Literature DB >> 16399036

Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications.

Yulei Jiang1, Robert M Nishikawa, Robert A Schmidt, Charles E Metz.   

Abstract

RATIONALE AND
OBJECTIVES: The aim of the study is to compare independent double readings by radiologists and computer-aided diagnosis (CAD) in diagnostic interpretation of mammographic calcifications.
MATERIALS AND METHODS: Ten radiologists independently interpreted 104 mammograms containing clustered microcalcifications. Forty-six of these were malignant and 58 were benign at biopsy. Radiologists read the images with and without a computer aid by using a counterbalanced study design. Sensitivity and specificity were calculated from observer biopsy recommendations, and receiver operating characteristic (ROC) curves were computed from their diagnostic confidence ratings. Unaided double-reading sensitivity and specificity values were derived post hoc by using three different objective rules and an additional rule of simulated-optimal double reading that assumed that consultations for resolving two radiologists' different independent diagnoses always produce the correct clinical recommendation. ROC curves of unaided double readings were obtained according to the literature.
RESULTS: Single reading without computer aid yielded 74% sensitivity and 32% specificity, whereas CAD reading yielded 87% sensitivity and 42% specificity and appeared on a higher ROC curve (P < .0001). Three methods of formulating independent double readings generated sensitivities between 59% and 89%, specificities between 50% and 13%, and operating points that moved essentially along the average unaided single-reading ROC curve. ROC curves of unaided independent double readings showed small, statistically insignificant improvement over those of unaided single readings. Results of the simulated-optimal double reading were similar to CAD: 89% sensitivity and 50% specificity.
CONCLUSION: Independent double readings of mammographic calcifications may not improve diagnostic performance. CAD reading improves diagnostic performance to an extent approaching the maximum possible performance.

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Mesh:

Year:  2006        PMID: 16399036     DOI: 10.1016/j.acra.2005.09.086

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


  13 in total

1.  Using breast radiographers' reports as a second opinion for radiologists' readings of microcalcifications in digital mammography.

Authors:  R Tanaka; M Takamori; Y Uchiyama; R M Nishikawa; J Shiraishi
Journal:  Br J Radiol       Date:  2014-12-23       Impact factor: 3.039

2.  The wisdom of crowds for visual search.

Authors:  Mordechai Z Juni; Miguel P Eckstein
Journal:  Proc Natl Acad Sci U S A       Date:  2017-05-10       Impact factor: 11.205

3.  Computer-aided diagnostic models in breast cancer screening.

Authors:  Turgay Ayer; Mehmet Us Ayvaci; Ze Xiu Liu; Oguzhan Alagoz; Elizabeth S Burnside
Journal:  Imaging Med       Date:  2010-06-01

4.  Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration.

Authors:  Turgay Ayer; Oguzhan Alagoz; Jagpreet Chhatwal; Jude W Shavlik; Charles E Kahn; Elizabeth S Burnside
Journal:  Cancer       Date:  2010-07-15       Impact factor: 6.860

5.  Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms.

Authors:  Rich S Rana; Yulei Jiang; Robert A Schmidt; Robert M Nishikawa; Bei Liu
Journal:  Acad Radiol       Date:  2007-03       Impact factor: 3.173

6.  Automated breast image classification using features from its discrete cosine transform.

Authors:  Edward J Kendall; Matthew T Flynn
Journal:  PLoS One       Date:  2014-03-14       Impact factor: 3.240

7.  Automatic detection of anomalies in screening mammograms.

Authors:  Edward J Kendall; Michael G Barnett; Krista Chytyk-Praznik
Journal:  BMC Med Imaging       Date:  2013-12-13       Impact factor: 1.930

8.  Exploration of analysis methods for diagnostic imaging tests: problems with ROC AUC and confidence scores in CT colonography.

Authors:  Susan Mallett; Steve Halligan; Gary S Collins; Doug G Altman
Journal:  PLoS One       Date:  2014-10-29       Impact factor: 3.240

9.  CAD May Not be Necessary for Microcalcifications in the Digital era, CAD May Benefit Radiologists for Masses.

Authors:  Stamatia V Destounis; Andrea L Arieno; Renee C Morgan
Journal:  J Clin Imaging Sci       Date:  2012-07-28

10.  Artificial neural networks in mammography interpretation and diagnostic decision making.

Authors:  Turgay Ayer; Qiushi Chen; Elizabeth S Burnside
Journal:  Comput Math Methods Med       Date:  2013-05-26       Impact factor: 2.238

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