Aim: This study aimed to estimate the cure proportion and effects of related factors on colorectal cancer in Iranian patients after surgery.
Background: Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the fourth leading cause of cancer death. The relative survival of CRC varies worldwide given the quality of care, including surgical techniques.
Methods: This retrospective cohort study was conducted on 490 patients, aged 20–94 years, with colorectal cancer. All the colorectal cancer patients undergoing surgery in Faghihi hospital, Shiraz University of Medical Sciences were prospectively followed-up for 8 years from 2008 to March 8, 2016. We used parametric cure model (mixture and non-mixture) to estimate the cure proportion and the adjusted hazard ration (HR) for colorectal cancer mortality after surgery. Data were analyzed by the “flexsurvcure” package in R software (version 3.4.2).
Results: The median age of patients was 57.5 (interquartile range =18) years. Specifically, 56.33% of the patients were male. The median time of follow-up in patients was 618 days. The cumulative survival proportion varied from 0.90 to 0.49 which indicated a reduction followed by a flat line in the probability of survival by sex. The flexible survival for adjusted cure proportion (%) was 68.3. Only obesity was associated with a decreased risk of mortality (HR=0.34; 95% CI: 0.12-0.97).
Conclusion: The overall eight-year survival proportion and adjusted cure proportion for CRC were 49% and 68.3%, respectively. Knowing the cure proportion and its related factors in patients with CRC, better services can be provided. Thus, early detection and screening strategies are required to reduce mortality and increase survival of patients.
Keywords: Cure proportion, Related factors, Colorectal cancer, Parametric cure model.
(Please cite as: Izadi N, Koohi F, Safarpour M, Naseri P, Rahimi S, Khodakarim S. Estimating the cure proportion of colorectal cancer and related factors after surgery in patients using parametric cure models. Gastroenterol Hepatol Bed Bench 2020;13(2):125-132).
Douaiher J, Ravipati A, Grams B, Chowdhury S, Alatise O, Are C. Colorectal cancer-global burden, trends, and geographical variations. J Surg Oncol 2017;115:619-30.
Arnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global patterns and trends in colorectal cancer incidence and mortality. Gut 2016;310912.
Ferlay J, Soerjomataram I, Ervik M , Dikshit R, Eser S, Mathers C, et al, Editors. GLOBOCAN 2012: Estimated cancer incidence, mortality and prevalence worldwide in 2012; v1.0. Lyon, France: The International Agency for Research on Cancer (IARC); 2012.
Dolatkhah R, Somi MH, Kermani IA, Ghojazadeh M, Jafarabadi MA, Farassati F, et al. Increased colorectal cancer incidence in Iran: a systematic review and meta-analysis. BMC Public Health 2015;15:997.
Fatemi SR, Pourhoseingholi MA, Asadi F, Vahedi M, Pasha S, Alizadeh L, et al. Recurrence and five-year survival in colorectal cancer patients after surgery. Iran J Cancer Prev 2015;8.
Gauci D, Allemani C, Woods L. Population-level cure of colorectal cancer in Malta: An analysis of patients diagnosed between 1995 and 2004. Cancer Epidemiol 2016;42:32-8.
Shack L, Shah A, Lambert P, Rachet B. Cure by age and stage at diagnosis for colorectal cancer patients in North West England, 1997–2004: a population-based study. Cancer Epidemiol 2012;36:548-53.
Andersson TM, Dickman PW, Eloranta S, Lambert PC. Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models. BMC Med Res Methodol 2011;11:96.
Guyot F, Faivre J, Manfredi S, Meny B, Bonithon-Kopp C, Bouvier A. Time trends in the treatment and survival of recurrences from colorectal cancer. Ann Oncol 2005;16:756-61.
De Angelis R, Capocaccia R, Hakulinen T, Soderman B, Verdecchia A. Mixture models for cancer survival analysis: application to population‐based data with covariates. Stat Med 1999;18:441-54.
Lambert PC, Dickman PW, Österlund P, Andersson T, Sankila R, Glimelius B. Temporal trends in the proportion cured for cancer of the colon and rectum: A population‐based study using data from the Finnish Cancer Registry. Int J Cancer 2007;121:2052-9.
Francisci S, Capocaccia R, Grande E, Santaquilani M, Simonetti A, Allemani C, et al. The cure of cancer: a European perspective. Europ J Cancer 2009;45:1067-79.
Word Health Organization Obesity: preventing and managing the global epidemic. 2000.
Maller RA, Zhou X, Editors. Survival analysis with long-term survivors. New York: Wiley; 1996.
Amdahl J. Flexible parametric cure models, 2017. Available from: https://github.com/jrdnmdhl/flexsurvcure.
Amico M, Keilegom IV. Cure Models in Survival Analysis. Ann Rev Stat App 2018;5:311-42.
Azizmohammad Looha M, Zarean E, Pourhoseingholi MA, Hosseini SV, Azimi T, Khodakarim S. Analyzing the long-term survival of patients with colorectal cancer: a study using parametric non-mixture cure rate models. Int J Cancer Manag 2018;11:e81681.
Rahimzadeh M, Baghestani AR, Gohari MR, Pourhoseingholi MA. Estimation of the cure rate in Iranian breast cancer patients. Asian Pac J Cancer Prev 2014;15:4839-42.
Yu XQ, De Angelis R, Andersson TM, Lambert PC, O'Connell DL, Dickman PW. Estimating the proportion cured of cancer: some practical advice for users. Cancer Epidemiol 2013;37:836-42.
Lambert PC, Thompson JR, Weston CL, Dickman PW. Estimating and modeling the cure fraction in population-based cancer survival analysis. Biostatistics 2006;8:576-94.
De Angelis R, Capocaccia R, Hakulinen T, Soderman B, Verdecchia A. Mixture models for cancer survival analysis: application to populationâ€گbased data with covariates. Stat Med 1999;18:441-54.
Lambert PC, Royston P. Further development of flexible parametric models for survival analysis. Stata J 2009;9:265.
Nomura A, Grove JS, Stemmermann GN, Severson RK. A prospective study of stomach cancer and its relation to diet, cigarettes, and alcohol consumption. Cancer Res 1990;50:627-31.
Cho E, Smith-Warner SA, Ritz J, Van Den Brandt PA, Colditz GA, Folsom AR, et al. Alcohol intake and colorectal cancer: a pooled analysis of 8 cohort studies. Ann Int Med 2004;140:603-13.
Longnecker MP. Alcoholic beverage consumption in relation to risk of breast cancer: meta-analysis and review. Cancer Causes Control 1994;5:73-82.
Wei EK, Colditz GA, Giovannucci EL, Wu K, Glynn RJ, Fuchs CS, et al. A Comprehensive Model of Colorectal Cancer by Risk Factor Status and Subsite Using Data From the Nursesâ€™ Health Study. Am J Epidemiol 185:224-37.