Literature DB >> 26341750

False-negative rate of combined mammography and ultrasound for women with palpable breast masses.

Carlos H F Chan1, Suzanne B Coopey2, Phoebe E Freer3, Kevin S Hughes4.   

Abstract

Mammography and ultrasound are often used concurrently for patients with palpable breast masses. While mammography has a false-negative rate of approximately 15 %, the addition of breast ultrasound decreases this rate among patients with palpable breast masses. There are no recent outcome data regarding the use of combined reporting of ultrasound and mammography (CRUM) for palpable breast masses. In this study, female patients presenting with a palpable breast mass were retrospectively reviewed in a prospectively entered database at a single institution from June 2010 to July 2013. All cancer cases and false-negative cases using CRUM were identified. Cancer rates, false-negative rates, and negative predictive values were calculated based on CRUM breast imaging-reporting and data system (BI-RADS) categories. One thousand two hundreds and twelve female patients presenting with a palpable breast mass were identified; 77 % of patients had CRUM and 73 % (682/932) were BI-RADS 1-2. Despite negative or benign BI-RADS, 9.5 % of patients with BI-RADS 1-2 (65/682) underwent biopsy, compared to 96 % of patients with a BI-RADS 4-5 designation. Eighty-one patients were found to have cancers; 2 had BI-RADS 1-2 imaging. The false-negative rate of CRUM was 2.4 % (2/81). Since 69 % (428/617) of BI-RADS 1-2 patients without tissue diagnosis had follow-up imaging and/or clinical exam (median: 27 months, range: 2-62 months) and none developed cancers, the cancer rate and negative predictive value of a palpable breast mass of BI-RADS 1-2 were estimated to be 0.3 % (2/682) and 99.7 %, respectively. In the modern era of combined imaging for breast masses, a patient with a low suspicion exam can be reassured with a negative CRUM report.

Entities:  

Keywords:  Breast ultrasound; Mammography; Palpable breast mass

Mesh:

Year:  2015        PMID: 26341750     DOI: 10.1007/s10549-015-3557-2

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  5 in total

1.  Synergy in combining findings from mammography and ultrasonography in detecting malignancy in women with higher density breasts and lesions over 2 cm in Albania.

Authors:  Altin Malaj; Albana Shahini
Journal:  Contemp Oncol (Pozn)       Date:  2017-01-12

2.  An Ad Hoc Random Initialization Deep Neural Network Architecture for Discriminating Malignant Breast Cancer Lesions in Mammographic Images.

Authors:  Andrea Duggento; Marco Aiello; Carlo Cavaliere; Giuseppe L Cascella; Davide Cascella; Giovanni Conte; Maria Guerrisi; Nicola Toschi
Journal:  Contrast Media Mol Imaging       Date:  2019-05-22       Impact factor: 3.161

3.  The influence of size, depth and histologic characteristics of invasive ductal breast carcinoma on thermographic properties of the breast.

Authors:  Marko Mance; Krešimir Bulic; Anko Antabak; Milan Miloševic
Journal:  EXCLI J       Date:  2019-07-22       Impact factor: 4.068

Review 4.  The Right Direction Needed to Develop White-Box Deep Learning in Radiology, Pathology, and Ophthalmology: A Short Review.

Authors:  Yoichi Hayashi
Journal:  Front Robot AI       Date:  2019-04-16

5.  Plasma extracellular vesicle long RNA profiles in the diagnosis and prediction of treatment response for breast cancer.

Authors:  Yonghui Su; Yuchen Li; Rong Guo; Jingjing Zhao; Weiru Chi; Hongyan Lai; Jia Wang; Zhen Wang; Lun Li; Yuting Sang; Jianjing Hou; Jingyan Xue; Zhimin Shao; Yayun Chi; Shenglin Huang; Jiong Wu
Journal:  NPJ Breast Cancer       Date:  2021-12-10
  5 in total

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