Mortality predicted accuracy for hepatocellular carcinoma patients with hepatic resection using artificial neural network

The Scientific World Journal

Volumn 2013, Issue , 2013, Pages

  Chiu, Herng Chia ^a   Ho, Te Wei ^b   Lee, King Teh ^a,c   Chen, Hong Yaw ^d   Ho, Wen Hsien ^a  

^a KAOHSIUNG MEDICAL UNIVERSITY   (Taiwan)

^b DEPARTMENT OF HEALTH   (Taiwan)

^c KAOHSIUNG MEDICAL UNIVERSITY HOSPITAL   (Taiwan)

^d Yuan's Hospital   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALPHA FETOPROTEIN;

ACCURACY; ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER CLASSIFICATION; CANCER MORTALITY; CANCER PATIENT; CANCER PROGNOSIS; CANCER STAGING; CANCER SURGERY; CANCER SURVIVAL; COMORBIDITY; FEMALE; HUMAN; INTERMETHOD COMPARISON; LIVER CELL CARCINOMA; LIVER CIRRHOSIS; LIVER RESECTION; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; PREDICTIVE VALUE; PREDICTOR VARIABLE; PROPORTIONAL HAZARDS MODEL; SENSITIVITY AND SPECIFICITY; SURVIVAL TIME; THROMBOCYTE; LIVER TUMOR; MIDDLE AGED; MORTALITY; PREVALENCE; PROGNOSIS; REPRODUCIBILITY; RISK ASSESSMENT; STATISTICS; SURVIVAL; SURVIVAL RATE; TAIWAN; TREATMENT OUTCOME; VERY ELDERLY;

AGED; AGED, 80 AND OVER; CARCINOMA, HEPATOCELLULAR; FEMALE; HEPATECTOMY; HUMANS; LIVER NEOPLASMS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PREVALENCE; PROGNOSIS; PROPORTIONAL HAZARDS MODELS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SURVIVAL ANALYSIS; SURVIVAL RATE; TAIWAN; TREATMENT OUTCOME;

EID: 84878340736     PISSN: None     EISSN: 1537744X     Source Type: Journal    
DOI: 10.1155/2013/201976     Document Type: Article

References (40)

