemists?. Anal Bioanal Chem. 2019; 411(22):5631-5632. DOI: 10.1007/s00216-019-01972-2. View

15.

R D, P R . An Optimized HCC Recurrence Prediction Using APO Algorithm Multiple Time Series Clinical Liver Cancer Dataset. J Med Syst. 2019; 43(7):193. DOI: 10.1007/s10916-019-1265-x. View

16.

Shi H, Lee K, Wang J, Sun D, Lee H, Chiu C . Artificial neural network model for predicting 5-year mortality after surgery for hepatocellular carcinoma: a nationwide study. J Gastrointest Surg. 2012; 16(11):2126-31. DOI: 10.1007/s11605-012-1986-3. View

17.

Liang J, Ping X, Tseng Y, Huang G, Lai F, Yang P . Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods. Comput Methods Programs Biomed. 2014; 117(3):425-34. DOI: 10.1016/j.cmpb.2014.09.001. View

18.

Mokrane F, Lu L, Vavasseur A, Otal P, Peron J, Luk L . Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol. 2019; 30(1):558-570. DOI: 10.1007/s00330-019-06347-w. View

19.

Cleophas T, Cleophas T . Artificial intelligence for diagnostic purposes: principles, procedures and limitations. Clin Chem Lab Med. 2009; 48(2):159-65. DOI: 10.1515/CCLM.2010.045. View

20.

Choi J, Oh I, Seo S, Ahn J . G2Vec: Distributed gene representations for identification of cancer prognostic genes. Sci Rep. 2018; 8(1):13729. PMC: 6137174. DOI: 10.1038/s41598-018-32180-0. View