Literature DB >> 9663589

The cure for colon cancer: results from the EUROCARE study.

A Verdecchia1, R De Angelis, R Capocaccia, M Sant, A Micheli, G Gatta, F Berrino.   

Abstract

The interpretation of time trends and geographical differences of population-based survival rates is generally not easy, due to the difficulty in disentangling the effects of observational biases, diagnostic and therapeutic procedures and their interactions. Whereas descriptive analysis of relative survival is generally based on survival levels estimated at fixed time since diagnosis, interpretation issues can take advantage from the analysis of the shape of the considered relative survival. Parametric survival models allowing the estimation of the fraction of cured patients are applied here to analyze and discuss the differences in colon cancer relative survival between European countries, according to age and period of diagnosis. The survival curves of colon cancer patients are described according to 2 parameters: the proportion of cured patients and the mean survival time of fatal cases. These parameters are estimated by least square nonlinear regression of relative survival values derived from the EUROCARE Project publication. Exponential and Weibull survival functions are used to model the relative survival curve for the fraction of fatal cases. The Weibull model gives generally a better fit with respect to the exponential model, thus indicating that the mortality rate for fatal cases is decreasing with time since diagnosis. For the youngest patients, however, the 2 survival functions give practically overlapping estimates. The overall proportion of colon cancer patients in Europe that are estimated to be cured was 38.6%. This proportion increased from 36% to 40% for patients diagnosed in 1978-1980 and in 1983-1985, respectively. Accordingly, mean survival time of fatal cases increased from 1.18 to 1.52 years. According to age, the proportion of cured patients present a marked decrease from young (48.4% at age 15-44 years) to middle-aged patients (38.6% at age 5564 years) and only a mild decrease from these to the oldest patients (34.4% at age 75 or more). The opposite effect was shown by survival time of fatal cases, i.e., 1.71, 1.75 and 0.77 years for the same age classes, respectively. Proportion of cured cases and mean survival time of fatal cases tended to be positively correlated with each other across countries. Our results are consistent with the hypothesis that a real improvement in colon cancer survival took place in Europe during the years 1978-1985 and also suggest that the well-known decrease of relative survival with age at diagnosis could be mostly due to a decreasing efficacy of early diagnosis for patients under 60 years old and to less effective therapies for older patients.

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Year:  1998        PMID: 9663589     DOI: 10.1002/(sici)1097-0215(19980729)77:3<322::aid-ijc2>3.0.co;2-q

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  14 in total

1.  Quantitative detection of lac-Z-transfected CC531 colon carcinoma cells in an orthotopic rat liver metastasis model.

Authors:  A Wittmer; K Khazaie; M R Berger
Journal:  Clin Exp Metastasis       Date:  1999-07       Impact factor: 5.150

2.  Prognostic significance of AEG-1 expression in colorectal carcinoma.

Authors:  Hongtao Song; Cong Li; Rui Li; Jingshu Geng
Journal:  Int J Colorectal Dis       Date:  2010-07-13       Impact factor: 2.571

3.  Cost effectiveness of imatinib mesylate in the treatment of advanced gastrointestinal stromal tumours.

Authors:  Daniel M Huse; Margaret von Mehren; Gregory Lenhart; Heikki Joensuu; Charles Blanke; Weiwei Feng; Stan Finkelstein; George Demetri
Journal:  Clin Drug Investig       Date:  2007       Impact factor: 2.859

4.  Influence of dietary factors on colorectal cancer survival.

Authors:  X Dray; M-C Boutron-Ruault; S Bertrais; D Sapinho; A-M Benhamiche-Bouvier; J Faivre
Journal:  Gut       Date:  2003-06       Impact factor: 23.059

5.  Survival and cure trends for European children, adolescents and young adults diagnosed with acute lymphoblastic leukemia from 1982 to 2002.

Authors:  Gemma Gatta; Silvia Rossi; Roberto Foschi; Annalisa Trama; Rafael Marcos-Gragera; Guido Pastore; Rafael Peris-Bonet; Charles Stiller; Riccardo Capocaccia
Journal:  Haematologica       Date:  2013-02-12       Impact factor: 9.941

6.  Projections of cancer prevalence by phase of care: a potential tool for planning future health service needs.

Authors:  Xue Qin Yu; Mark Clements; Dianne O'Connell
Journal:  J Cancer Surviv       Date:  2013-08-07       Impact factor: 4.442

7.  Hazard regression model and cure rate model in colon cancer relative survival trends: are they telling the same story?

Authors:  Theodora Bejan-Angoulvant; Anne-Marie Bouvier; Nadine Bossard; Aurelien Belot; Valérie Jooste; Guy Launoy; Laurent Remontet
Journal:  Eur J Epidemiol       Date:  2008-02-09       Impact factor: 8.082

8.  Mixture Cure Models in Oncology: A Tutorial and Practical Guidance.

Authors:  Federico Felizzi; Noman Paracha; Johannes Pöhlmann; Joshua Ray
Journal:  Pharmacoecon Open       Date:  2021-02-26

9.  Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models.

Authors:  Therese M L Andersson; Paul W Dickman; Sandra Eloranta; Paul C Lambert
Journal:  BMC Med Res Methodol       Date:  2011-06-22       Impact factor: 4.615

10.  Childhood leukaemia: long-term excess mortality and the proportion 'cured'.

Authors:  A Shah; C A Stiller; M G Kenward; T Vincent; T O B Eden; M P Coleman
Journal:  Br J Cancer       Date:  2008-07-08       Impact factor: 7.640

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