Literature DB >> 16990959

Breast carcinomas: why are they missed?

M Muttarak1, S Pojchamarnwiputh, B Chaiwun.   

Abstract

INTRODUCTION: Mammography has proven to be an effective modality for the detection of early breast carcinoma. However, 4-34 percent of breast cancers may be missed at mammography. Delayed diagnosis of breast carcinoma results in an unfavourable prognosis. The objective of this study was to determine the causes and characteristics of breast carcinomas missed by mammography at our institution, with the aim of reducing the rate of missed carcinoma.
METHODS: We reviewed the reports of 13,191 mammograms performed over a five-year period. Breast Imaging Reporting and Data Systems (BI-RADS) were used for the mammographical assessment, and reports were cross-referenced with the histological diagnosis of breast carcinoma. Causes of missed carcinomas were classified.
RESULTS: Of 344 patients who had breast carcinoma and had mammograms done prior to surgery, 18 (5.2 percent) failed to be diagnosed by mammography. Of these, five were caused by dense breast parenchyma obscuring the lesions, 11 were due to perception and interpretation errors, and one each from unusual lesion characteristics and poor positioning.
CONCLUSION: Several factors, including dense breast parenchyma obscuring a lesion, perception error, interpretation error, unusual lesion characteristics, and poor technique or positioning, are possible causes of missed breast cancers.

Entities:  

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Year:  2006        PMID: 16990959

Source DB:  PubMed          Journal:  Singapore Med J        ISSN: 0037-5675            Impact factor:   1.858


  5 in total

1.  Analysis of mammographic diagnostic errors in breast clinic.

Authors:  V Palazzetti; F Guidi; L Ottaviani; G Valeri; S Baldassarre; G M Giuseppetti
Journal:  Radiol Med       Date:  2016-07-02       Impact factor: 3.469

2.  Breast Positioning during Mammography: Mistakes to be Avoided.

Authors:  Manju Bala Popli; Rahul Teotia; Meenakshi Narang; Hare Krishna
Journal:  Breast Cancer (Auckl)       Date:  2014-07-30

3.  Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.

Authors:  Jinhua Wang; Xi Yang; Hongmin Cai; Wanchang Tan; Cangzheng Jin; Li Li
Journal:  Sci Rep       Date:  2016-06-07       Impact factor: 4.379

4.  Differential diagnostic performance of acoustic radiation force impulse imaging in small (≤20 mm) breast cancers: Is it valuable?

Authors:  Si-Da Wang; Lei Wang; Zhi-Xian Li; Kang-Lai Wei; Xin-Hong Liao; Yuan-Yuan Chen; Xue Huang
Journal:  Sci Rep       Date:  2017-08-17       Impact factor: 4.379

5.  Ultrasonographic characteristics of mammographically occult small breast cancer.

Authors:  Pornpim Korpraphong; Oranan Tritanon; Woranuj Tangcharoensathien; Tamnit Angsusinha; Suebwong Chuthapisith
Journal:  J Breast Cancer       Date:  2012-09-28       Impact factor: 3.588

  5 in total

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