Literature DB >> 1509042

Reading and decision aids for improved accuracy and standardization of mammographic diagnosis.

C J D'Orsi1, D J Getty, J A Swets, R M Pickett, S E Seltzer, B J McNeil.   

Abstract

Image-reading and decision aids were designed to improve the accuracy of mammogram interpretation. The reading aid was a list of diagnostic radiographic features and scales for quantification of each feature. The decision aid, a computer program, converted the reader's scaled values, weighted for predictive power, into an advisory estimate of the probability of malignancy. The features were identified and their importance was assigned in four steps: (a) interviews of five expert readers to establish an initial set of features, (b) perceptual tests to refine the feature set, (c) a consensus meeting to refine this set and establish nomenclature and scales, and (d) the expert's scaling of each feature in a set of 150 mammograms. Those scaled judgments were analyzed to provide the final list of features and their relative importance and to program the computer decision aid. To test the enhancement effect, six other radiologists interpreted a different set of mammograms without, and later with, the two aids. Receiver operating characteristic analysis showed a gain of approximately 0.05 in sensitivity or specificity when the other value remained at 0.85. In a subset of the more difficult cases, the enhancement effect was approximately 0.15 in either sensitivity or specificity.

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Year:  1992        PMID: 1509042     DOI: 10.1148/radiology.184.3.1509042

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


  7 in total

1.  CADMIUM II: acquisition and representation of radiological knowledge for computerized decision support in mammography.

Authors:  E Alberdi; P Taylor; R Lee; J Fox; M Sordo; A Todd-Pokropek
Journal:  Proc AMIA Symp       Date:  2000

Review 2.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

3.  Voice-activated retrieval of mammography reference images.

Authors:  H A Swett; P G Mutalik; V P Neklesa; L Horvath; C Lee; J Richter; I Tocino; P R Fisher
Journal:  J Digit Imaging       Date:  1998-05       Impact factor: 4.056

4.  CT Colonography Reporting and Data System (C-RADS): benchmark values from a clinical screening program.

Authors:  B Dustin Pooler; David H Kim; Vu P Lam; Elizabeth S Burnside; Perry J Pickhardt
Journal:  AJR Am J Roentgenol       Date:  2014-06       Impact factor: 3.959

5.  Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer.

Authors:  C E Kahn; L M Roberts; K Wang; D Jenks; P Haddawy
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1995

6.  The ACR BI-RADS experience: learning from history.

Authors:  Elizabeth S Burnside; Edward A Sickles; Lawrence W Bassett; Daniel L Rubin; Carol H Lee; Debra M Ikeda; Ellen B Mendelson; Pamela A Wilcox; Priscilla F Butler; Carl J D'Orsi
Journal:  J Am Coll Radiol       Date:  2009-12       Impact factor: 5.532

7.  International variation in screening mammography interpretations in community-based programs.

Authors:  Joann G Elmore; Connie Y Nakano; Thomas D Koepsell; Laurel M Desnick; Carl J D'Orsi; David F Ransohoff
Journal:  J Natl Cancer Inst       Date:  2003-09-17       Impact factor: 13.506

  7 in total

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