Radiology
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Baker, J. A.
Right arrow Articles by Floyd, C. E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Baker, J. A.
Right arrow Articles by Floyd, C. E., Jr

Radiology, Vol 196, 817-822, Copyright © 1995 by Radiological Society of North America

ARTICLES

Breast cancer: prediction with artificial neural network based on BI- RADS standardized lexicon

JA Baker, PJ Kornguth, JY Lo, ME Williford and CE Floyd Jr
Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.

PURPOSE: To determine if an artificial neural network (ANN) to categorize benign and malignant breast lesions can be standardized for use by all radiologists. MATERIALS AND METHODS: An ANN was constructed based on the standardized lexicon of the Breast Imaging Recording and Data System (BI-RADS) of the American College of Radiology. Eighteen inputs to the network included 10 BI-RADS lesion descriptors and eight input values from the patient's medical history. The network was trained and tested on 206 cases (133 benign, 73 malignant cases). Receiver operating characteristic curves for the network and radiologists were compared. RESULTS: At a specified output threshold, the ANN would have improved the positive predictive value (PPV) of biopsy from 35% to 61% with a relative sensitivity of 100%. At a fixed sensitivity of 95%, the specificity of the ANN (62%) was significantly greater than the specificity of radiologists (30%) (P < .01). CONCLUSION: The BI-RADS lexicon provides a standardized language between mammographers and an ANN that can improve the PPV of breast biopsy.


This article has been cited by other articles:


Home page
 RadiologyHome page
J. L. Jesneck, J. