Literature DB >> 11526282

Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center.

T W Freer1, M J Ulissey.   

Abstract

PURPOSE: To prospectively assess the effect of computer-aided detection (CAD) on the interpretation of screening mammograms in a community breast center.
MATERIALS AND METHODS: Over a 12-month period, 12,860 screening mammograms were interpreted with the assistance of a CAD system. Each mammogram was initially interpreted without the assistance of CAD, followed immediately by a reevaluation of areas marked by the CAD system. Data were recorded to measure the effect of CAD on the recall rate, positive predictive value for biopsy, cancer detection rate, and stage of malignancies at detection.
RESULTS: When comparing the radiologist's performance without CAD with that when CAD was used, the authors observed the following: (a) an increase in recall rate from 6.5% to 7.7%, (b) no change in the positive predictive value for biopsy at 38%, (c) a 19.5% increase in the number of cancers detected, and (d) an increase in the proportion of early-stage (0 and I) malignancies detected from 73% to 78%.
CONCLUSION: The use of CAD in the interpretation of screening mammograms can increase the detection of early-stage malignancies without undue effect on the recall rate or positive predictive value for biopsy.

Entities:  

Mesh:

Year:  2001        PMID: 11526282     DOI: 10.1148/radiol.2203001282

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  130 in total

1.  Soft copy display requirements for digital mammography.

Authors:  Bradley M Hemminger
Journal:  J Digit Imaging       Date:  2003-12-15       Impact factor: 4.056

2.  Computer-assisted detection of pulmonary nodules: preliminary observations using a prototype system with multidetector-row CT data sets.

Authors:  Leo P Lawler; Susan A Wood; Harpreet K Pannu; Elliot K Fishman; Harpreet S Pannu
Journal:  J Digit Imaging       Date:  2003-12-15       Impact factor: 4.056

3.  An interactive system for computer-aided diagnosis of breast masses.

Authors:  Xingwei Wang; Lihua Li; Wei Liu; Weidong Xu; Dror Lederman; Bin Zheng
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

4.  Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: a 3D approach.

Authors:  Berkman Sahiner; Heang-Ping Chan; Lubomir M Hadjiiski; Mark A Helvie; Jun Wei; Chuan Zhou; Yao Lu
Journal:  Med Phys       Date:  2012-01       Impact factor: 4.071

5.  Automated detection of mass lesions in dedicated breast CT: a preliminary study.

Authors:  I Reiser; R M Nishikawa; M L Giger; J M Boone; K K Lindfors; K Yang
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

Review 6.  After Detection: The Improved Accuracy of Lung Cancer Assessment Using Radiologic Computer-aided Diagnosis.

Authors:  Guy J Amir; Harold P Lehmann
Journal:  Acad Radiol       Date:  2015-11-23       Impact factor: 3.173

7.  Image toggling saves time in mammography.

Authors:  Trafton Drew; Avi M Aizenman; Matthew B Thompson; Mark D Kovacs; Michael Trambert; Murray A Reicher; Jeremy M Wolfe
Journal:  J Med Imaging (Bellingham)       Date:  2015-10-12

8.  Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study.

Authors:  Anton S Becker; Michael Mueller; Elina Stoffel; Magda Marcon; Soleen Ghafoor; Andreas Boss
Journal:  Br J Radiol       Date:  2018-01-10       Impact factor: 3.039

Review 9.  State of the art of current modalities for the diagnosis of breast lesions.

Authors:  Cosimo Di Maggio
Journal:  Eur J Nucl Med Mol Imaging       Date:  2004-04-15       Impact factor: 9.236

Review 10.  Screening for breast cancer.

Authors:  Joann G Elmore; Katrina Armstrong; Constance D Lehman; Suzanne W Fletcher
Journal:  JAMA       Date:  2005-03-09       Impact factor: 56.272

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