1. World Health Organization, Mortality Database, WHO Statistical Information System, December 2008, http://www.who.int/whosis
2. Centers for Disease Control and Prevention, Hepatocellular carcinoma-United States, 2001-2006. Morbidity and Mortality Weekly Report 2010 59 517 520
3. Capocaccia R., Sant M., Berrino F., Hepatocellular carcinoma: trends of incidence and survival in Europe and the United States at the end of the 20th century. The American Journal of Gastroenterology 2007 102 1661 1670 (Pubitemid 47206702)
4. Lin X. D., Lin L. W., Local injection therapy for hepatocellular carcinoma. Hepatobiliary and Pancreatic Diseases International 2006 5 1 16 21 2-s2.0-33644513055
5. Hanazaki K., Kajikawa S., Shimozawa N., Mihara M., Shimada K., Hiraguri M., Koide N., Adachi W., Amano J., Survival and recurrence after hepatic resection of 386 consecutive patients with hepatocellular carcinoma. Journal of the American College of Surgeons 2000 191 4 381 388 2-s2.0-0033809358 10.1016/S1072-7515(00)00700-6
6. Lee K. T., Lu Y. W., Wang S. N., Chen H. Y., Chuang S. C., Chang W. T., Shi H. Y., Ker C. G., Chiu H. C., The effect of preoperative transarterial chemoembolization of resectable hepatocellular carcinoma on clinical and economic outcomes. Journal of Surgical Oncology 2009 99 6 343 350 2-s2.0-65449150982 10.1002/jso.21248
7. Witten I. H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques 2005 Elsevier San Francisco, Calif, USA
8. Tsai J. T., Chou J. H., Liu T. K., Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm. IEEE Transactions on Neural Networks 2006 17 1 69 80 2-s2.0-33144466864 10.1109/TNN.2005.860885 (Pubitemid 43263941)
9. Das A., Ben-Menachem T., Farooq F. T., Cooper G. S., Chak A., Sivak M. V., Wong R. C. K., Artificial neural network as a predictive instrument in patients with acute nonvariceal upper gastrointestinal hemorrhage. Gastroenterology 2008 134 1 65 74 2-s2.0-37349089341 10.1053/j.gastro.2007.10. 037 (Pubitemid 350309326)
10. Ho W. H., Chang C. S., Genetic-algorithm-based artificial neural network modeling for platelet transfusion requirements on acute myeloblastic leukemia patients. Expert Systems with Applications 2011 38 5 6319 6323 2-s2.0-79151483835 10.1016/j.eswa.2010.11.110
11. Nguyen T., Malley R., Inkelis S. H., Kuppermann N., Comparison of prediction models for adverse outcome in pediatric meningococcal disease using artificial neural network and logistic regression analyses. Journal of Clinical Epidemiology 2002 55 7 687 695 2-s2.0-0036319886 10.1016/S0895-4356(02)00394-3 (Pubitemid 34831060)
12. Ho W. H., Chou J. H., Guo C. Y., Parameter identification of chaotic systems using improved differential evolution algorithm. Nonlinear Dynamics 2010 61 1-2 29 41 2-s2.0-77954349450 10.1007/s11071-009-9629-2
13. Ho W. H., Chen J. X., Lee I. N., Su H. C., An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm. Expert Systems with Applications 2011 38 10 13050 13056 2-s2.0-79957987865 10.1016/j.eswa.2011.04.109
14. Tangri N., Ansell D., Naimark D., Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression. Nephrology Dialysis Transplantation 2008 23 9 2972 2981 2-s2.0-58549103311 10.1093/ndt/gfn187
15. Peng S. Y., Peng S. K., Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks. Anaesthesia 2008 63 7 705 713 2-s2.0-44949195717 10.1111/j.1365-2044.2008.05478.x (Pubitemid 351814231)
16. Luk J. M., Lam B. Y., Lee N. P. Y., Ho D. W., Sham P. C., Chen L., Peng J., Leng X., Day P. J., Fan S. T., Artificial neural networks and decision tree model analysis of liver cancer proteomes. Biochemical and Biophysical Research Communications 2007 361 1 68 73 2-s2.0-34548693705 10.1016/j.bbrc.2007.06.172 (Pubitemid 47411503)
17. Hanai T., Yatabe Y., Nakayama Y., Takahashi T., Honda H., Mitsudomi T., Kobayashi T., Prognostic models in patients with non-small-cell lung cancer using artificial neural networks in comparison with logistic regression. Cancer Science 2003 94 5 473 477 2-s2.0-0038081320 (Pubitemid 36790482)
18. Bassi P., Sacco E., De Marco V., Aragona M., Volpe A., Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis. BJU International 2007 99 5 1007 1012 2-s2.0-34247120531 10.1111/j.1464-410X. 2007.06755.x (Pubitemid 46587910)
19. Sargent D. J., Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer 2001 91 8 1636 1642 2-s2.0-0035871395 (Pubitemid 32319240)
20. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I. H., The WEKA data mining software: an update. SIGKDD Explorations 2009 11 10 18
21. Yeh C. N., Chen M. F., Lee W. C., Jeng L. B., Prognostic factors of hepatic resection for hepatocellular carcinoma with cirrhosis: univariate and multivariate analysis. Journal of Surgical Oncology 2002 81 4 195 202 2-s2.0-0036893458 10.1002/jso.10178 (Pubitemid 35397151)
