Artificial intelligence and its role in guiding liver-directed therapy for hepatocellular carcinoma: Is it ready for prime time?

Abstract

Artificial intelligence (AI) is an innovative discipline in medicine, impacting both hepatology and hepato-pancreato-biliary surgery, ensuring reliable outcomes because of its repeatable and efficient algorithms. A considerable number of studies about the efficiency of AI in the management of hepatocellular carcinoma (HCC) have been published. While its diagnostic role is well recognized, providing large amounts of quantitative radiological HCC features, its use in HCC treatment is still debated. Innovative use of AI may help to select the best approach for each patient as it is able to predict the outcomes after resection and/or other treatments. In this review, we assess the role of AI in selecting the best therapeutic option and predicting long-term risks after surgical or interventional treatments for HCC patients. Further studies are needed to consolidate AI applications.

Keywords

Artificial intelligence / precision medicine / HCC / hepatocellular carcinoma

References

[1] Turing AM.Computing machinery and intelligence.Mind1950;LIX:433-60

[2] Kaul V,Gross SA.History of artificial intelligence in medicine.Gastrointest Endosc2020;92:807-12

[3] Yang YJ.Application of artificial intelligence in gastroenterology.World J Gastroenterol2019;25:1666-83 PMCID:PMC6465941

[4] Nam D,Paradis V,Kather JN.Artificial intelligence in liver diseases: improving diagnostics, prognostics and response prediction.JHEP Rep2022;4:100443 PMCID:PMC8867112

[5] Russell SJ. Artificial intelligence. A modern approach. New Jersey: Pearson Education, Inc.,; 2003.

[6] Calderaro J,Luedde T.Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma.J Hepatol2022;76:1348-61 PMCID:PMC9126418

[7] Locke S,Al-adely S,Wilson A.Natural language processing in medicine: a review.Trends Anaesth Crit Care2021;38:4-9

[8] Feng B,Wang S,Liu XB.Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: current status and future perspectives.World J Gastroenterol2021;27:5341-50 PMCID:PMC8409162

[9] Cucchetti A,Grigioni AD.Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study.J Hepatol2010;52:880-8

[10] Goyal M,Yan S.Artificial intelligence-based image classification methods for diagnosis of skin cancer: challenges and opportunities.Comput Biol Med2020;127:104065 PMCID:PMC8290363

[11] Ting DSW,Lim G.Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA2017;318:2211-23 PMCID:PMC5820739

[12] Cuocolo R,Stanzione A.Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.Eur Radiol2020;30:6877-87

[13] Cho SJ,Baik SH,Choi BS.Brain metastasis detection using machine learning: a systematic review and meta-analysis.Neuro Oncol2021;23:214-25 PMCID:PMC7906058

[14] Barua I,Jodal HC.Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta-analysis.Endoscopy2021;53:277-84

[15] Hardalaç F,Peker O.Fracture detection in wrist X-ray images using deep learning-based object detection models.Sensors2022;22:1285 PMCID:PMC8838335

[16] Sung H,Siegel RL.Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin2021;71:209-49

[17] Association For The Study Of The Liver, European Organisation For Research And Treatment Of Cancer. EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma.J Hepatol2012;56:908-43

[18] Mitchell DG,Sherman M.LI-RADS (Liver Imaging Reporting and Data System): summary, discussion, and consensus of the LI-RADS Management Working Group and future directions.Hepatology2015;61:1056-65

[19] Liang Y,Guo Y.Diagnostic performance of LI-RADS for MRI and CT detection of HCC: a systematic review and diagnostic meta-analysis.Eur J Radiol2021;134:109404

[20] Llovet JM,Bruix J.Prognosis of hepatocellular carcinoma: the BCLC staging classification.Semin Liver Dis1999;19:329-38

[21] El-Serag HB.Epidemiology of hepatocellular carcinoma in the United States: Where are we?.Hepatology2014;60:1767-75 PMCID:PMC4211957

[22] Lewis S,Taouli B.Radiomics of hepatocellular carcinoma.Abdom Radiol2021;46:111-23

[23] Castaldo A,Pontillo G.State of the art in artificial intelligence and radiomics in hepatocellular carcinoma.Diagnostics2021;11:1194 PMCID:PMC8307071

