Literature DB >> 29622173

Impact of the California Breast Density Law on Screening Breast MR Utilization, Provider Ordering Practices, and Patient Demographics.

Shruthi Ram1, Nandini Sarma2, Javier E López3, Yu Liu4, Chin-Shang Li5, Shadi Aminololama-Shakeri6.   

Abstract

PURPOSE: To assess the impact of California's Breast Density Law (BDL) on MRI utilization and clinician ordering practices.
MATERIALS AND METHODS: Our institutional review board approved this study that retrospectively compared the ordering pattern for screening breast MRI examinations in the 30-month period before and after the BDL was enacted. Examinations were subcategorized into those with breast density mentioned as an examination indication. Patients were classified into (1) high risk; (2) above average risk, defined but not quantified; and (3) undefined or average risk. χ2 test or Fisher's exact test was used to compare MRI utilization, use of breast density as an indication, patient demographics, and provider characteristics.
RESULTS: Screening MRI examinations with breast density as the indication increased from 8.5% (32 of 376) to 21.1% (136 of 646, P < .0001) after BDL. When high-risk patients were excluded, the increase was from 8% to 17.2% (P < .0001). Patient demographics before and after BDL were, by race: white 71.8% versus 71.2%; Asian 6.4% versus 10.5%; black 3.7% versus 3.1%; American Indian 0.3% versus 1.4%; Native Hawaiian or Pacific Islander 1.6% versus 1.7%; by ethnicity: Hispanic or Latino 10.6% versus 7.9%. Before and after BDL, predominantly female providers (81.4% and 77.4%, P = not significant [NS]) and specialists (62.5% and 63.5%, P = NS) ordered the majority of breast MRI examinations compared with males (18.6% and 22.6%, P = NS).
CONCLUSION: Screening breast MRI utilization for non-high-risk women more than doubled after the California BDL went into effect. BDL has had an impact on MRI utilization, and its clinical value for changing outcomes deserves further study.
Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast density legislation; dense breasts; disparities; racial; screening breast MRI; supplemental screening

Mesh:

Year:  2018        PMID: 29622173     DOI: 10.1016/j.jacr.2017.12.001

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  5 in total

Review 1.  The impact of mandatory mammographic breast density notification on supplemental screening practice in the United States: a systematic review.

Authors:  Meagan Brennan; Brooke Nickel; Shuangqin Huang; Nehmat Houssami
Journal:  Breast Cancer Res Treat       Date:  2021-03-28       Impact factor: 4.872

2.  Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.

Authors:  Yang Zhang; Jeon-Hor Chen; Kai-Ting Chang; Vivian Youngjean Park; Min Jung Kim; Siwa Chan; Peter Chang; Daniel Chow; Alex Luk; Tiffany Kwong; Min-Ying Su
Journal:  Acad Radiol       Date:  2019-01-31       Impact factor: 3.173

3.  Supplemental Breast Imaging Utilization After Breast Density Legislation in North Carolina.

Authors:  Sarah J Nyante; Mary W Marsh; Thad Benefield; Kathryn Earnhardt; Sheila S Lee; Louise M Henderson
Journal:  J Am Coll Radiol       Date:  2019-07-01       Impact factor: 5.532

4.  Prospective multicenter assessment of patient preferences for properties of gadolinium-based contrast media and their potential socioeconomic impact in a screening breast MRI setting.

Authors:  Sean A Woolen; Jonathan P Troost; Shokoufeh Khalatbari; Akshat C Pujara; Jennifer S McDonald; Robert J McDonald; Prasad Shankar; Alana A Lewin; Amy N Melsaether; Steven M Westphal; Katherine H Patterson; Ashley Nettles; John P Welby; Parth Pradip Patel; Neud Kiros; Lisa Piccoli; Matthew S Davenport
Journal:  Eur Radiol       Date:  2021-05-28       Impact factor: 5.315

5.  Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.

Authors:  Yang Zhang; Siwa Chan; Jeon-Hor Chen; Kai-Ting Chang; Chin-Yao Lin; Huay-Ben Pan; Wei-Ching Lin; Tiffany Kwong; Ritesh Parajuli; Rita S Mehta; Sou-Hsin Chien; Min-Ying Su
Journal:  J Digit Imaging       Date:  2021-07-09       Impact factor: 4.056

  5 in total

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