Hepatocellular Carcinoma Prediction in HCV Patients using Machine Learning and Deep Learning Techniques

Fiza  Saeed; Ashish  Shiwlani; Muhammad  Umar; Zeib Jahangir; Anoosha Tahir; Sheena Shiwlani

doi:10.58602/jics.v3i2.48

Fiza Saeed University of Texas, Arlington, (Texas-USA)
Ashish Shiwlani Illinois Institute of Technology, Chicago, (Illinois-USA)
Muhammad Umar Illinois Institute of Technology, Chicago, (Illinois-USA)
Zeib Jahangir William Jessup University, San Jose, (California-USA)
Anoosha Tahir Buch International Hospital, Multan, (Pakistan)
Sheena Shiwlani Mount Sinai Hospital, NYC, (New York-USA)

DOI: https://doi.org/10.58602/jics.v3i2.48

Keywords: Hepatocellular Carcinoma (HCC), Hepatitis C Virus (HCV), Alkaline Phosphate (ALP), Deep Learning (DL), Sustained Virological Response (SVR)

Abstract

Hepatitis C virus is the root cause of 78% of hepato-cellular carcinoma cases. Hepatocellular carcinoma (HCC) represents one of the primary causes of liver cancer mortality and incidence. Clinical prediction of HCC in patients suffering with hepatitis C virus infection (HCV) is challenging due to the diagnostic gold standard, liver biopsy, which is an invasive technique with several limitations. Artificial intelligence (AI) technology is being used in clinical research at a larger rate in recent years, and the field of HCC diagnosis is no exception. Several advanced and light-weight machine learning algorithms combined with less invasive blood tests have promising diagnostic potential to diagnose HCC from HCV. Deep learning algorithms are regarded as best methods for handling and processing complex, unstructured and raw data from various modalities, ranging from routine clinical variables i.e., from EMRs and laboratories to high-resolution medical images. This paper offers a thorough analysis of the most current research that has used machine learning and deep learning to diagnose, prognosticate, treat, and predict HCC risk in patients suffering with HCV.

References

A. M. Moon, A. G. Singal, and E. B. Tapper, “Contemporary Epidemiology of Chronic Liver Disease and Cirrhosis,” Clinical Gastroenterology and Hepatology, vol. 18, no. 12, pp. 2650–2666, Nov. 2020, doi: 10.1016/J.CGH.2019.07.060.

N. Ali and K. Hossain, “Liver injury in severe COVID-19 infection: current insights and challenges,” Expert Rev Gastroenterol Hepatol, Oct. 2020, doi: 10.1080/17474124.2020.1794812.

A. D. Nardo, M. Schneeweiss-Gleixner, M. Bakail, E. D. Dixon, S. F. Lax, and M. Trauner, “Pathophysiological mechanisms of liver injury in COVID-19,” Liver International, vol. 41, no. 1, pp. 20–32, Jan. 2021, doi: 10.1111/LIV.14730.

A. R. Silva et al., “HBV and HCV serological markers in health professionals and users of the Brazilian Unified Health System network in the city of Resende, Rio de Janeiro, Brazil,” J Bras Patol Med Lab, vol. 53, no. 2, pp. 92–99, Jan. 2017, doi: 10.5935/1676-2444.20170016.

A. Villanueva, V. Hernandez-Gea, and J. M. Llovet, “Medical therapies for hepatocellular carcinoma: a critical view of the evidence,” Nature Reviews Gastroenterology & Hepatology 2012 10:1, vol. 10, no. 1, pp. 34–42, Nov. 2012, doi: 10.1038/nrgastro.2012.199.

S. Mohammadreza Mortazavizadeh, S. Rafatmagham, F. Tabatabaie, R. Hakimizad, S. Mohammad, and A. Hashemipour, “Frequency distribution and ten-year survival rate of patients with different malignant liver lesions in Iran,” 2022, doi: 10.51847/u57GBNwQq3.

E. Vo Quang, Y. Shimakawa, and P. Nahon, “Epidemiological projections of viral-induced hepatocellular carcinoma in the perspective of WHO global hepatitis elimination,” Liver International, vol. 41, no. 5, pp. 915–927, May 2021, doi: 10.1111/LIV.14843.

D. Liu et al., “Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound,” Eur Radiol, vol. 30, no. 4, pp. 2365–2376, Apr. 2020, doi: 10.1007/S00330-019-06553-6/METRICS.

Y. Dong et al., “Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomics features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study,” Clin Hemorheol Microcirc, vol. 81, no. 1, pp. 97–107, Jan. 2022, doi: 10.3233/CH-211363.

