Application of a non-parametric non-mixture cure rate model for analyzing the survival of patients with colorectal cancer in Iran
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
OBJECTIVES: Colorectal cancer (CRC) patients are considered to have been cured when the mortality rate of individuals with the disease returns to the same level as expected in the general population. This study aimed to assess the impact of various risk factors on the cure fraction of CRC patients using a real dataset of Iranian CRC patients with a non-mixture non-parametric cure model. METHODS: This study was conducted on the medical records of 512 patients who were definitively diagnosed with CRC at Taleghani Hospital, Tehran, Iran from 2001 to 2007. A non-mixture non-parametric cure rate model was applied to the data after using stepwise selection to identify the risk factors of CRC. RESULTS: For non-cured cases, the mean survival time was 1,243.83 days (95% confidence interval [CI], 1,174.65 to 1,313.00) and the median survival time was 1,493.00 days (95% CI, 1,398.67 to 1,587.33). The 1- and 3-year survival rates were 92.9% (95% CI, 91.0 to 95.0) and 73.4% (95% CI, 68.0 to 79.0), respectively. Pathologic stage T1 of the primary tumor (estimate=0.58; p=0.013), a poorly differentiated tumor (estimate=1.17; p<0.001), a body mass index (BMI) between 18.6 and 24.9 kg/m2 (estimate=−0.60; p=0.04), and a BMI between 25.0 and 29.9 kg/m2 (estimate=−1.43; p<0.001) had significant impacts on the cure fraction of CRC in the multivariate analysis. The proportion of cured patients was 64.1% (95% CI, 56.7 to 72.4). CONCLUSIONS: This study found that the pathologic stage of the primary tumor, tumor grade, and BMI were potential risk factors that had an impact on the cure fraction. A non-mixture non-parametric cure rate model provides a flexible framework for accurately determining the impact of risk factors on the long-term survival of patients with CRC.
Abstract
OBJECTIVES: Colorectal cancer (CRC) patients are considered to have been cured when the mortality rate of individuals with the disease returns to the same level as expected in the general population. This study aimed to assess the impact of various risk factors on the cure fraction of CRC patients using a real dataset of Iranian CRC patients with a non-mixture non-parametric cure model. METHODS: This study was conducted on the medical records of 512 patients who were definitively diagnosed with CRC at Taleghani Hospital, Tehran, Iran from 2001 to 2007. A non-mixture non-parametric cure rate model was applied to the data after using stepwise selection to identify the risk factors of CRC. RESULTS: For non-cured cases, the mean survival time was 1,243.83 days (95% confidence interval [CI], 1,174.65 to 1,313.00) and the median survival time was 1,493.00 days (95% CI, 1,398.67 to 1,587.33). The 1- and 3-year survival rates were 92.9% (95% CI, 91.0 to 95.0) and 73.4% (95% CI, 68.0 to 79.0), respectively. Pathologic stage T1 of the primary tumor (estimate=0.58; p=0.013), a poorly differentiated tumor (estimate=1.17; p<0.001), a body mass index (BMI) between 18.6 and 24.9 kg/m2 (estimate=−0.60; p=0.04), and a BMI between 25.0 and 29.9 kg/m2 (estimate=−1.43; p<0.001) had significant impacts on the cure fraction of CRC in the multivariate analysis. The proportion of cured patients was 64.1% (95% CI, 56.7 to 72.4). CONCLUSIONS: This study found that the pathologic stage of the primary tumor, tumor grade, and BMI were potential risk factors that had an impact on the cure fraction. A non-mixture non-parametric cure rate model provides a flexible framework for accurately determining the impact of risk factors on the long-term survival of patients with CRC.