[24] Forner A,Bruix J.Hepatocellular carcinoma.Lancet2018;391:1301-14

[25] Gillies RJ,Hricak H.Radiomics: images are more than pictures, they are data.Radiology2016;278:563-77 PMCID:PMC4734157

[26] Schoenberg MB,Koch D.A novel machine learning algorithm to predict disease free survival after resection of hepatocellular carcinoma.Ann Transl Med2020;8:434 PMCID:PMC7210189

[27] Schoenberg MB,Vater A.Resection or transplant in early hepatocellular carcinoma.Dtsch Arztebl Int2017;114:519-26 PMCID:PMC5624271

[28] Lozanovski VJ,Arefidoust A.Prognostic role of the donor risk index, the eurotransplant donor risk index, and the balance of risk score on graft loss after liver transplantation.Transpl Int2021;34:778-800

[29] Kim WR,Heimbach JK.MELD 3.0: the model for end-stage liver disease updated for the modern era.Gastroenterology2021;161:1887-1895.e4

[30] Rana A,Porubsky M.The survival outcomes following liver transplantation (SOFT) score: validation with contemporaneous data and stratification of high-risk cohorts.Clin Transplant2013;27:627-32

[31] Veerankutty FH,Yadav MK.Artificial Intelligence in hepatology, liver surgery and transplantation: emerging applications and frontiers of research.World J Hepatol2021;13:1977-90 PMCID:PMC8727218

[32] Khorsandi SE,Osborn T.Artificial intelligence in liver transplantation.Transplant Proc2021;53:2939-44

[33] Kazemi A,Sami A.Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques.Exp Clin Transplant2019;17:775-83

[34] Wingfield LR,Thorogood S,Knight S.Using artificial intelligence for predicting survival of individual grafts in liver transplantation: a systematic review.Liver Transpl2020;26:922-34

[35] Briceño J,Prieto M.Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study.J Hepatol2014;61:1020-8

[36] Lau L,Rubinstein B.Machine-learning algorithms predict graft failure after liver transplantation.Transplantation2017;101:e125-32 PMCID:PMC7228574

[37] Liu CL,Lee WC,Lin YC.Predicting short-term survival after liver transplantation using machine learning.Sci Rep2020;10:5654 PMCID:PMC7101323

[38] Nam JY,Bae J.Novel model to predict HCC recurrence after liver transplantation obtained using deep learning: a multicenter study.Cancers2020;12:2791 PMCID:PMC7650768

[39] Ivanics T,Patel MS.The Toronto postliver transplantation hepatocellular carcinoma recurrence calculator: a machine learning approach.Liver Transpl2022;28:593-602

[40] Association for the Study of the Liver. Electronic address: [e-mail], European Association for the Study of the Liver. EASL Clinical Practice Guidelines: management of hepatocellular carcinoma.J Hepatol2018;69:182-236

[41] Balzan S,Farges O.The “50-50 criteria” on postoperative day 5: an accurate predictor of liver failure and death after hepatectomy.Ann Surg2005;242:824-9

[42] Wang K,Retson T.Members of the NASH Clinical Research NetworkAutomated CT and MRI liver segmentation and biometry using a generalized convolutional neural network.Radiol Artif Intell2019;1:180022 PMCID:PMC7314107

[43] Winkel DJ,Breit HC.Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation.Eur J Radiol2020;126:108918

[44] Mise Y,Satou S.How has virtual hepatectomy changed the practice of liver surgery?.Ann Surg2018;268:127-33

[45] Huff TJ,Zuniga JM.The potential for machine learning algorithms to improve and reduce the cost of 3-dimensional printing for surgical planning.Expert Rev Med Devices2018;15:349-56

[46] Durand F.Assessment of the prognosis of cirrhosis: child-pugh versus MELD.J Hepatol2005;42 Suppl:S100-7

[47] Johnson PJ,Kagebayashi C.Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade.J Clin Oncol2015;33:550-8 PMCID:PMC4322258

[48] Lu LH,Mu-Yan C.Platelet-albumin-bilirubin grade: Risk stratification of liver failure, prognosis after resection for hepatocellular carcinoma.Dig Liver Dis2019;51:1430-7

[49] Zhou P,Miao XY.Comparison of FIB-4 index and child-pugh score in predicting the outcome of hepatic resection for hepatocellular carcinoma.J Gastrointest Surg2020;24:823-31

[50] Mai RY,Bai T.Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma.Surgery2020;168:643-52

