Computer-aided detection system applied to full-field digital mammograms

Bolívar, A. Vega; Gómez, Sonia Sánchez; Merino, Paula; Alonso-Bartolomé, Pilar; García, E. Ortega; Cacho, Pedro Muñoz; Hoffmeister, Jeffrey W.

doi:10.3109/02841851.2010.520024

Cited by 15 publications

(15 citation statements)

References 30 publications

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“…Single reading with CAD was found to have comparable cancer detection rates to double reading by radiologists but with a slight increase in recall rates 7 . One study demonstrated that CAD improved radiologist sensitivity by 21% in digitised mammograms 8 whereas other researchers found that CAD with FFDM showed a high sensitivity in detecting cancers manifesting as calcifications or masses and that CAD sensitivity was maintained in small lesions of 1 to 20mm and invasive lobular carcinoma 9,10 . Single reading with CAD is used routinely in a number of countries, notably in the United States.…”

Section: Introductionmentioning

confidence: 97%

Initial experience with computer aided detection for microcalcification in digital breast tomosynthesis

Harkness

Lim²,

Wilson³

et al. 2015

SPIE Proceedings

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Digital breast tomosynthesis (DBT) addresses limitations of 2-D projection imaging for detection of masses. Microcalcification clusters may be more difficult to appreciate in DBT as individual calcifications within clusters may appear on different slices. This research aims to evaluate the performance of ImageChecker 3D Calc CAD v1.0. Women were recruited as part of the TOMMY trial. From the trial, 169 were included in this study. The DBT images were processed with the computer aided detection (CAD) algorithm. Three consultant radiologists reviewed the images and recorded whether CAD prompts were on or off target. 79/80 (98.8%) malignant cases had a prompt on the area of microcalcification. In these cases, there were 1-15 marks (median 5) with the majority of false prompts (n=326/431) due to benign (68%) and vascular (24%) calcifications. Of 89 normal/benign cases, there were 1-13 prompts (median 3), 27 (30%) had no prompts and the majority of false prompts (n=238) were benign (77%) calcifications. CAD is effective in prompting malignant microcalcification clusters and may overcome the difficulty of detecting clusters in slice images. Although there was a high rate of false prompts, further advances in the software may improve specificity.

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Section: Introductionmentioning

confidence: 97%

Initial experience with computer aided detection for microcalcification in digital breast tomosynthesis

Harkness

Lim²,

Wilson³

et al. 2015

SPIE Proceedings

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“…Mammographic breast density is one of of the significant risk factors for breast cancer [2]. In addition, sensitivity of breast cancer detection is reduced when reading mammograms of denser breasts for both radiologists [3] and Computer Aided Systems (CADs) [4]. Therefore, different methods, either qualitative or quantitative, have been proposed for measuring breast density.…”

Section: Introductionmentioning

confidence: 99%

iDensity: an automatic Gabor filter-based algorithm for breast density assessment

et al. 2015

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Although many semi-automated and automated algorithms for breast density assessment have been recently proposed, none of these have been widely accepted. In this study a novel automated algorithm, named iDensity, inspired by the human visual system is proposed for classifying mammograms into four breast density categories corresponding to the Breast Imaging Reporting and Data System (BI-RADS). For each BI-RADS category 80 cases were taken from the normal volumes of the Digital Database for Screening Mammography (DDSM). For each case only the left mediolateral oblique was utilized. After image calibration using the provided tables of each scanner in the DDSM, the pectoral muscle and background were removed. Images were filtered by a median filter and down sampled. Images were then filtered by a filter bank consisting of Gabor filters in six orientations and 3 scales, as well as a Gaussian filter. Three gray level histogrambased features and three second order statistics features were extracted from each filtered image. Using the extracted features, mammograms were separated initially separated into two groups, low or high density, then in a second stage, the low density group was subdivided into BI-RADS I or II, and the high density group into BI-RADS III or IV. The algorithm achieved a sensitivity of 95% and specificity of 94% in the first stage, sensitivity of 89% and specificity of 95% when classifying BI-RADS I and II cases, and a sensitivity of 88% and 91% specificity when classifying BI-RADS III and IV.