S. Hafeez, M. S. Alam, Z. Sajjad, Z. A. Khan, W. Akhter, and F. Mubarak, “Triphasic computed tomography (CT) scan in focal tumoral liver lesions,” J Pak Med Assoc, vol. 61, no. 6, Jun. 2011, Accessed: May 26, 2024. [Online]. Available: https://ecommons.aku.edu/pakistan_fhs_mc_radiol/22

J. Drott, B. Björnsson, P. Sandström, and C. Berterö, “Experiences of Symptoms and Impact on Daily Life and Health in Hepatocellular Carcinoma Patients: A Meta-synthesis of Qualitative Research,” Cancer Nurs, vol. 45, no. 6, pp. 430–437, Nov. 2022, doi: 10.1097/NCC.0000000000001044.

M. Serper et al., “Patient-reported outcomes in HCC: A scoping review by the Practice Metrics Committee of the American Association for the Study of Liver Diseases,” Hepatology, vol. 76, no. 1, pp. 251–274, Jul. 2022, doi: 10.1002/HEP.32313.

S. Hashem et al., “Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients,” Gastroenterol Res Pract, vol. 2016, 2016, doi: 10.1155/2016/2636390.

W. Wang and C. Wei, “Advances in the early diagnosis of hepatocellular carcinoma,” Genes Dis, vol. 7, no. 3, pp. 308–319, Sep. 2020, doi: 10.1016/J.GENDIS.2020.01.014.

K. Cui, Y. Ou, Y. Shen, S. Li, and Z. Sun, “Clinical value of circulating tumor cells for the diagnosis and prognosis of hepatocellular carcinoma (HCC): A systematic review and meta-Analysis,” Medicine (United States), vol. 99, no. 40, p. E22242, Oct. 2020, doi: 10.1097/MD.0000000000022242.

R. Sartoris, J. Gregory, M. Dioguardi Burgio, M. Ronot, and V. Vilgrain, “HCC advances in diagnosis and prognosis: Digital and Imaging,” Liver International, vol. 41, no. S1, pp. 73–77, Jun. 2021, doi: 10.1111/LIV.14865.

D. Q. Huang, H. B. El-Serag, and R. Loomba, “Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention,” Nature Reviews Gastroenterology & Hepatology 2020 18:4, vol. 18, no. 4, pp. 223–238, Dec. 2020, doi: 10.1038/s41575-020-00381-6.

L. Zhang et al., “Prediction of HCC microvascular invasion with gadobenate-enhanced MRI: correlation with pathology,” Eur Radiol, vol. 30, no. 10, pp. 5327–5336, Oct. 2020, doi: 10.1007/S00330-020-06895-6/METRICS.

S. Hashem et al., “Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease,” Comput Methods Programs Biomed, vol. 196, p. 105551, Nov. 2020, doi: 10.1016/J.CMPB.2020.105551.

M. Yumus, M. Apaydin, A. Degirmenci, and O. Karal, “Missing data imputation using machine learning based methods to improve HCC survival prediction,” 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings, Oct. 2020, doi: 10.1109/SIU49456.2020.9302222.

A. Bakrania, N. Joshi, X. Zhao, G. Zheng, and M. Bhat, “Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases,” Pharmacol Res, vol. 189, p. 106706, Mar. 2023, doi: 10.1016/J.PHRS.2023.106706.

Y. Wang, C. Ji, Y. Wang, M. Ji, J. J. Yang, and C. M. Zhou, “Predicting postoperative liver cancer death outcomes with machine learning,” Curr Med Res Opin, vol. 37, no. 4, pp. 629–634, 2021, doi: 10.1080/03007995.2021.1885361.

S. Hashem et al., “A SIMPLE MULTI-LINEAR REGRESSION MODEL FOR PREDICTING FIBROSIS SCORES IN CHRONIC EGYPTIAN HEPATITIS C VIRUS PATIENTS,” International Journal of Bio-Technology and Research (IJBTR) ISSN(P, vol. 4, pp. 37–46, 2014, Accessed: May 26, 2024. [Online]. Available: www.tjprc.org

C. Le Berre et al., “Application of Artificial Intelligence to Gastroenterology and Hepatology,” Gastroenterology, vol. 158, no. 1, pp. 76-94.e2, Jan. 2020, doi: 10.1053/J.GASTRO.2019.08.058.

T. H. Su, C. H. Wu, and J. H. Kao, “Artificial intelligence in precision medicine in hepatology,” J Gastroenterol Hepatol, vol. 36, no. 3, pp. 569–580, Mar. 2021, doi: 10.1111/JGH.15415.

J. Calderaro, T. P. Seraphin, T. Luedde, and T. G. Simon, “Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma,” J Hepatol, vol. 76, no. 6, pp. 1348–1361, Jun. 2022, doi: 10.1016/J.JHEP.2022.01.014.