참고문헌 (41)
- GBD 2015 Risk Factors Collaborators, "Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015", Lancet, 388, pp.1659~1724 (2016)
- Arnold M, "Global patterns and trends in colorectal cancer incidence and mortality", Gut, 66, pp.683~691 (2017)
- Ferlay J, "Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012", Int J Cancer, 136, pp.E359~E386 (2015)
- Center MM, "Worldwide variations in colorectal cancer", CA Cancer J Clin, 59, pp.366~378 (2009)
- "What causes colorectal cancer?"[URL]
- Graham S, "Diet in the epidemiology of cancer of the colon and rectum", J Natl Cancer Inst, 61, pp.709~714 (1978)
- Azadeh S, "Colorectal cancer in Iran: an epidemiological study", Asian Pac J Cancer Prev, 9, pp.123~126 (2008)
- Mahdavinia M, "Family history of colorectal cancer in Iran", BMC Cancer, 5, pp.112~ (2005)
- Safaee A, "Four years incidence rate of colorectal cancer in Iran: a survey of national cancer registry data - implications for screening", Asian Pac J Cancer Prev, 13, pp.2695~2698 (2012)
- Pourhoseingholi MA, "Mortality trends of gastrointestinal cancers in Iranian population", Gastroenterol Hepatol Bed Bench, 6(Suppl 1), pp.S52~S57 (2013)
- Almasi Z, "Incidence, mortality, and epidemiological aspects of cancers in Iran; differences with the world data", J BUON, 21, pp.994~1004 (2016)
- Sadjadi A, "Cancer occurrence in Ardabil: results of a population-based cancer registry from Iran", Int J Cancer, 107, pp.113~118 (2003)
- Leslie A, "Management of colorectal cancer", Postgrad Med J, 78, pp.473~478 (2002)
- Renkonen-Sinisalo L, "Surveillance improves survival of colorectal cancer in patients with hereditary nonpolyposis colorectal cancer", Cancer Detect Prev, 24, pp.137~142 (2000)
- Kartsonaki C., "Survival analysis", Diagn Histopathol, 22, pp.263~270 (2016)
- Corbière F, "A penalized likelihood approach for mixture cure models", Stat Med, 28, pp.510~524 (2009)
- Maller R, "Survival analysis with long-term survivors", pp.73~79 (1996), Wiley
- Bertrand A, "Inference in a survival cure model with mismeasured covariates using a simulation-extrapolation approach", Biometrika, 104, pp.31~50 (2017)
- Boag JW, "Maximum likelihood estimates of the proportion of patients cured by cancer therapy", J R Stat Soc Series B Methodol, 11, pp.15~53 (1949)
- Chen MH, "A new Bayesian model for survival data with a surviving fraction", J Am Stat Assoc, 94, pp.909~919 (1999)
- Tsodikov AD, "Estimating cure rates from survival data: an alternative to two-component mixture models", J Am Stat Assoc, 98, pp.1063~1078 (2003)
- Moghimi-Dehkordi B, "An overview of colorectal cancer survival rates and prognosis in Asia", World J Gastrointest Oncol, 4, pp.71~75 (2012)
- Hjartåker A, "International encyclopedia of public health", pp.369~380 (2017), Academic Press
- Majek O, "Sex differences in colorectal cancer survival: population-based analysis of 164,996 colorectal cancer patients in Germany", PLoS One, 8, pp.e68077~ (2013)
- Paulson EC, "Gender influences treatment and survival in colorectal cancer surgery", Dis Colon Rectum, 52, pp.1982~1991 (2009)
- Micheli A, "The advantage of women in cancer survival: an analysis of EUROCARE-4 data", Eur J Cancer, 45, pp.1017~1027 (2009)
- McArdle CS, "Male gender adversely affects survival following surgery for colorectal cancer", Br J Surg, 90, pp.711~715 (2003)
- Gauci D, "Population-level cure of colorectal cancer in Malta: an analysis of patients diagnosed between 1995 and 2004", Cancer Epidemiol, 42, pp.32~38 (2016)
- Koo JH, "Sex differences in epidemiological, clinical and pathological characteristics of colorectal cancer", J Gastroenterol Hepatol, 25, pp.33~42 (2010)
- Edwards BK, "Annual report to the nation on the status of cancer, 1973-1999, featuring implications of age and aging on U.S. cancer burden", Cancer, 94, pp.2766~2792 (2002)
- Yancik R, "Cancer in older persons. Magnitude of the problem--how do we apply what we know?", Cancer, 74, pp.1995~2003 (1994)
- Jiang Z, "Effect of age on survival outcome in operated and non-operated patients with colon cancer: a population-based study", PLoS One, 11, pp.e0147383~ (2016)
- van Eeghen EE, "Impact of age and comorbidity on survival in colorectal cancer", J Gastrointest Oncol, 6, pp.605~612 (2015)
- Hines RB, "Effect of comorbidity and body mass index on colon cancer survival of African American and Caucasian patients with colon cancer", Cancer, 115, pp.5798~5806 (2009)
- Lee J, "Association between body mass index and prognosis of colorectal cancer: a meta-analysis of prospective cohort studies", PLoS One, 10, pp.e0120706~ (2015)
- Murphy TK, "Body mass index and colon cancer mortality in a large prospective study", Am J Epidemiol, 152, pp.847~854 (2000)
- Campbell PT, "Impact of body mass index on survival after colorectal cancer diagnosis: the cancer prevention study-ii nutrition cohort", J Clin Oncol, 30, pp.42~52 (2012)
- Blenkinsopp WK, "Histopathology reporting in large bowel cancer", J Clin Pathol, 34, pp.509~513 (1981)
- Compton CC, "Pathology report in colon cancer: what is prognostically important?", Dig Dis, 17, pp.67~79 (1999)
- Rasouli MA, "Prognostic factors and survival of colorectal cancer in Kurdistan province, Iran: a population-based study (2009-2014)", Medicine (Baltimore), 96, pp.e5941~ (2017)
- Jafari-Koshki T, "Exploring factors related to metastasis free survival in breast cancer patients using Bayesian cure models", Asian Pac J Cancer Prev, 15, pp.9673~9678 (2014)
