Literature DB >> 28029492

Overdiagnosis in breast imaging.

Andy Evans1, Sarah Vinnicombe2.   

Abstract

The main harm of overdiagnosis is overtreatment. However a form of overdiagnosis also occurs when foci of cancer are found by imaging in addition to the symptomatic lesion when this leads to additional treatment which does not benefit the patient. Even if overtreatment is avoided, knowledge of the diagnosis can still cause psychological harm. Overdiagnosis is an inevitable effect of mammographic screening as the benefit comes from diagnosing breast cancer prior to clinical detectability. Estimates of the rate of overdiagnosis at screening are around 10%. DCIS represents 20% of cancers detected by screening and is the main focus in the overdiagnosis debate. Detection and treatment of low grade DCIS and invasive tubular cancer would appear to represent overdiagnosis in most cases. Supplementary screening with tomosynthesis or US are both likely to increase overdiagnosis as both modalities detect predominantly low grade invasive cancers. MRI causes overdiagnosis because it is so sensitive that it detects real tumour foci which after radiotherapy and systemic therapy do not, in many cases go on and cause local recurrence if the women had had no MRI and undergone breast conservation and adjuvant therapy with these small foci left in situ.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Breast imaging; Mammography; Overdiagnosis; Tomosynthesis; Ultrasound

Mesh:

Year:  2016        PMID: 28029492     DOI: 10.1016/j.breast.2016.10.011

Source DB:  PubMed          Journal:  Breast        ISSN: 0960-9776            Impact factor:   4.380


  8 in total

1.  Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning.

Authors:  Michael S Manak; Jonathan S Varsanik; Brad J Hogan; Matt J Whitfield; Wendell R Su; Nikhil Joshi; Nicolai Steinke; Andrew Min; Delaney Berger; Robert J Saphirstein; Gauri Dixit; Thiagarajan Meyyappan; Hui-May Chu; Kevin B Knopf; David M Albala; Grannum R Sant; Ashok C Chander
Journal:  Nat Biomed Eng       Date:  2018-09-17       Impact factor: 25.671

2.  Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ.

Authors:  Linyong Wu; Yujia Zhao; Peng Lin; Hui Qin; Yichen Liu; Da Wan; Xin Li; Yun He; Hong Yang
Journal:  BMC Med Imaging       Date:  2021-05-17       Impact factor: 1.930

3.  The Challenges of Screening Mammography in Racial/Ethnic Minority Populations in the United States: A mini-review and observations from a predominantly Hispanic community.

Authors:  Julia E McGuinness; Katherine D Crew
Journal:  J Cancer Treatment Diagn       Date:  2018-04-05

Review 4.  [Artificial intelligence in breast imaging : Areas of application from a clinical perspective].

Authors:  Pascal A T Baltzer
Journal:  Radiologe       Date:  2021-01-28       Impact factor: 0.635

5.  Downstream Mammary and Extramammary Cascade Services and Spending Following Screening Breast Magnetic Resonance Imaging vs Mammography Among Commercially Insured Women.

Authors:  Ishani Ganguli; Nancy L Keating; Nitya Thakore; Joyce Lii; Sughra Raza; Lydia E Pace
Journal:  JAMA Netw Open       Date:  2022-04-01

6.  Automated artifact detection in abbreviated dynamic contrast-enhanced (DCE) MRI-derived maximum intensity projections (MIPs) of the breast.

Authors:  Lorenz A Kapsner; Sabine Ohlmeyer; Lukas Folle; Frederik B Laun; Armin M Nagel; Andrzej Liebert; Hannes Schreiter; Matthias W Beckmann; Michael Uder; Evelyn Wenkel; Sebastian Bickelhaupt
Journal:  Eur Radiol       Date:  2022-04-02       Impact factor: 7.034

7.  Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging.

Authors:  Alistair Mackenzie; Emma L Thomson; Melissa Mitchell; Premkumar Elangovan; Chantal van Ongeval; Lesley Cockmartin; Lucy M Warren; Louise S Wilkinson; Matthew G Wallis; Rosalind M Given-Wilson; David R Dance; Kenneth C Young
Journal:  Eur Radiol       Date:  2021-07-30       Impact factor: 5.315

8.  Identifying normal mammograms in a large screening population using artificial intelligence.

Authors:  Kristina Lång; Magnus Dustler; Victor Dahlblom; Anna Åkesson; Ingvar Andersson; Sophia Zackrisson
Journal:  Eur Radiol       Date:  2020-09-02       Impact factor: 5.315

  8 in total

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