Literature DB >> 21216632

Missed and true interval and screen-detected breast cancers in a population based screening program.

Solveig Roth Hoff1, Jon Helge Samset, Anne-Line Abrahamsen, Einar Vigeland, Olbjørn Klepp, Solveig Hofvind.   

Abstract

RATIONALE AND
OBJECTIVES: To increase radiologic knowledge, the distribution of mammographic features on prior screening mammograms of missed interval and screen-detected cancers was compared to the distribution on diagnostic mammograms of screen-detected cancers. The same variables were compared on mammograms of discordant and concordant screen-detected cancers.
MATERIALS AND METHODS: The study was performed in Møre og Romsdal County, Norway, as a part of the quality assurance of the Norwegian Breast Cancer Screening Program. Women were screened using analog techniques and diagnosed from 2002 to 2008. Prior and diagnostic mammograms of 81 interval and 123 screen-detected breast cancers in women aged 50 to 71 years were retrospectively reviewed and classified as either missed or true by four experienced breast radiologists. Mammographic features were classified according to a modified Breast Imaging Reporting and Data System.
RESULTS: Thirty percent (24 of 81) of the interval cancers and 21% (26 of 123) of the screen-detected cancers were classified as missed. Calcifications, alone or in association with mass or asymmetry, tended to be more common on prior mammograms of missed cancers compared to diagnostic mammograms of screen-detected cancers (34% [17 of 50] vs 21% [26 of 123], P = .114), whereas an opposite trend was seen for mass (54% [27 of 50] vs 68% [84 of 123], P = .109). Similar results were seen when comparing discordant and concordant cancers.
CONCLUSIONS: Calcifications represent a challenge in the interpretation of screening mammograms. For educational purposes, the importance of reviewing both interval and screen-detected cancers is obvious. Knowledge gained from systematic reviews might reduce the number of missed cancers on mammographic screening. Performing reviews according to established guidelines would make it possible to compare results across screening programs.
Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 21216632     DOI: 10.1016/j.acra.2010.11.014

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  7 in total

1.  Body Mass Index Is Inversely Associated with Risk of Postmenopausal Interval Breast Cancer: Results from the Women's Health Initiative.

Authors:  Zhenzhen Zhang; Grace Curran; Jackilen Shannon; Ellen M Velie; Veronica L Irvin; JoAnn E Manson; Michael S Simon; Duygu Altinok Dindar; Chelsea Pyle; Pepper Schedin; Fred K Tabung
Journal:  Cancers (Basel)       Date:  2022-06-30       Impact factor: 6.575

2.  Diet-Driven Inflammation and Insulinemia and Risk of Interval Breast Cancer.

Authors:  Zhenzhen Zhang; Fred K Tabung; Qi Jin; Grace Curran; Veronica L Irvin; Jackilen Shannon; Ellen M Velie; JoAnn E Manson; Michael S Simon; Mara Vitolins; Celina I Valencia; Linda Snetselaar; Sonali Jindal; Pepper Schedin
Journal:  Nutr Cancer       Date:  2022-04-26       Impact factor: 2.816

3.  Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

Authors:  Abdul Rahman Diab; Bryan Haslam; Jiye G Kim; William Lotter; Giorgia Grisot; Eric Wu; Kevin Wu; Jorge Onieva Onieva; Yun Boyer; Jerrold L Boxerman; Meiyun Wang; Mack Bandler; Gopal R Vijayaraghavan; A Gregory Sorensen
Journal:  Nat Med       Date:  2021-01-11       Impact factor: 87.241

Review 4.  Use-inspired basic research in medical image perception.

Authors:  Jeremy M Wolfe
Journal:  Cogn Res Princ Implic       Date:  2016-11-14

5.  Can high school students help to improve breast radiologists in detecting missed breast cancer lesions on full-field digital mammography?

Authors:  T J A van Nijnatten; M L Smidt; B Goorts; S Samiei; I Houben; E M Kok; J E Wildberger; S G F Robben; M B I Lobbes
Journal:  J Cancer       Date:  2019-01-01       Impact factor: 4.207

6.  Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis.

Authors:  Ga Eun Park; Bong Joo Kang; Sung Hun Kim; Jeongmin Lee
Journal:  Diagnostics (Basel)       Date:  2022-02-02

7.  Application of deep learning in the detection of breast lesions with four different breast densities.

Authors:  Hongmei Li; Jing Ye; Hao Liu; Yichuan Wang; Binbin Shi; Juan Chen; Aiping Kong; Qing Xu; Junhui Cai
Journal:  Cancer Med       Date:  2021-06-16       Impact factor: 4.452

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.