J. C. Ahn, T. A. Qureshi, D. Li, A. G. Singal, and J. D. Yang, “Deep learning in hepatocellular carcinoma: Current status and future perspectives,” World J Hepatol, vol. 13, no. 12, p. 2039, Dec. 2021, doi: 10.4254/WJH.V13.I12.2039.

R. Sarkis-Onofre, F. Catalá-López, E. Aromataris, and C. Lockwood, “How to properly use the PRISMA Statement,” Syst Rev, vol. 10, no. 1, pp. 1–3, Dec. 2021, doi: 10.1186/S13643-021-01671-Z/METRICS.

M. Norris and C. Oppenheim, “Comparing alternatives to the Web of Science for coverage of the social sciences’ literature,” J Informetr, vol. 1, no. 2, pp. 161–169, Apr. 2007, doi: 10.1016/J.JOI.2006.12.001.

J. Bo, F. Xiang, F. XiaoWei, Z. LianHua, L. ShiChun, and L. YuKun, “A Nomogram Based on Contrast-Enhanced Ultrasound to Predict the Microvascular Invasion in Hepatocellular Carcinoma,” Ultrasound Med Biol, vol. 49, no. 7, pp. 1561–1568, Jul. 2023, doi: 10.1016/J.ULTRASMEDBIO.2023.02.020.

I. Nakamura et al., “Enhanced patterns on intraoperative contrast-enhanced ultrasonography predict outcomes after curative liver resection in patients with hepatocellular carcinoma,” Surg Today, vol. 51, no. 5, pp. 764–776, May 2021, doi: 10.1007/S00595-020-02145-W/METRICS.

N. Nishida and M. Kudo, “Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor,” Front Oncol, vol. 10, p. 594580, Dec. 2020, doi: 10.3389/FONC.2020.594580/BIBTEX.

F. Vernuccio et al., “Benign and malignant mimickers of infiltrative hepatocellular carcinoma: tips and tricks for differential diagnosis on CT and MRI,” Clin Imaging, vol. 70, pp. 33–45, Feb. 2021, doi: 10.1016/J.CLINIMAG.2020.10.011.

Y. Liang et al., “Diagnostic performance of LI-RADS for MRI and CT detection of HCC: A systematic review and diagnostic meta-analysis,” Eur J Radiol, vol. 134, p. 109404, Jan. 2021, doi: 10.1016/J.EJRAD.2020.109404.

S. Lee et al., “Percentages of hepatocellular carcinoma in LI-RADS categories with CT and MRI: A systematic review and meta-Analysis,” Radiology, vol. 307, no. 1, Apr. 2023, doi: 10.1148/RADIOL.220646/ASSET/IMAGES/LARGE/RADIOL.220646.FIG4.JPEG.

F. Özdemir and A. Baskiran, “The Importance of AFP in Liver Transplantation for HCC,” J Gastrointest Cancer, vol. 51, no. 4, pp. 1127–1132, Dec. 2020, doi: 10.1007/S12029-020-00486-W/METRICS.

P. Luo et al., “Current Status and Perspective Biomarkers in AFP Negative HCC: Towards Screening for and Diagnosing Hepatocellular Carcinoma at an Earlier Stage,” Pathology and Oncology Research, vol. 26, no. 2, pp. 599–603, Apr. 2020, doi: 10.1007/S12253-019-00585-5/METRICS.

N. Mehta et al., “AFP-L3 and DCP are superior to AFP in predicting waitlist dropout in HCC patients: Results of a prospective study,” Liver Transplantation, vol. 29, no. 10, pp. 1041–1049, Oct. 2023, doi: 10.1097/LVT.0000000000000149.

J. D. Debes, P. A. Romagnoli, J. Prieto, M. Arrese, A. Z. Mattos, and A. Boonstra, “Serum Biomarkers for the Prediction of Hepatocellular Carcinoma,” Cancers 2021, Vol. 13, Page 1681, vol. 13, no. 7, p. 1681, Apr. 2021, doi: 10.3390/CANCERS13071681.

I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput Sci, vol. 2, no. 3, pp. 1–21, May 2021, doi: 10.1007/S42979-021-00592-X/FIGURES/11.

J. G. Greener, S. M. Kandathil, L. Moffat, and D. T. Jones, “A guide to machine learning for biologists,” Nature Reviews Molecular Cell Biology 2021 23:1, vol. 23, no. 1, pp. 40–55, Sep. 2021, doi: 10.1038/s41580-021-00407-0.

B. Taha Jijo and A. Mohsin Abdulazeez, “Classification Based on Decision Tree Algorithm for Machine Learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20–28, Mar. 2021, doi: 10.38094/jastt20165.

