A Machine Learning Model Based on Health Records for Predicting Recurrence After Microwave Ablation of Hepatocellular Carcinoma

References

• Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70:7–30. doi:10.3322/caac.21590
   PubMed Web of Science ®Google Scholar
• Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet. 2018;391:1301–1314. doi:10.1016/S0140-6736(18)30010-2
   PubMed Web of Science ®Google Scholar
• Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424. doi:10.3322/caac.21492
   PubMed Web of Science ®Google Scholar
• Dimitroulis D, Damaskos C, Valsami S, et al. From diagnosis to treatment of hepatocellular carcinoma: an epidemic problem for both developed and developing world. World J Gastroenterol. 2017;23:5282–5294. doi:10.3748/wjg.v23.i29.5282
   PubMed Web of Science ®Google Scholar
• Vitale A, Trevisani F, Farinati F, Cillo U. Treatment of hepatocellular carcinoma in the Precision Medicine era: from treatment stage migration to therapeutic hierarchy. Hepatology. 2020;72(6):2206–2218. doi:10.1002/hep.31187
   PubMed Web of Science ®Google Scholar
• Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018;68:723–750. doi:10.1002/hep.29913
   PubMed Web of Science ®Google Scholar
• Eilard MS, Naredi P, Helmersson M, et al. Survival and prognostic factors after transplantation, resection and ablation in a national cohort of early hepatocellular carcinoma. HPB. 2021;23:394–403. doi:10.1016/j.hpb.2020.07.010
   PubMed Web of Science ®Google Scholar
• Sangiovanni A, Colombo M. A therapeutic conundrum: delaying ablation of small nonresectable early hepatocellular carcinoma to facilitate liver transplantation. Liver Transpl. 2016;22:161–162. doi:10.1002/lt.24382
   PubMed Web of Science ®Google Scholar
• Liang P, Yu J, Yu XL, et al. Percutaneous cooled-tip microwave ablation under ultrasound guidance for primary liver cancer: a multicentre analysis of 1363 treatment-naive lesions in 1007 patients in China. Gut. 2012;61:1100–1101. doi:10.1136/gutjnl-2011-300975
   PubMed Web of Science ®Google Scholar
• Liu W, Zou R, Wang C, et al. Microwave ablation versus resection for hepatocellular carcinoma within the Milan criteria: a propensity-score analysis. Ther Adv Med Oncol. 2019;11:1758835919874652. doi:10.1177/1758835919874652
   Web of Science ®Google Scholar
• Zaitoun M, Elsayed SB, Zaitoun NA, et al. Combined therapy with conventional trans-arterial chemoembolization (cTACE) and microwave ablation (MWA) for hepatocellular carcinoma >3-<5 cm. Int J Hyperthermia. 2021;38:248–256. doi:10.1080/02656736.2021.1887941
   PubMed Web of Science ®Google Scholar
• Liu X, Lu J, Zhang G, et al. A machine learning approach yields a multiparameter prognostic marker in liver cancer. Cancer Immunol Res. 2021;9:337–347. doi:10.1158/2326-6066.CIR-20-0616
   PubMed Web of Science ®Google Scholar
• 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. doi:10.1186/s13244-021-00977-9
   PubMed Web of Science ®Google Scholar
• European Association for the Study of the Liver. Electronic address: [email protected], European Association for the Study of the Liver. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2018;69:182–236. doi:10.1016/j.jhep.2018.03.019
   PubMed Web of Science ®Google Scholar
• Heimbach JK, Kulik LM, Finn RS, et al. AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology. 2018;67:358–380. doi:10.1002/hep.29086
   PubMed Web of Science ®Google Scholar
• Lu P, Zhuo Z, Zhang W, Tang J, Tang H, Lu J. Accuracy improvement of quantitative LIBS analysis of coal properties using a hybrid model based on a wavelet threshold de-noising and feature selection method. Appl Opt. 2020;59:6443–6451. doi:10.1364/AO.394746
   PubMed Web of Science ®Google Scholar
