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ORIGINAL RESEARCH

Utility of Machine Learning in the Prediction of Post-Hepatectomy Liver Failure in Liver Cancer

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Pages 1323-1330 | Received 21 Nov 2023, Accepted 11 Jun 2024, Published online: 04 Jul 2024

References

  • Belghiti J, Hiramatsu K, Benoist S, Massault P, Sauvanet A, Farges O. Seven hundred forty-seven hepatectomies in the 1990s: an update to evaluate the actual risk of liver resection. J Am Coll Surg. 2000;191(1):38–46. doi:10.1016/S1072-7515(00)00261-1
  • Rahbari N, Garden J, Padbury R, et al. Posthepatectomy liver failure: a definition and grading by the International Study Group of Liver Surgery (ISGLS). Surgery. 2011;149(5):713–724. doi:10.1016/j.surg.2010.10.001
  • Yoshino K, Yoh T, Taura K, Seo S, Ciria R, Briceno-Delgado J. A systematic review of prediction models for post-hepatectomy liver failure in patients undergoing liver surgery. HPB. 2021;23(9):1311–1320. doi:10.1016/j.hpb.2021.05.002
  • Pugh RN, Murray-Lyon IM, Dawson JL, Pietroni MC, Williams R. Transection of the oesophagus for bleeding oesophageal varices. Br J Surg. 1973;60(8):646–649. doi:10.1002/bjs.1800600817
  • Johnson PJ, Berhane S, Kagebayashi C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. J Clin Oncol. 2015;33(6):550–558. doi:10.1200/JCO.2014.57.9151
  • Sterling RK, Lissen E, Clumeck N, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317–1325. doi:10.1002/hep.21178
  • Makuuchi M, Kosuge T, Takayama T, et al. Surgery for small liver cancers. Semin Surg Oncol. 1993;9(4):298–304. doi:10.1002/ssu.2980090404
  • Imamura H, Seyama Y, Kokudo N, et al. One thousand fifty-six hepatectomies without mortality in 8 years. Arch Surg. 2003;138(11):1198–1206. doi:10.1001/archsurg.138.11.1198
  • Olthof PB, Arntz P, Truant S, et al. Hepatobiliary scintigraphy to predict postoperative liver failure after major liver resection; a multicenter cohort study in 547 patients. HPB. 2023;25(4):417–424. doi:10.1016/j.hpb.2022.12.005
  • Li C, Wang Q, Zou M, et al. A radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma. Front Oncol. 2023;13:1164739. doi:10.3389/fonc.2023.1164739
  • Famularo S, Donadon M, Cipriani F, et al. Machine learning predictive model to guide treatment allocation for recurrent hepatocellular carcinoma after surgery. JAMA Surgery. 2023;158(2):192–202. doi:10.1001/jamasurg.2022.6697
  • Ruzzenente A, Bagante F, Poletto E, et al. A machine learning analysis of difficulty scoring systems for laparoscopic liver surgery. Surg Endo. 2022;36(12):8869–8880. doi:10.1007/s00464-022-09322-7
  • Theysohn J, Demircioglu A, Kleditzsch M, et al. Prediction of left lobe hypertrophy after right lobe radioembolization of the liver using a clinical data model with external validation. Sci Rep. 2022;12(1):20718. doi:10.1038/s41598-022-25077-6
  • Amygdalos I, Muller-Franzes G, Bednarsch J, et al. Novel machine learning algorithm can identify patients at risk of poor overall survival following curative resection for colorectal liver metastases. J Hepatobiliary Pancreat Sci. 2023;30(5):602–614. doi:10.1002/jhbp.1249
  • Mai RY, Bai T, et al. Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma. Surgery. 2020;168(4):643–652. doi:10.1016/j.surg.2020.06.031
  • Wang J, Zheng T, Liao Y, et al. Machine learning prediction model for post-hepatectomy liver failure in hepatocellular carcinoma: a multicenter study. Front Oncol. 2022;12:986867. doi:10.3389/fonc.2022.986867
  • Evrimler S, Gedik MA, Serel TA, et al. Bladder urothelial carcinoma: machine learning-based computed tomography radiomics for prediction of histological variant. Acad Radiol. 2022;29(11):1682–1688. doi:10.1016/j.acra.2022.02.007
  • Yang E, Ding Q, Fan X, et al. Machine learning modeling and prognostic value analysis of invasion-related genes in cutaneous melanoma. Comput Biol Med. 2023;162:107089. doi:10.1016/j.compbiomed.2023.107089
  • Lundervold AJ, Hillestad EMR, Lied GA, et al. Assessment of self-reported executive function in patients with irritable bowel syndrome using a machine-learning framework. J Clin Med 2023;31:3771. doi: 10.3390/jcm12113771
  • Zhang Z, Chen L, Xu P, Hong Y. Predictive analytics with ensemble modeling in laparoscopic surgery: a technical note. Laparosc Endosc Rob Surg. 2022;5(1):25–34. doi:10.1016/j.lers.2021.12.003
  • Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48(3):452–458. doi:10.1038/bmt.2012.244
  • Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugenics. 1936;7(2):179–188. doi:10.1111/j.1469-1809.1936.tb02137.x
  • Chen T, He T, Benesty M, et al. Extreme Gradient Boosting. Package Version-0.4-1.4; 2015. Available from: https://xgboost.ai/. Accessed May, 15, 2023.
  • Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–234. doi:10.1038/nmeth.4642