Literature DB >> 12449356

Computer-aided detection in mammography: an assessment of performance on current and prior images.

Bin Zheng1, Ratan Shah, Luisa Wallace, Christiane Hakim, Marie A Ganott, David Gur.   

Abstract

RATIONALE AND
OBJECTIVES: The authors assessed and compared the performance of a computer-aided detection (CAD) scheme for the detection of masses and microcalcification clusters on a set of images collected from two consecutive ("current" and "prior") mammographic examinations.
MATERIALS AND METHODS: A previously developed CAD scheme was used to assess two consecutive screening mammograms from 200 cases in which the current mammogram showed a mass or cluster of microcalcifications that resulted in breast biopsy. The latest prior examinations had been initially interpreted as negative or definitely benign findings (Breast Imaging Reporting and Data System rating, 1 or 2). The study involved images of 400 examinations acquired in 200 patients. Radiologists identified 172 masses and 128 clusters of microcalcifications on the current images. The performance of the CAD scheme was analyzed and compared for the current and latest prior images.
RESULTS: There were significant differences (P < .01) between current and prior images in many feature values. The performance of the CAD scheme was significantly lower for prior than for current images (P < .01). At 0.5 and 0.2 false-positive mass and cluster cues per image, the scheme detected 78 malignant masses (78%) and 63 malignant clusters (80%) on current images. Only 42% of malignant cases were detected on prior images, including 40 masses (40%) and 36 microcalcification clusters (46%).
CONCLUSION: CAD schemes can detect a substantial fraction of masses and microcalcification clusters depicted on prior images. To improve performance with prior images, the scheme may have to be adaptively reoptimized with increasingly more subtle abnormalities.

Entities:  

Mesh:

Year:  2002        PMID: 12449356     DOI: 10.1016/s1076-6332(03)80557-3

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


  8 in total

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4.  Improving performance of computer-aided detection of masses by incorporating bilateral mammographic density asymmetry: an assessment.

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5.  Improving performance of computer-aided detection scheme by combining results from two machine learning classifiers.

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7.  Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis.

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8.  The importance of early detection of calcifications associated with breast cancer in screening.

Authors:  J J Mordang; A Gubern-Mérida; A Bria; F Tortorella; R M Mann; M J M Broeders; G J den Heeten; N Karssemeijer
Journal:  Breast Cancer Res Treat       Date:  2017-10-17       Impact factor: 4.872

  8 in total

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