[51] Kudo M.Systemic therapy for hepatocellular carcinoma: 2017 update.Oncology2017;93 Suppl 1:135-46

[52] Llovet JM,Villanueva A.Randomized trials and endpoints in advanced HCC: role of PFS as a surrogate of survival.J Hepatol2019;70:1262-77

[53] D’Amico F,Vitale A.Predicting recurrence after liver transplantation in patients with hepatocellular carcinoma exceeding the up-to-seven criteria.Liver Transpl2009;15:1278-87

[54] Ling Q,Zhuo J.Development of models to predict early post-transplant recurrence of hepatocellular carcinoma that also integrate the quality and characteristics of the liver graft: a national registry study in China.Surgery2018:155-64

[55] Agopian VG,Zarrinpar A.A novel prognostic nomogram accurately predicts hepatocellular carcinoma recurrence after liver transplantation: analysis of 865 consecutive liver transplant recipients.J Am Coll Surg2015;220:416-27

[56] Mehta N,Harnois DM.Validation of a risk estimation of tumor recurrence after transplant (RETREAT) score for hepatocellular carcinoma recurrence after liver transplant.JAMA Oncol2017;3:493-500 PMCID:PMC5395317

[57] El-Gebaly F,Elkadeem M.Study of serum soluble programmed death ligand 1 as a prognostic factor in hepatocellular carcinoma in egyptian patients.Curr Cancer Drug Targets2019;19:896-905

[58] Wang S,Wang H.A novel multidimensional signature predicts prognosis in hepatocellular carcinoma patients.J Cell Physiol2019;234:11610-9

[59] Erstad DJ.Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma.Ann Surg Oncol2019;26:1474-93

[60] Ma X,Gu D.Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT.Eur Radiol2019;29:3595-605

[61] Xu X,Liu QP.Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma.J Hepatol2019;70:1133-44

[62] Zhang Z,Jiang H.Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection.Ann Transl Med2020;8:870 PMCID:PMC7396783

[63] Dong Y,Xia W.Preoperative prediction of microvascular invasion in hepatocellular carcinoma: initial application of a radiomic algorithm based on grayscale ultrasound images.Front Oncol2020;10:353 PMCID:PMC7096379

[64] Ho WH,Chen HY,Chiu HC.Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network.PLoS One2012;7:e29179 PMCID:PMC3250424

[65] Saillard C,Laifa O.Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides.Hepatology2020;72:2000-13

[66] Ji GW,Xu Q.Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.EBioMedicine2019;50:156-65 PMCID:PMC6923482

[67] Zheng BH,Zhang ZZ.Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients.BMC Cancer2018;18:1148 PMCID:PMC6249916

[68] Shen JX,Chen ZH.Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation.Transl Oncol2021;14:100866

[69] Tiong L.Systematic review and meta-analysis of survival and disease recurrence after radiofrequency ablation for hepatocellular carcinoma.Br J Surg2011;98:1210-24

[70] Hung HH,Hsia CY.Survival rates are comparable after radiofrequency ablation or surgery in patients with small hepatocellular carcinomas.Clin Gastroenterol Hepatol2011;9:79-86

[71] Kao WY,Hung HH.Risk factors for long-term prognosis in hepatocellular carcinoma after radiofrequency ablation therapy: the clinical implication of aspartate aminotransferase-platelet ratio index.Eur J Gastroenterol Hepatol2011;23:528-36

[72] Kao WY,Hung HH.Serum alpha-fetoprotein response can predict prognosis in hepatocellular carcinoma patients undergoing radiofrequency ablation therapy.Clin Radiol2012;67:429-36

[73] Wu CF,Liang PC,Peng SF.Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation.J Formos Med Assoc2017;116:765-73

[74] Liang JD,Tseng YJ,Lai F.Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.Comput Methods Programs Biomed2014;117:425-34

[75] Pesapane F,Patella F.New concepts in embolotherapy of HCC.Med Oncol2017;34:58

[76] Kim BK,Kim KA.Complete response at first chemoembolization is still the most robust predictor for favorable outcome in hepatocellular carcinoma.J Hepatol2015;62:1304-10

[77] Marcacuzco Quinto A,San Román Manso R.Complications of transarterial chemoembolization (TACE) in the treatment of liver tumors.Cir Esp2018;96:560-7

