Effective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression

Background The use of statistical methods to analyze data, regardless of their theoretical assumptions, leads to misinterpretation of the results. Objectives Effective attributes in colorectal cancer relapse were investigated through survival analysis in the present study. Comparison between th...

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Autores principales:	Saeedeh Pourahmad, Bahareh Khosravi, Mohammad Mohamadianpanah
Formato:	article
Lenguaje:	EN
Publicado:	Shiraz University of Medical Sciences 2014
Materias:	colorectal cancer artificial neural network method cox proportional hazards model relapse Medicine R
Acceso en línea:	https://doaj.org/article/85bf214c9c08438691f99b0e1d16791c
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spelling	oai:doaj.org-article:85bf214c9c08438691f99b0e1d16791c2021-11-14T07:57:20ZEffective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression2783-243010.17795/acr-22329https://doaj.org/article/85bf214c9c08438691f99b0e1d16791c2014-06-01T00:00:00Zhttps://colorectalresearch.sums.ac.ir/article_45468_af37da396f4342d64e0fff97c2c9e297.pdfhttps://doaj.org/toc/2783-2430Background The use of statistical methods to analyze data, regardless of their theoretical assumptions, leads to misinterpretation of the results. Objectives Effective attributes in colorectal cancer relapse were investigated through survival analysis in the present study. Comparison between the results of artificial neural network (ANN) method and Cox proportional hazards (Cox PH) model was the main purpose of this research. Patients and Methods A total of 184 patients with locoregional colorectal cancer, referred to Shahid Faghihi Hospital (Shiraz, Iran) for surgery, were followed in a five-year period for possible relapse during 2003-2011. Disease-free survival was then modeled based on the patients’ attributes, using Cox PH regression and ANN methods. All the attributes effective on disease relapse were investigated by these two methods. Results A total of 114 (62%) males and 70 (38%) females with a median age of 54 (range: 23-84) years old participated in the study. Among them, there were 95 (51.6%) patients with colon cancer and 89 (48.4%) with rectum cancer. In addition, 53 patients relapsed and 131 patients did not present any relapse or missed the follow up (censored data). The results showed that the accuracy rate in prediction was higher for the ANN method than the Cox PH model (78.2% versus 72.7%). In addition, the area under the receiver operating curve (ROC) was also more for the ANN method (0.86 versus 0.74). Five attributes of the patients, including neoadjuvant treatment, perforation and/or obstruction, perineural invasion, stage, and tumor grade, were significant through the Cox HP model. The first five attributes by the ANN method were surgeon, primary tumor site, perforation and/or obstruction, age, and adjuvant treatments. In this study, the order of attributes determined by the ANN method was rather confirmed by the physicians. Conclusions The results showed superiority of the ANN method over the Cox PH model with respect to the area under the ROC and the accuracy rate in prediction. However, this method requires a large data set to learn the relations and cannot distinguish the confounding attributes.Saeedeh PourahmadBahareh KhosraviMohammad MohamadianpanahShiraz University of Medical Sciencesarticlecolorectal cancerartificial neural network methodcox proportional hazards modelrelapseMedicineRENIranian Journal of Colorectal Research, Vol 2, Iss 2, Pp 0-0 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	colorectal cancer artificial neural network method cox proportional hazards model relapse Medicine R
spellingShingle	colorectal cancer artificial neural network method cox proportional hazards model relapse Medicine R Saeedeh Pourahmad Bahareh Khosravi Mohammad Mohamadianpanah Effective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression
description	Background The use of statistical methods to analyze data, regardless of their theoretical assumptions, leads to misinterpretation of the results. Objectives Effective attributes in colorectal cancer relapse were investigated through survival analysis in the present study. Comparison between the results of artificial neural network (ANN) method and Cox proportional hazards (Cox PH) model was the main purpose of this research. Patients and Methods A total of 184 patients with locoregional colorectal cancer, referred to Shahid Faghihi Hospital (Shiraz, Iran) for surgery, were followed in a five-year period for possible relapse during 2003-2011. Disease-free survival was then modeled based on the patients’ attributes, using Cox PH regression and ANN methods. All the attributes effective on disease relapse were investigated by these two methods. Results A total of 114 (62%) males and 70 (38%) females with a median age of 54 (range: 23-84) years old participated in the study. Among them, there were 95 (51.6%) patients with colon cancer and 89 (48.4%) with rectum cancer. In addition, 53 patients relapsed and 131 patients did not present any relapse or missed the follow up (censored data). The results showed that the accuracy rate in prediction was higher for the ANN method than the Cox PH model (78.2% versus 72.7%). In addition, the area under the receiver operating curve (ROC) was also more for the ANN method (0.86 versus 0.74). Five attributes of the patients, including neoadjuvant treatment, perforation and/or obstruction, perineural invasion, stage, and tumor grade, were significant through the Cox HP model. The first five attributes by the ANN method were surgeon, primary tumor site, perforation and/or obstruction, age, and adjuvant treatments. In this study, the order of attributes determined by the ANN method was rather confirmed by the physicians. Conclusions The results showed superiority of the ANN method over the Cox PH model with respect to the area under the ROC and the accuracy rate in prediction. However, this method requires a large data set to learn the relations and cannot distinguish the confounding attributes.
format	article
author	Saeedeh Pourahmad Bahareh Khosravi Mohammad Mohamadianpanah
author_facet	Saeedeh Pourahmad Bahareh Khosravi Mohammad Mohamadianpanah
author_sort	Saeedeh Pourahmad
title	Effective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression
title_short	Effective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression
title_full	Effective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression
title_fullStr	Effective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression
title_full_unstemmed	Effective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression
title_sort	effective attributes in colorectal cancer relapse using artificial neural network and cox proportional hazards regression
publisher	Shiraz University of Medical Sciences
publishDate	2014
url	https://doaj.org/article/85bf214c9c08438691f99b0e1d16791c
work_keys_str_mv	AT saeedehpourahmad effectiveattributesincolorectalcancerrelapseusingartificialneuralnetworkandcoxproportionalhazardsregression AT baharehkhosravi effectiveattributesincolorectalcancerrelapseusingartificialneuralnetworkandcoxproportionalhazardsregression AT mohammadmohamadianpanah effectiveattributesincolorectalcancerrelapseusingartificialneuralnetworkandcoxproportionalhazardsregression
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Effective Attributes in Colorectal Cancer Relapse Using Artificial Neural Network and Cox Proportional Hazards Regression

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