Literature DB >> 26878225

Risk of Breast Cancer in Women with False-Positive Results according to Mammographic Features.

Xavier Castells1, Isabel Torá-Rocamora1, Margarita Posso1, Marta Román1, Maria Vernet-Tomas1, Ana Rodríguez-Arana1, Laia Domingo1, Carmen Vidal1, Marisa Baré1, Joana Ferrer1, María Jesús Quintana1, Mar Sánchez1, Carmen Natal1, Josep A Espinàs1, Francina Saladié1, María Sala1.   

Abstract

Purpose To assess the risk of breast cancer in women with false-positive screening results according to radiologic classification of mammographic features. Materials and Methods Review board approval was obtained, with waiver of informed consent. This retrospective cohort study included 521 200 women aged 50-69 years who underwent screening as part of the Spanish Breast Cancer Screening Program between 1994 and 2010 and who were observed until December 2012. Cox proportional hazards regression analysis was used to estimate the age-adjusted hazard ratio (HR) of breast cancer and the 95% confidence interval (CI) in women with false-positive mammograms as compared with women with negative mammograms. Separate models were adjusted for screen-detected and interval cancers and for screen-film and digital mammography. Time without a breast cancer diagnosis was plotted by using Kaplan-Meier curves. Results When compared with women with negative mammograms, the age-adjusted HR of cancer in women with false-positive results was 1.84 (95% CI: 1.73, 1.95; P < .001). The risk was higher in women who had calcifications, whether they were (HR, 2.73; 95% CI: 2.28, 3.28; P < .001) or were not (HR, 2.24; 95% CI: 2.02, 2.48; P < .001) associated with masses. Women in whom mammographic features showed changes in subsequent false-positive results were those who had the highest risk (HR, 9.13; 95% CI: 8.28, 10.07; P < .001). Conclusion Women with false-positive results had an increased risk of breast cancer, particularly women who had calcifications at mammography. Women who had more than one examination with false-positive findings and in whom the mammographic features changed over time had a highly increased risk of breast cancer. Previous mammographic features might yield useful information for further risk-prediction models and personalized follow-up screening protocols. (©) RSNA, 2016 Online supplemental material is available for this article.

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Year:  2016        PMID: 26878225     DOI: 10.1148/radiol.2016151174

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  9 in total

1.  Biomarkers expression in benign breast diseases and risk of subsequent breast cancer: a case-control study.

Authors:  Margarita Posso; Josep M Corominas; Laia Serrano; Marta Román; Isabel Torá-Rocamora; Laia Domingo; Anabel Romero; María Jesús Quintana; María Vernet-Tomas; Marisa Baré; Carmen Vidal; Mar Sánchez; Francina Saladié; Carmen Natal; Joana Ferrer; Sònia Servitja; María Sala; Xavier Castells
Journal:  Cancer Med       Date:  2017-05-04       Impact factor: 4.452

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Authors:  Maged A Aldhaeebi; Thamer S Almoneef; Abdulbaset Ali; Zhao Ren; Omar M Ramahi
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3.  Association of Microcalcification Clusters with Short-term Invasive Breast Cancer Risk and Breast Cancer Risk Factors.

Authors:  Maya Alsheh Ali; Kamila Czene; Per Hall; Keith Humphreys
Journal:  Sci Rep       Date:  2019-10-10       Impact factor: 4.379

4.  A systematic review and quality assessment of individualised breast cancer risk prediction models.

Authors:  Javier Louro; Margarita Posso; Michele Hilton Boon; Marta Román; Laia Domingo; Xavier Castells; María Sala
Journal:  Br J Cancer       Date:  2019-05-22       Impact factor: 7.640

5.  Developing and validating an individualized breast cancer risk prediction model for women attending breast cancer screening.

Authors:  Javier Louro; Marta Román; Margarita Posso; Ivonne Vázquez; Francina Saladié; Ana Rodriguez-Arana; M Jesús Quintana; Laia Domingo; Marisa Baré; Rafael Marcos-Gragera; María Vernet-Tomas; Maria Sala; Xavier Castells
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6.  Reducing Unnecessary Biopsies Using Digital Breast Tomosynthesis and Ultrasound in Dense and Nondense Breasts.

Authors:  Ibrahim Hadadi; Jillian Clarke; William Rae; Mark McEntee; Wendy Vincent; Ernest Ekpo
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7.  External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women.

Authors:  Aimilia Gastounioti; Mikael Eriksson; Eric A Cohen; Walter Mankowski; Lauren Pantalone; Sarah Ehsan; Anne Marie McCarthy; Despina Kontos; Per Hall; Emily F Conant
Journal:  Cancers (Basel)       Date:  2022-09-30       Impact factor: 6.575

Review 8.  Errors in Mammography Cannot be Solved Through Technology Alone

Authors:  Ernest Usang Ekpo; Maram Alakhras; Patrick Brennan
Journal:  Asian Pac J Cancer Prev       Date:  2018-02-26

9.  Mammographic microcalcifications and risk of breast cancer.

Authors:  Shadi Azam; Mikael Eriksson; Arvid Sjölander; Marike Gabrielson; Roxanna Hellgren; Kamila Czene; Per Hall
Journal:  Br J Cancer       Date:  2021-06-14       Impact factor: 7.640

  9 in total

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