Literature DB >> 27389655

Mammographic density is the main correlate of tumors detected on ultrasound but not on mammography.

Lothar Häberle1,2, Peter A Fasching1,3, Barbara Brehm4, Katharina Heusinger1, Sebastian M Jud1, Christian R Loehberg1, Carolin C Hack1, Caroline Preuss1, Michael P Lux1, Arndt Hartmann5, Celine M Vachon6, Martina Meier-Meitinger4, Michael Uder4, Matthias W Beckmann1, Rüdiger Schulz-Wendtland4.   

Abstract

Although mammography screening programs do not include ultrasound examinations, some diagnostic units do provide women with both mammography and ultrasonography. This article is concerned with estimating the risk of a breast cancer patient diagnosed in a hospital-based mammography unit having a tumor that is visible on ultrasound but not on mammography. A total of 1,399 women with invasive breast cancer from a hospital-based diagnostic mammography unit were included in this retrospective study. For inclusion, mammograms from the time of the primary diagnosis had to be available for computer-assisted assessment of percentage mammographic density (PMD), as well as Breast Imaging Reporting and Data System (BIRADS) assessment of mammography. In addition, ultrasound findings were available for the complete cohort as part of routine diagnostic procedures, regardless of any patient or imaging characteristics. Logistic regression analyses were conducted to identify predictors of mammography failure, defined as BIRADS assessment 1 or 2. The probability that the visibility of a tumor might be masked at diagnosis was estimated using a regression model with the identified predictors. Tumors were only visible on ultrasound in 107 cases (7.6%). PMD was the strongest predictor for mammography failure, but age, body mass index and previous breast surgery also influenced the risk, independently of the PMD. Risk probabilities ranged from 1% for a defined low-risk group up to 40% for a high-risk group. These findings might help identify women who should be offered ultrasound examinations in addition to mammography.
© 2016 UICC.

Entities:  

Keywords:  mammographic density; mammography screening; masking; risk prediction

Mesh:

Year:  2016        PMID: 27389655     DOI: 10.1002/ijc.30261

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  6 in total

1.  Predicting Triple-Negative Breast Cancer Subtype Using Multiple Single Nucleotide Polymorphisms for Breast Cancer Risk and Several Variable Selection Methods.

Authors:  Lothar Häberle; Alexander Hein; Matthias Rübner; Michael Schneider; Arif B Ekici; Paul Gass; Arndt Hartmann; Rüdiger Schulz-Wendtland; Matthias W Beckmann; Wing-Yee Lo; Werner Schroth; Hiltrud Brauch; Peter A Fasching; Marius Wunderle
Journal:  Geburtshilfe Frauenheilkd       Date:  2017-06-28       Impact factor: 2.915

2.  Diagnostic Accuracy of Breast Medical Tactile Examiners (MTEs): A Prospective Pilot Study.

Authors:  Michael P Lux; Julius Emons; Mayada R Bani; Marius Wunderle; Charlotte Sell; Caroline Preuss; Claudia Rauh; Sebastian M Jud; Felix Heindl; Hanna Langemann; Thomas Geyer; Anna-Lisa Brandl; Carolin C Hack; Werner Adler; Rüdiger Schulz-Wendtland; Matthias W Beckmann; Peter A Fasching; Paul Gass
Journal:  Breast Care (Basel)       Date:  2019-01-30       Impact factor: 2.860

3.  Digital breast tomosynthesis: sensitivity for cancer in younger symptomatic women.

Authors:  Patsy Whelehan; Kulsam Ali; Sarah Vinnicombe; Graham Ball; Julie Cox; Paul Farry; Maggie Jenkin; Keith Lowry; Stuart A McIntosh; Rachel Nutt; Rachel Oeppen; Michael Reilly; Michaela Stahnke; Jim Steel; Yee Ting Sim; Violet Warwick; Louise Wilkinson; Dimitrios Zafeiris; Andrew J Evans
Journal:  Br J Radiol       Date:  2021-01-07       Impact factor: 3.039

4.  Saliva samples as a source of DNA for high throughput genotyping: an acceptable and sufficient means in improvement of risk estimation throughout mammographic diagnostics.

Authors:  U G Poehls; C C Hack; A B Ekici; M W Beckmann; P A Fasching; M Ruebner; H Huebner
Journal:  Eur J Med Res       Date:  2018-04-27       Impact factor: 2.175

5.  Risk, Prediction and Prevention of Hereditary Breast Cancer - Large-Scale Genomic Studies in Times of Big and Smart Data.

Authors:  Marius Wunderle; Gregor Olmes; Naiba Nabieva; Lothar Häberle; Sebastian M Jud; Alexander Hein; Claudia Rauh; Carolin C Hack; Ramona Erber; Arif B Ekici; Juliane Hoyer; Georgia Vasileiou; Cornelia Kraus; André Reis; Arndt Hartmann; Rüdiger Schulz-Wendtland; Michael P Lux; Matthias W Beckmann; Peter A Fasching
Journal:  Geburtshilfe Frauenheilkd       Date:  2018-06-04       Impact factor: 2.915

6.  Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound.

Authors:  Lothar Häberle; Carolin C Hack; Katharina Heusinger; Florian Wagner; Sebastian M Jud; Michael Uder; Matthias W Beckmann; Rüdiger Schulz-Wendtland; Thomas Wittenberg; Peter A Fasching
Journal:  Eur J Med Res       Date:  2017-08-30       Impact factor: 2.175

  6 in total

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