Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.

[1]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[2]  V. L. Clark,et al.  Clinical Methods: The History, Physical, and Laboratory Examinations , 1990 .

[3]  Xiao-dong Lin,et al.  Local injection therapy for hepatocellular carcinoma. , 2006, Hepatobiliary & pancreatic diseases international : HBPD INT.

[4]  Kouhei Akazawa,et al.  Comparison of the Levels of Accuracy of an Artificial Neural Network Model and a Logistic Regression Model for the Diagnosis of Acute Appendicitis , 2007, Journal of Medical Systems.

[5]  Marijana Zekic-Susac,et al.  Prediction of influenza vaccination outcome by neural networks and logistic regression , 2010, J. Biomed. Informatics.

[6]  Patrick Royston,et al.  Visualizing and assessing discrimination in the logistic regression model , 2010, Statistics in medicine.

[7]  Tung-Kuan Liu,et al.  Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm , 2006, IEEE Trans. Neural Networks.

[8]  J. Chou,et al.  Parameter identification of chaotic systems using improved differential evolution algorithm , 2010 .

[9]  D. Sargent,et al.  Comparison of artificial neural networks with other statistical approaches , 2001, Cancer.

[10]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[11]  Taizo Hanai,et al.  Prognostic models in patients with non‐small‐cell lung cancer using artificial neural networks in comparison with logistic regression , 2003, Cancer science.

[12]  Arianna Simonetti,et al.  Hepatocellular Carcinoma: Trends of Incidence and Survival in Europe and the United States at the End of the 20th Century , 2007, The American Journal of Gastroenterology.

[13]  P. Sy,et al.  Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks , 2008 .

[14]  A. Gardini,et al.  Liver Resection for Hepatocellular Carcinoma on Cirrhosis: Univariate and Multivariate Analysis of Risk Factors for Intrahepatic Recurrence , 2003, Annals of surgery.

[15]  S. Peng,et al.  Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks , 2008, Anaesthesia.

[16]  N. Tabibian.  Hepatocellular carcinoma in the United States. , 1988, American family physician.

[17]  C. Ker,et al.  The effect of preoperative transarterial chemoembolization of resectable hepatocellular carcinoma on clinical and economic outcomes , 2009, Journal of surgical oncology.

[18]  Wen-Hsien Ho,et al.  Genetic-algorithm-based artificial neural network modeling for platelet transfusion requirements on acute myeloblastic leukemia patients , 2011, Expert Syst. Appl..

[19]  L. Jeng,et al.  Prognostic factors of hepatic resection for hepatocellular carcinoma with cirrhosis: Univariate and multivariate analysis , 2002, Journal of surgical oncology.

[20]  Nathan Kuppermann,et al.  Comparison of prediction models for adverse outcome in pediatric meningococcal disease using artificial neural network and logistic regression analyses. , 2002, Journal of clinical epidemiology.

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[22]  M. Kothare,et al.  Algorithms for sleep–wake identification using actigraphy: a comparative study and new results , 2009, Journal of sleep research.

[23]  M. Gonen,et al.  Outcome of partial hepatectomy for large (> 10 cm) hepatocellular carcinoma , 2005, Cancer.

[24]  Ahmet Alkan,et al.  Automatic seizure detection in EEG using logistic regression and artificial neural network , 2005, Journal of Neuroscience Methods.

[25]  Michael Green,et al.  Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room , 2006, Artif. Intell. Medicine.

[26]  Jonas S. Almeida,et al.  Prediction of pelvic organ prolapse using an artificial neural network. , 2008, American journal of obstetrics and gynecology.

[27]  M. Mihara,et al.  Survival and recurrence after hepatic resection of 386 consecutive patients with hepatocellular carcinoma. , 2000, Journal of the American College of Surgeons.

[28]  Wen-Hsien Ho,et al.  An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm , 2011, Expert Syst. Appl..

[29]  J. Ward,et al.  Hepatocellular carcinoma - United States, 2001-2006. , 2010, MMWR. Morbidity and mortality weekly report.

[30]  Y. Iwashita,et al.  Preoperative transcatheter arterial chemoembolization reduces long-term survival rate after hepatic resection for resectable hepatocellular carcinoma. , 2006, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[31]  Kazuhiro Hanazaki,et al.  Longterm prognosis after hepatic resection for small hepatocellular carcinoma. , 2004, Journal of the American College of Surgeons.

[32]  N. Tangri,et al.  Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression , 2008, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[33]  Farees T. Farooq,et al.  Artificial neural network as a predictive instrument in patients with acute nonvariceal upper gastrointestinal hemorrhage. , 2008, Gastroenterology.

[34]  Chun‐Hsiang Wang,et al.  Artificial Neural Network Model Is Superior to Logistic Regression Model in Predicting Treatment Outcomes of Interferon-Based Combination Therapy in Patients with Chronic Hepatitis C , 2008, Intervirology.

[35]  Andrea Volpe,et al.  Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis , 2007, BJU international.

[36]  Mohammad Ghodsi,et al.  Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data , 2005, BMC Medical Informatics Decis. Mak..

[37]  Wen-Lian Hsu,et al.  Predicting helix–helix interactions from residue contacts in membrane proteins , 2009, Bioinform..

[38]  Philip J Day,et al.  Artificial neural networks and decision tree model analysis of liver cancer proteomes. , 2007, Biochemical and biophysical research communications.

[39]  Shu-Ping Lin,et al.  A Comparison of MICU Survival Prediction Using the Logistic Regression Model and Artificial Neural Network Model , 2006, The journal of nursing research : JNR.

[40]  Yvonne Vergouwe,et al.  Prognosis and prognostic research: validating a prognostic model , 2009, BMJ : British Medical Journal.

[41]  Manne Hannula,et al.  Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator , 2008, Comput. Biol. Medicine.