• Tahmassebi A, Wengert GJ, Helbich TH, et al. Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients. Invest Radiol. 2019;54:110–117. doi:10.1097/RLI.0000000000000518
   PubMed Web of Science ®Google Scholar
• Chang W, Liu Y, Xiao Y, et al. A machine-learning-based prediction method for hypertension outcomes based on medical data. Diagnostics. 2019;9(4):178. doi:10.3390/diagnostics9040178
   Web of Science ®Google Scholar
• Cha Y, Shin J, Go B, et al. An interpretable machine learning method for supporting ecosystem management: application to species distribution models of freshwater macroinvertebrates. J Environ Manage. 2021;291:112719. doi:10.1016/j.jenvman.2021.112719
   PubMed Web of Science ®Google Scholar
• Oh S, Park Y, Cho KJ, Kim SJ. Explainable machine learning model for glaucoma diagnosis and its interpretation. Diagnostics. 2021;12(1):11. doi:10.3390/diagnostics12010011
   Web of Science ®Google Scholar
• Lin MY, Li CC, Lin PH, et al. Explainable machine learning to predict successful weaning among patients requiring prolonged mechanical ventilation: a retrospective cohort study in central Taiwan. Front Med. 2021;8:663739. doi:10.3389/fmed.2021.663739
   Web of Science ®Google Scholar
• Yang Y, Chen Y, Ye F, et al. Late recurrence of hepatocellular carcinoma after radiofrequency ablation: a multicenter study of risk factors, patterns, and survival. Eur Radiol. 2021;31:3053–3064. doi:10.1007/s00330-020-07460-x
   PubMed Web of Science ®Google Scholar
• Kim CG, Lee HW, Choi HJ, et al. Development and validation of a prognostic model for patients with hepatocellular carcinoma undergoing radiofrequency ablation. Cancer Med. 2019;8:5023–5032. doi:10.1002/cam4.2417
   PubMed Web of Science ®Google Scholar
• Kao WY, Su CW, Chiou YY, et al. Hepatocellular carcinoma: nomograms based on the albumin-bilirubin grade to assess the outcomes of radiofrequency ablation. Radiology. 2017;285:670–680. doi:10.1148/radiol.2017162382
   PubMed Web of Science ®Google Scholar
• Lee S, Kang TW, Song KD, et al. Effect of microvascular invasion risk on early recurrence of hepatocellular carcinoma after surgery and radiofrequency ablation. Ann Surg. 2021;273:564–571. doi:10.1097/SLA.0000000000003268
   PubMed Web of Science ®Google Scholar
• Yamashita YI, Imai K, Yusa T, et al. Microvascular invasion of single small hepatocellular carcinoma ≤3 cm: predictors and optimal treatments. Ann Gastroenterol Surg. 2018;2:197–203. doi:10.1002/ags3.12057
   PubMed Web of Science ®Google Scholar
• Yuan C, Wang Z, Gu D, et al. Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram. Cancer Imaging. 2019;19:21. doi:10.1186/s40644-019-0207-7
   PubMed Web of Science ®Google Scholar
• Shen JX, Zhou Q, Chen ZH, et al. Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation. Transl Oncol. 2021;14:100866. doi:10.1016/j.tranon.2020.100866
   PubMed Web of Science ®Google Scholar
• Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine learning-based model for prediction of outcomes in acute stroke. Stroke. 2019;50:1263–1265. doi:10.1161/STROKEAHA.118.024293
   PubMed Web of Science ®Google Scholar
• Wang S, Pathak J, Zhang Y. Using electronic health records and machine learning to predict postpartum depression. Stud Health Technol Inform. 2019;264:888–892. doi:10.3233/SHTI190351
   PubMedGoogle Scholar
• Pan P, Li Y, Xiao Y, et al. Prognostic assessment of COVID-19 in the intensive care unit by machine learning methods: model development and validation. J Med Internet Res. 2020;22:e23128. doi:10.2196/23128
   PubMed Web of Science ®Google Scholar
• Chen T, Li X, Li Y, et al. Prediction and risk stratification of kidney outcomes in IgA nephropathy. Am J Kidney Dis. 2019;74:300–309. doi:10.1053/j.ajkd.2019.02.016
   PubMed Web of Science ®Google Scholar