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Items: 1 to 20 of 86

1.

The MISCAN-Fadia continuous tumor growth model for breast cancer.

Tan SY, van Oortmarssen GJ, de Koning HJ, Boer R, Habbema JD.

J Natl Cancer Inst Monogr. 2006;(36):56-65.

PMID:
17032895
2.

Modeling the impact of treatment and screening on U.S. breast cancer mortality: a Bayesian approach.

Berry DA, Inoue L, Shen Y, Venier J, Cohen D, Bondy M, Theriault R, Munsell MF.

J Natl Cancer Inst Monogr. 2006;(36):30-6.

PMID:
17032892
3.

A stochastic simulation model of U.S. breast cancer mortality trends from 1975 to 2000.

Plevritis SK, Sigal BM, Salzman P, Rosenberg J, Glynn P.

J Natl Cancer Inst Monogr. 2006;(36):86-95.

PMID:
17032898
4.
5.
6.

The University of Rochester model of breast cancer detection and survival.

Hanin LG, Miller A, Zorin AV, Yakovlev AY.

J Natl Cancer Inst Monogr. 2006;(36):66-78.

PMID:
17032896
7.

Modeling cancer natural history, epidemiology, and control: reflections on the CISNET breast group experience.

Habbema JD, Schechter CB, Cronin KA, Clarke LD, Feuer EJ.

J Natl Cancer Inst Monogr. 2006;(36):122-6. No abstract available.

PMID:
17032902
8.

Impact of mammography on U.S. breast cancer mortality, 1975-2000: are intermediate outcome measures informative?

Habbema JD, Tan SY, Cronin KA.

J Natl Cancer Inst Monogr. 2006;(36):105-11.

PMID:
17032900
9.

Estimation of natural history parameters of breast cancer based on non-randomized organized screening data: subsidiary analysis of effects of inter-screening interval, sensitivity, and attendance rate on reduction of advanced cancer.

Wu JC, Hakama M, Anttila A, Yen AM, Malila N, Sarkeala T, Auvinen A, Chiu SY, Chen HH.

Breast Cancer Res Treat. 2010 Jul;122(2):553-66. doi: 10.1007/s10549-009-0701-x. Epub 2010 Jan 7.

PMID:
20054645
10.

A stochastic model for predicting the mortality of breast cancer.

Lee S, Zelen M.

J Natl Cancer Inst Monogr. 2006;(36):79-86.

PMID:
17032897
11.

A comparative review of CISNET breast models used to analyze U.S. breast cancer incidence and mortality trends.

Clarke LD, Plevritis SK, Boer R, Cronin KA, Feuer EJ.

J Natl Cancer Inst Monogr. 2006;(36):96-105. Review.

PMID:
17032899
12.
13.

Breast cancer incidence and mortality in the Nordic capitals, 1970-1998. Trends related to mammography screening programmes.

Törnberg S, Kemetli L, Lynge E, Helene Olsen A, Hofvind S, Wang H, Anttila A, Hakama M, Nyström L.

Acta Oncol. 2006;45(5):528-35.

PMID:
16864165
14.

Modeling the dissemination of mammography in the United States.

Cronin KA, Yu B, Krapcho M, Miglioretti DL, Fay MP, Izmirlian G, Ballard-Barbash R, Geller BM, Feuer EJ.

Cancer Causes Control. 2005 Aug;16(6):701-12.

PMID:
16049809
15.

Improved survival for women with stage I breast cancer in south-east Sweden: a comparison between two time periods before and after increased use of adjuvant systemic therapy.

Johansson P, Fohlin H, Arnesson LG, Dufmats M, Nordenskjöld K, Nordenskjöld B, Stål O; South-East Sweden Breast Cancer Study Group.

Acta Oncol. 2009;48(4):504-13. doi: 10.1080/02841860902718754.

PMID:
19235568
16.

Use of risk projection models to estimate mortality and incidence from radiation-induced breast cancer in screening programs.

Ramos M, Ferrer S, Villaescusa JI, Verdú G, Salas MD, Cuevas MD.

Phys Med Biol. 2005 Feb 7;50(3):505-20.

PMID:
15773726
18.

The Wisconsin Breast Cancer Epidemiology Simulation Model.

Fryback DG, Stout NK, Rosenberg MA, Trentham-Dietz A, Kuruchittham V, Remington PL.

J Natl Cancer Inst Monogr. 2006;(36):37-47.

PMID:
17032893
19.

Tumor size and breast cancer detection: what might be the effect of a less sensitive screening tool than mammography?

Duffy SW, Tabar L, Vitak B, Warwick J.

Breast J. 2006 Jan-Feb;12 Suppl 1:S91-5.

PMID:
16430402
20.

Breast cancer screening policies in developing countries: a cost-effectiveness analysis for India.

Okonkwo QL, Draisma G, der Kinderen A, Brown ML, de Koning HJ.

J Natl Cancer Inst. 2008 Sep 17;100(18):1290-300. doi: 10.1093/jnci/djn292. Epub 2008 Sep 9.

PMID:
18780864

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