22. Ercolani G., Grazi G. L., Ravaioli M., Del Gaudio M., Gardini A., Cescon M., Varotti G., Cetta F., Cavallari A., Liver resection for hepatocellular carcinoma on cirrhosis: univariate and multivariate analysis of risk factors for intrahepatic recurrence. Annals of Surgery 2003 237 4 536 543 2-s2.0-0038460305 10.1097/00000658-200304000-00016 (Pubitemid 38176435)
23. Shimozawa N., Hanazaki K., Longterm prognosis after hepatic resection for small hepatocellular carcinoma. Journal of the American College of Surgeons 2004 198 3 356 365 2-s2.0-1442286169 10.1016/j.jamcollsurg.2003.10.017 (Pubitemid 38283726)
24. Liau K. H., Ruo L., Shia J., Padela A., Gonen M., Jarnagin W. R., Fong Y., D'Angelica M. I., Blumgart L. H., DeMatteo R. P., Outcome of partial hepatectomy for large (>10 cm) hepatocellular carcinoma. Cancer 2005 104 1948 1955 (Pubitemid 41513179)
25. Sasaki A., Iwashita Y., Shibata K., Ohta M., Kitano S., Mori M., Preoperative transcatheter arterial chemoembolization reduces long-term survival rate after hepatic resection for resectable hepatocellular carcinoma. European Journal of Surgical Oncology 2006 32 7 773 779 2-s2.0-33747763295 10.1016/j.ejso.2006.04.002 (Pubitemid 44276006)
26. Robinson C. J., Swift S., Johnson D. D., Almeida J. S., Prediction of pelvic organ prolapse using an artificial neural network. American Journal of Obstetrics and Gynecology 2008 199 2 193.e1 193.e6 2-s2.0-47949084316 10.1016/j.ajog.2008.04.029
27. Royston P., Altman D. G., Visualizing and assessing discrimination in the logistic regression model. Statistics in Medicine 2010 29 24 2508 2520 2-s2.0-77958482572 10.1002/sim.3994
28. Altman D. G., Vergouwe Y., Royston P., Moons K. G. M., Prognosis and prognostic research: validating a prognostic model. BMJ 2009 338 7708 1432 1435 2-s2.0-67650082402 10.1136/bmj.b605
29. Lo A., Chiu Y. Y., Rødland E. A., Lyu P. C., Sung T. Y., Hsu W. L., Predicting helix-helix interactions from residue contacts in membrane proteins. Bioinformatics 2009 25 8 996 1003 2-s2.0-64549119506 10.1093/bioinformatics/btp114
30. Tilmanne J., Urbain J., Kothare M. V., Wouwer A. V., Kothare S. V., Algorithms for sleep-wake identification using actigraphy: a comparative study and new results. Journal of Sleep Research 2009 18 1 85 98 2-s2.0-61349111571 10.1111/j.1365-2869.2008.00706.x
31. Green M., Björk J., Forberg J., Ekelund U., Edenbrandt L., Ohlsson M., Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room. Artificial Intelligence in Medicine 2006 38 3 305 318 2-s2.0-33750936249 10.1016/j.artmed.2006.07.006 (Pubitemid 44739270)
32. Lin S. P., Lee C. H., Lu Y. S., Hsu L. N., A comparison of MICU survival prediction using the logistic regression model and artificial neural network model. The Journal of Nursing Research 2006 14 4 306 314 2-s2.0-33947517995
33. Sakai S., Kobayashi K., Toyabe S. I., Mandai N., Kanda T., Akazawa K., Comparison of the levels of accuracy of an artificial neural network model and a logistic regression model for the diagnosis of acute appendicitis. Journal of Medical Systems 2007 31 5 357 364 2-s2.0-34548068067 10.1007/s10916-007-9077-9 (Pubitemid 47293323)
34. Wang C. H., Mo L. R., Lin R. C., Kuo J. J., Chang K. K., Wu J. J., 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. Intervirology 2008 51 1 14 20 2-s2.0-41749097613 10.1159/000118791 (Pubitemid 351489173)
35. Hannula M., Huttunen K., Koskelo J., Laitinen T., Leino T., Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator. Computers in Biology and Medicine 2008 38 11-12 1163 1170 2-s2.0-56349087084 10.1016/j.compbiomed.2008. 09.007
36. Alkan A., Koklukaya E., Subasi A., Automatic seizure detection in EEG using logistic regression and artificial neural network. Journal of Neuroscience Methods 2005 148 2 167 176 2-s2.0-26944479675 10.1016/j.jneumeth.2005.04.009 (Pubitemid 41476228)
37. Eftekhar B., Mohammad K., Ardebili H. E., Ghodsi M., Ketabchi E., Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Medical Informatics and Decision Making 2005 5, article 3 2-s2.0-15244347342 10.1186/1472-6947-5-3 (Pubitemid 40390011)
38. Das A., Ben-Menachem T., Farooq F. T., Cooper G. S., Chak A., Sivak M. V., Wong R. C. K., Artificial neural network as a predictive instrument in patients with acute nonvariceal upper gastrointestinal hemorrhage. Gastroenterology 2008 134 1 65 74 2-s2.0-37349089341 10.1053/j.gastro.2007.10. 037 (Pubitemid 350309326)
39. Trtica-Majnaric L., Zekic-Susac M., Sarlija N., Vitale B., Prediction of influenza vaccination outcome by neural networks and logistic regression. Journal of Biomedical Informatics 2010 43 5 774 781 2-s2.0-77956262660 10.1016/j.jbi.2010.04.011
40. Simon D., Boring J. R. III, Clinical Methods: The History, Physical, and Laboratory Examinations 1990 Boston, Mass, USA Butterworth