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“…Many studies have revealed that CAD can reduce the false negative rate and increase the detection of breast cancer, particularly early breast cancer [2,3,8-10] without a significant increase in recall rate [2,8] and false positive rate for biopsy [8,10]. More recent studies have reported that CAD systems for full-field digital mammograms can also improve the diagnostic performance of mammograms [11,12]. Yang et al reported that the CAD system can correctly mark most asymptomatic breast cancers detected with digital mammographic screening [11], and Bolivar et al demonstrated that improved CAD sensitivity was maintained for small lesions and invasive lobular carcinomas, which have lower mammographic sensitivity [12].…”

Section: Introductionmentioning

confidence: 99%

“…More recent studies have reported that CAD systems for full-field digital mammograms can also improve the diagnostic performance of mammograms [11,12]. Yang et al reported that the CAD system can correctly mark most asymptomatic breast cancers detected with digital mammographic screening [11], and Bolivar et al demonstrated that improved CAD sensitivity was maintained for small lesions and invasive lobular carcinomas, which have lower mammographic sensitivity [12]. …”

Section: Introductionmentioning

confidence: 99%

Who could benefit the most from using a computer-aided detection system in full-field digital mammography?

et al. 2014

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BackgroundThe computer-aided detection (CAD) system on mammography has the potential to assist radiologists in breast cancer screening. The purpose of this study is to evaluate the diagnostic performance of the CAD system in full-field digital mammography for detecting breast cancer when used by dedicated breast radiologist (BR) and radiology resident (RR), and to reveal who could benefit the most from a CAD application.MethodsWe retrospectively chose 100 image sets from mammographies performed with CAD between June 2008 and June 2010. Thirty masses (15 benign and 15 malignant), 30 microcalcifications (15 benign and 15 malignant), and 40 normal mammography images were included. The participating radiologists consisted of 7 BRs and 13 RRs. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for total, normal plus microcalcification and normal plus mass both with and without CAD use for each reader. We compared the diagnostic performance values obtained with and without CAD use for the BR and RR groups, respectively. The reading time reviewing one set of 100 images and time reduction with CAD use for the BR and RR groups were also evaluated.ResultsThe diagnostic performance was generally higher in the BR group than in the RR group. Sensitivity improved with CAD use in the BR and RR groups (from 81.10 to 84.29% for BR; 75.38 to 77.95% for RR). A tendency for improvement in all diagnostic performance values was observed in the BR group, whereas in the RR group, sensitivity improved but specificity, PPV, and NPV did not. None of the diagnostic performance parameters were significantly different. The mean reading time was shortened with CAD use in both the BR and RR groups (111.6 minutes to 94.3 minutes for BR; 135.5 minutes to 109.8 minutes for RR). The mean time reduction was higher for the RR than that in the BR group.ConclusionsCAD was helpful for dedicated BRs to improve their diagnostic performance and for RRs to improve the sensitivity in a screening setting. CAD could be essential for radiologists by decreasing reading time without decreasing diagnostic performance.

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Computer-aided detection system applied to full-field digital mammograms

Abstract: CAD with FFDM showed a high sensitivity in identifying cancers manifesting as calcifications or masses. CAD sensitivity was maintained in small lesions (1-20 mm) and invasive lobular carcinomas, which have lower mammographic sensitivity.

Cited by 15 publications

References 30 publications

Initial experience with computer aided detection for microcalcification in digital breast tomosynthesis

Initial experience with computer aided detection for microcalcification in digital breast tomosynthesis

iDensity: an automatic Gabor filter-based algorithm for breast density assessment

Who could benefit the most from using a computer-aided detection system in full-field digital mammography?

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