Literature DB >> 20606088

Evaluation of the Tyrer-Cuzick (International Breast Cancer Intervention Study) model for breast cancer risk prediction in women with atypical hyperplasia.

Judy C Boughey1, Lynn C Hartmann, Stephanie S Anderson, Amy C Degnim, Robert A Vierkant, Carol A Reynolds, Marlene H Frost, V Shane Pankratz.   

Abstract

PURPOSE: Accurate breast cancer risk assessment is vital to personalize screening and risk reduction strategies. Women with atypical hyperplasia have a four-fold higher risk of breast cancer. We evaluated the performance of the Tyrer-Cuzick model, which was designed to predict 10-year risk of breast cancer development, in a well-defined cohort of women with atypia. PATIENTS AND METHODS: The Mayo Benign Breast Disease cohort includes 9,376 women who had a benign breast biopsy between 1967 and 1991. Among those, 331 women with atypia were identified by our study pathologists. Risk factor data for the Tyrer-Cuzick model were collated for each woman and used to predict individual risk of developing invasive breast cancer within 10 years.
RESULTS: Over a median follow-up of 14.6 years, 64 (19%) of the 331 women developed invasive breast cancer. In the first 10 years after biopsy, 31 women developed invasive breast cancer whereas the Tyrer-Cuzick model predicted 58.9. The observed-to-predicted ratio was 0.53 (95% CI, 0.37 to 0.75). The concordance statistic was 0.540, revealing that the Tyrer-Cuzick model did not accurately distinguish, on an individual level, between women who developed invasive breast cancer and those who did not.
CONCLUSION: The Tyrer-Cuzick model significantly overestimated risk of breast cancer for women with atypia, and individual risk estimates showed poor concordance between predicted risk and invasive breast cancer development. Thus, we cannot recommend the use of the Tyrer-Cuzick model to predict 10-year breast cancer risk in women with atypical hyperplasia.

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Year:  2010        PMID: 20606088      PMCID: PMC2917314          DOI: 10.1200/JCO.2010.28.0784

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  27 in total

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3.  Ki67: a time-varying biomarker of risk of breast cancer in atypical hyperplasia.

Authors:  Marta Santisteban; Carol Reynolds; Emily G Barr Fritcher; Marlene H Frost; Robert A Vierkant; Stephanie S Anderson; Amy C Degnim; Daniel W Visscher; V Shane Pankratz; Lynn C Hartmann
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  34 in total

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2.  Extent of atypical hyperplasia stratifies breast cancer risk in 2 independent cohorts of women.

Authors:  Amy C Degnim; William D Dupont; Derek C Radisky; Robert A Vierkant; Ryan D Frank; Marlene H Frost; Stacey J Winham; Melinda E Sanders; Jeffrey R Smith; David L Page; Tanya L Hoskin; Celine M Vachon; Karthik Ghosh; Tina J Hieken; Lori A Denison; Jodi M Carter; Lynn C Hartmann; Daniel W Visscher
Journal:  Cancer       Date:  2016-06-28       Impact factor: 6.860

3.  The Tyrer-Cuzick Model Inaccurately Predicts Invasive Breast Cancer Risk in Women With LCIS.

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4.  Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer.

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Review 5.  Paradigm Shifts in Breast Care Delivery: Impact of Imaging in a Multidisciplinary Environment.

Authors:  Savitri Krishnamurthy; Therese Bevers; Henry M Kuerer; Benjamin Smith; Wei Tse Yang
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6.  Addressing barriers to uptake of breast cancer chemoprevention for patients and providers.

Authors:  Katherine D Crew
Journal:  Am Soc Clin Oncol Educ Book       Date:  2015

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Authors:  Monica Morrow; Stuart J Schnitt; Larry Norton
Journal:  Nat Rev Clin Oncol       Date:  2015-01-27       Impact factor: 66.675

8.  Model for individualized prediction of breast cancer risk after a benign breast biopsy.

Authors:  V Shane Pankratz; Amy C Degnim; Ryan D Frank; Marlene H Frost; Daniel W Visscher; Robert A Vierkant; Tina J Hieken; Karthik Ghosh; Yaman Tarabishy; Celine M Vachon; Derek C Radisky; Lynn C Hartmann
Journal:  J Clin Oncol       Date:  2015-01-26       Impact factor: 44.544

9.  Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.

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10.  Breast Density and Benign Breast Disease: Risk Assessment to Identify Women at High Risk of Breast Cancer.

Authors:  Jeffrey A Tice; Diana L Miglioretti; Chin-Shang Li; Celine M Vachon; Charlotte C Gard; Karla Kerlikowske
Journal:  J Clin Oncol       Date:  2015-08-17       Impact factor: 44.544

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