[78] Sieghart W,Pinter M.The ART of decision making: retreatment with transarterial chemoembolization in patients with hepatocellular carcinoma.Hepatology2013;57:2261-73

[79] Adhoute X,Naude S.Retreatment with TACE: the ABCR SCORE, an aid to the decision-making process.J Hepatol2015;62:855-62

[80] Mähringer-Kunz A,Schmidtmann I.Validation of the SNACOR clinical scoring system after transarterial chemoembolisation in patients with hepatocellular carcinoma.BMC Cancer2018;18:489 PMCID:PMC5923193

[81] Mähringer-Kunz A,Hahn F.Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: a pilot study.Liver Int2020;40:694-703

[82] Liu D,Xie X.Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.Eur Radiol2020;30:2365-76

[83] Oezdemir I,Shaw C,Hoyt K.Tumor vascular networks depicted in contrast-enhanced ultrasound images as a predictor for transarterial chemoembolization treatment response.Ultrasound Med Biol2020;46:2276-86 PMCID:PMC7725382

[84] Morshid A,Khalaf AM.A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization.Radiol Artif Intell2019;1:e180021 PMCID:PMC6920060

[85] Peng J,Ning Z.Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging.Eur Radiol2020;30:413-24 PMCID:PMC6890698

[86] Abajian A,Savic LJ.Predicting treatment response to image-guided therapies using machine learning: an example for trans-arterial treatment of hepatocellular carcinoma.J Vis Exp2018; PMCID:PMC6235502

[87] Altman BJ.Metabolic stress in autophagy and cell death pathways.Cold Spring Harb Perspect Biol2012;4:a008763 PMCID:PMC3428762

[88] Ziv E,Boas FE.Gene signature associated with upregulation of the Wnt/β-catenin signaling pathway predicts tumor response to transarterial embolization.J Vasc Interv Radiol2017;28:349-355.e1 PMCID:PMC5325772

[89] Chen B,Calvisi DF,Kelley RK.Harnessing big “omics” data and AI for drug discovery in hepatocellular carcinoma.Nat Rev Gastroenterol Hepatol2020;17:238-51

[90] Wilhelm S,Lynch M.Discovery and development of sorafenib: a multikinase inhibitor for treating cancer.Nat Rev Drug Discov2006;5:835-44

[91] Abou-Alfa GK,Ricci S.Phase II study of sorafenib in patients with advanced hepatocellular carcinoma.J Clin Oncol2006;24:4293-300

[92] Zhu YJ,Wang HY.New knowledge of the mechanisms of sorafenib resistance in liver cancer.Acta Pharmacol Sin2017;38:614-22 PMCID:PMC5457690

[93] Abuhelwa AY,Yuen HY.A clinical scoring tool validated with machine learning for predicting severe hand-foot syndrome from sorafenib in hepatocellular carcinoma.Cancer Chemother Pharmacol2022;89:479-85 PMCID:PMC8956540

[94] Sangro B,Hervás-Stubbs S.Advances in immunotherapy for hepatocellular carcinoma.Nat Rev Gastroenterol Hepatol2021;18:525-43 PMCID:PMC8042636

[95] Chen D,Zang L.Integrated machine learning and bioinformatic analyses constructed a novel stemness-related classifier to predict prognosis and immunotherapy responses for hepatocellular carcinoma patients.Int J Biol Sci2022;18:360-73 PMCID:PMC8692161

[96] Sangro B,Wadhawan S.Association of inflammatory biomarkers with clinical outcomes in nivolumab-treated patients with advanced hepatocellular carcinoma.J Hepatol2020;73:1460-9 PMCID:PMC7751218

[97] Zeng Q,Caruso S.Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology.J Hepatol2022;77:116-27

[98] Lui TKL,Leung WK.Machine learning models in the prediction of 1-year mortality in patients with advanced hepatocellular cancer on immunotherapy: a proof-of-concept study.Hepatol Int2022;16:879-91

[99] Ferrero E,Sanseau P.In silico prediction of novel therapeutic targets using gene-disease association data.J Transl Med2017;15:182 PMCID:PMC5576250

[100] Tong Z,Wang J.Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine.Sci Rep2019;9:10442 PMCID:PMC6639372

[101] Cetin-Atalay R,Nalbat E.Data centric molecular analysis and evaluation of hepatocellular carcinoma therapeutics using machine intelligence-based tools.J Gastrointest Cancer2021;52:1266-76