Analyzing the Long-Term Survival of Colon and Rectal Cancer Patients Using Non-Mixture Cure Rate Model

Introduction: Colorectal cancer is the most common cause of cancer mortality in Iran. There are differences in the etiology, clinical behavior and pathological features in cancer of the colon versus the rectum. The aim of this study was to evaluate the factors related to survival and cure probability of patients with colon and rectal cancer using a semi-parametric non-mixture cure rate model. Methods: This retrospective cohort study was conducted on 311 patients, with colorectal cancer. Data of all patients with colon and rectum malignances who underwent the first treatment in Omid Hospital, Mashhad, between 2006 and 2011 were gathered through medical records. Patients were followed-up for 9 years until September 2020. Semi-parametric non-mixture cure model was implemented using miCoPTCM package in the R software. Results: The mean survival time was 2973.94 days (95% confidence interval [CI]: (2694.96, 3252.93). The 5-year survival rates for colon and rectal cancer patients were 0.54 (%95 CI:(0.45, 0.61)) and 0.57 (%95 CI:(0.48,0.65)), respectively. The proportion of cured colon cancer patients was 44.0%, while it was 40.0% for the ones with rectal cancer. Age and stage of the disease were determined as the common related factors of survival and cure fraction for both colon and rectal cancers. Ethnicity and type of first treatment were distinguished as factors related to survival and cure fraction of rectal cancer. Whereas the history of drug abuse increased the hazard of death in colon cancer patients; Also, overweight played a protective role in the survival and cure fraction of rectal cancer patients. Conclusion: Because the factors associated with colorectal cancer are not necessarily equal to the risk factors for colon and rectal cancer, it is recommended to obtain more accurate and valid results in the survival analysis of colorectal cancer patients, the colon and rectum should be considered separately. It is also appropriate to use cure rate models when there is a cure fraction in the data.