K. K. B and D. S. S, “A Survey on Predicting Advanced Liver Fibrosis Using Different Machine Learning Algorithms,” Int J Sci Res Sci Eng Technol, vol. 7, no. 1, pp. 177–183, 2020, doi: 10.32628/IJSRSET207138.

M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni, “Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 13, pp. 6308–6325, 2020, doi: 10.1109/JSTARS.2020.3026724.

D. M. Abdullah and A. M. Abdulazeez, “Machine Learning Applications based on SVM Classification A Review,” Qubahan Academic Journal, vol. 1, no. 2, pp. 81–90, Apr. 2021, doi: 10.48161/QAJ.V1N2A50.

P. M. Oestmann et al., “Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver,” Eur Radiol, vol. 31, no. 7, pp. 4981–4990, Jul. 2021, doi: 10.1007/S00330-020-07559-1/METRICS.

G. N. Ioannou et al., “Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis,” JAMA Netw Open, vol. 3, no. 9, pp. e2015626–e2015626, Sep. 2020, doi: 10.1001/JAMANETWORKOPEN.2020.15626.

J. C. Ahn, T. A. Qureshi, D. Li, A. G. Singal, and J. D. Yang, “Deep learning in hepatocellular carcinoma: Current status and future perspectives,” World J Hepatol, vol. 13, no. 12, p. 2039, Dec. 2021, doi: 10.4254/WJH.V13.I12.2039.

S. Hashem et al., “Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease,” Comput Methods Programs Biomed, vol. 196, p. 105551, Nov. 2020, doi: 10.1016/J.CMPB.2020.105551.

G. L. H. Wong et al., “Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis,” JHEP Reports, vol. 4, no. 3, p. 100441, Mar. 2022, doi: 10.1016/J.JHEPR.2022.100441.

R. Wei et al., “Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning,” EBioMedicine, vol. 35, pp. 124–132, Sep. 2018, doi: 10.1016/j.ebiom.2018.07.041.

T. Minami et al., “Machine learning for individualized prediction of hepatocellular carcinoma development after the eradication of hepatitis C virus with antivirals,” J Hepatol, vol. 79, no. 4, pp. 1006–1014, Oct. 2023, doi: 10.1016/J.JHEP.2023.05.042.

S. Rajesh, N. A. Choudhury, and S. Moulik, “Hepatocellular Carcinoma (HCC) Liver Cancer prediction using Machine Learning Algorithms,” 2020 IEEE 17th India Council International Conference, INDICON 2020, Dec. 2020, doi: 10.1109/INDICON49873.2020.9342443.

A. G. Singal et al., “Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma,” American Journal of Gastroenterology, vol. 108, no. 11, pp. 1723–1730, Nov. 2013, doi: 10.1038/AJG.2013.332.

M. Sato et al., “Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma,” Scientific Reports 2019 9:1, vol. 9, no. 1, pp. 1–7, May 2019, doi: 10.1038/s41598-019-44022-8.

E. Audureau et al., “Personalized surveillance for hepatocellular carcinoma in cirrhosis – using machine learning adapted to HCV status,” J Hepatol, vol. 73, no. 6, pp. 1434–1445, Dec. 2020, doi: 10.1016/J.JHEP.2020.05.052.

G. N. Ioannou et al., “Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients with Hepatitis C Cirrhosis,” JAMA Netw Open, vol. 3, no. 9, Sep. 2020, doi: 10.1001/jamanetworkopen.2020.15626.

J. Y. Nam, D. H. Sinn, J. Bae, E. S. Jang, J. W. Kim, and S. H. Jeong, “Deep learning model for prediction of hepatocellular carcinoma in patients with HBV-related cirrhosis on antiviral therapy,” JHEP Reports, vol. 2, no. 6, p. 100175, Dec. 2020, doi: 10.1016/J.JHEPR.2020.100175.

D. Van Phan, C. L. Chan, A. H. A. Li, T. Y. Chien, and V. C. Nguyen, “Liver cancer prediction in a viral hepatitis cohort: A deep learning approach,” Int J Cancer, vol. 147, no. 10, pp. 2871–2878, Nov. 2020, doi: 10.1002/IJC.33245.

[60] G. N. Ioannou et al., “498 DEEP LEARNING MODELS ACCURATELY PREDICT DEVELOPMENT OF HCC IN 146,218 PATIENTS WITH CHRONIC HEPATITIS C”.

A. G. Singal et al., “Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma,” American Journal of Gastroenterology, vol. 108, no. 11, pp. 1723–1730, Nov. 2013, doi: 10.1038/AJG.2013.332.