Survival Prediction in Patients with Colorectal Cancer Using Artificial Neural Network and Cox Regression

Abstract

Background: Colorectal cancer (CRC) is the third prevalent cancer worldwide, and it includes 10% of all cancer mortality. In Iran, men and women have the third and the fourth incidence rate of CRC, respectively. Survival analysis methods deal with data that measure the time until an event occurs. Artificial neural networks (ANN) and Cox regression are methods for survival analysis. Objectives: The current study was designated to determine related factors to CRC patients' survival using ANN and Cox regression. Methods: In this historical cohort, information of patients who were diagnosed with CRC in Omid Hospital of Mashhad was collected. A total of 157 subjects were investigated from 2006 to 2011 and were followed up until 2016. In ANN, data were divided into two groups of training and testing, and the best neural network architecture was determined based on the area under the ROC curve (AUC). Cox regression model was also fitted and the accuracy of these two models in survival prediction was compared by AUC. Results: The mean and standard deviation of age was 56.4 ± 14.6 years. The three-, five-and seven-year survival rates of patients were 0.67, 0.62, and 0.58, respectively. Using test dataset, the area under curve was estimated 0.759 for the chosen model in ANN and 0.544 for Cox regression model. Conclusions: In this study, ANN is an appropriate approach for predicting CRC patients' survival which was superior to Cox regression. Thus, it is recommended for predicting and also determining the influence of risk factors on patients' survival.

Trends of Colorectal Cancer Epidemiology and Morphology in Tehran Metropolis, Iran from 2006 to 2015

“…This cancer is also the third common cancer in men (16.57) and the second in women (11.86) in Iran. The incidence rate of CRC is higher in central parts of Iran due to urbanization and unhealthy lifestyle (5)(6)(7). CRC is a multifactorial disease with wide global patterns and is strongly linked to human development index level, reflecting the adoption of western lifestyles that accompany economic transition and elevate risk.…”

Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer

“…Colorectal cancer (CRC) is a type of gastrointestinal (GI) malignancy that appears as masses are believed to arise from primarily benign adenomatous polyps; it can invade or extent to other parts of the body (1,2). CRC has become the third commonest malignant tumor worldwide and is also the second leading cause of cancer-related deaths in women and the third for men worldwide (2)(3)(4). Additionally, it ranked the first cause of death in terms of cancer in most developing co untries (5).…”

“…Therefore, initiating measures against CRC inhibition, protection, and treatment have a fundamental value. In this sense, an accurate and regular screening enables earlier detection; and then timely interventions can contribute to enhancing the likelihood of recovery, cure, and lower death ( 2 , 10 ). Due to the complexity of the disease diagnosis, many factors should be considered, which may be complementary, contradictory, and competitive; thus, all health professionals attempt to decrease imprecision in diagnosis by collecting empirical data to manage patients’ problems ( 11 ).…”