Literature DB >> 15167217

Contribution of three components to individual cancer risk predicting breast cancer risk in Italy.

P Boyle1, M Mezzetti, C La Vecchia, S Franceschi, A Decarli, C Robertson.   

Abstract

We used data from a multicentre case-control study conducted in Italy between 1991 and 1994 on over 2500 cases of breast cancer and a comparable number of controls, and estimates of breast cancer incidence in Italy to compute individual breast cancer risk for Italian women. The estimated probabilities between age 50 and 80 ranged from approximately 5% (for a woman with no family history and low modifiable risk profile) to about 30% (for a woman with young family history and high modifiable risk) on the basis of various women's baseline characteristics. Expected numbers of breast cancer cases using the present model were compared with those based on the USA Gail model, and with the observed ones in the comparison group of the Italian Tamoxifen Trial. These show a closer agreement between the observed and the expected total numbers of breast cancers than the USA Gail model. Thus, the Gail model can be improved for use in other populations by using estimates of incidence and risk which are more appropriate to the target population.

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Year:  2004        PMID: 15167217     DOI: 10.1097/01.cej.0000130014.83901.53

Source DB:  PubMed          Journal:  Eur J Cancer Prev        ISSN: 0959-8278            Impact factor:   2.497


  17 in total

1.  External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients.

Authors:  Yvonne Vergouwe; Karel G M Moons; Ewout W Steyerberg
Journal:  Am J Epidemiol       Date:  2010-08-31       Impact factor: 4.897

2.  Gail model utilization in predicting breast cancer risk in Egyptian women: a cross-sectional study.

Authors:  Basem Saleh; Mohamed A Elhawary; Moataz E Mohamed; Islam N Ali; Menna S El Zayat; Hadeer Mohamed
Journal:  Breast Cancer Res Treat       Date:  2021-04-14       Impact factor: 4.872

3.  Risk factor modification and projections of absolute breast cancer risk.

Authors:  Elisabetta Petracci; Adriano Decarli; Catherine Schairer; Ruth M Pfeiffer; David Pee; Giovanna Masala; Domenico Palli; Mitchell H Gail
Journal:  J Natl Cancer Inst       Date:  2011-06-24       Impact factor: 13.506

4.  Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk.

Authors:  Robert J Glynn; Graham A Colditz; Rulla M Tamimi; Wendy Y Chen; Susan E Hankinson; Walter W Willett; Bernard Rosner
Journal:  Breast Cancer Res Treat       Date:  2017-06-06       Impact factor: 4.872

5.  Validation of two US breast cancer risk prediction models in German women.

Authors:  Anika Hüsing; Anne S Quante; Jenny Chang-Claude; Krasimira Aleksandrova; Rudolf Kaaks; Ruth M Pfeiffer
Journal:  Cancer Causes Control       Date:  2020-04-06       Impact factor: 2.506

6.  Development of a Cancer Risk Prediction Tool for Use in the UK Primary Care and Community Settings.

Authors:  Artitaya Lophatananon; Juliet Usher-Smith; Jackie Campbell; Joanne Warcaba; Barbora Silarova; Erika A Waters; Graham A Colditz; Kenneth R Muir
Journal:  Cancer Prev Res (Phila)       Date:  2017-05-30

7.  Validation of the Gail model for predicting individual breast cancer risk in a prospective nationwide study of 28,104 Singapore women.

Authors:  Wen Yee Chay; Whee Sze Ong; Puay Hoon Tan; Nicholas Qi Jie Leo; Gay Hui Ho; Chia Siong Wong; Kee Seng Chia; Khuan Yew Chow; Minhan Tan; Peter Ang
Journal:  Breast Cancer Res       Date:  2012-01-30       Impact factor: 6.466

8.  Assessing risk of breast cancer in an ethnically South-East Asia population (results of a multiple ethnic groups study).

Authors:  Fei Gao; David Machin; Khuan-Yew Chow; Yu-Fan Sim; Stephen W Duffy; David B Matchar; Chien-Hui Goh; Kee-Seng Chia
Journal:  BMC Cancer       Date:  2012-11-19       Impact factor: 4.430

9.  Validation of Rosner-Colditz breast cancer incidence model using an independent data set, the California Teachers Study.

Authors:  B A Rosner; G A Colditz; S E Hankinson; J Sullivan-Halley; J V Lacey; L Bernstein
Journal:  Breast Cancer Res Treat       Date:  2013-10-26       Impact factor: 4.872

10.  Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study.

Authors:  Roberto Pastor-Barriuso; Nieves Ascunce; María Ederra; Nieves Erdozáin; Alberto Murillo; José E Alés-Martínez; Marina Pollán
Journal:  Breast Cancer Res Treat       Date:  2013-02-03       Impact factor: 4.872

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