Literature DB >> 2119877

A model for breast cancer screening.

G J van Oortmarssen1, J D Habbema, P J van der Maas, H J de Koning, H J Collette, A L Verbeek, A T Geerts, K T Lubbe.   

Abstract

A model for breast cancer screening has been developed. When the appropriate screening policy is specified, the model reproduces the detection rates and the incidence of interval cancers as observed in the recent screening projects in Utrecht and Nijmegen, the Netherlands. The model-predicted mortality rate reduction is in accordance with the results of the Kopparberg/Ostergötland randomized trial in Sweden. Key parameters of the model are the duration of the preclinical stages and the sensitivity of mammography. The average duration is approximately 2 years at age 40 and increases to approximately 5 years at age 70. The sensitivity is high (approximately 95%) for tumors larger than 1 cm. The model is used in the prospective evaluation of effects and costs of various screening policies.

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Year:  1990        PMID: 2119877     DOI: 10.1002/1097-0142(19901001)66:7<1601::aid-cncr2820660727>3.0.co;2-o

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  22 in total

Review 1.  Calibration methods used in cancer simulation models and suggested reporting guidelines.

Authors:  Natasha K Stout; Amy B Knudsen; Chung Yin Kong; Pamela M McMahon; G Scott Gazelle
Journal:  Pharmacoeconomics       Date:  2009       Impact factor: 4.981

2.  Lump detection is enhanced in silicone breast models simulating postmenopausal breast tissue.

Authors:  M M McDermott; N C Dolan; J Huang; D Reifler; A W Rademaker
Journal:  J Gen Intern Med       Date:  1996-02       Impact factor: 5.128

3.  Extending the benefits of breast cancer screening. Still hard to know how large the benefits will really be.

Authors:  U Werneke; K McPherson
Journal:  BMJ       Date:  1998-08-08

4.  Quantitative estimates of sensitivity and specificity in mammographic screening.

Authors:  U Werneke
Journal:  J Epidemiol Community Health       Date:  1997-12       Impact factor: 3.710

5.  Beyond black box epidemiology.

Authors:  D L Weed
Journal:  Am J Public Health       Date:  1998-01       Impact factor: 9.308

6.  Assessing the effectiveness of health interventions for cost-effectiveness analysis. Panel on Cost-Effectiveness in Health and Medicine.

Authors:  J S Mandelblatt; D G Fryback; M C Weinstein; L B Russell; M R Gold
Journal:  J Gen Intern Med       Date:  1997-09       Impact factor: 5.128

Review 7.  Dynamic microsimulation models for health outcomes: a review.

Authors:  Carolyn M Rutter; Alan M Zaslavsky; Eric J Feuer
Journal:  Med Decis Making       Date:  2010-05-18       Impact factor: 2.583

8.  Quantitative estimates of the impact of sensitivity and specificity in mammographic screening in Germany.

Authors:  P G Warmerdam; H J de Koning; R Boer; P M Beemsterboer; M L Dierks; E Swart; B P Robra
Journal:  J Epidemiol Community Health       Date:  1997-04       Impact factor: 3.710

9.  Rethinking: Ideal Screening Age for Breast Cancer in Developing Countries.

Authors:  Maha Abdel Hadi; Hefzi Al Ratrout; Hamid Al Wadaani
Journal:  J Breast Health       Date:  2015-07-01

10.  MRI screening for breast cancer in women with familial or genetic predisposition: design of the Dutch National Study (MRISC).

Authors:  M Kriege; C T Brekelmans; C Boetes; E J Rutgers; J C Oosterwijk; R A Tollenaar; R A Manoliu; R Holland; H J de Koning; J G Klijn
Journal:  Fam Cancer       Date:  2001       Impact factor: 2.375

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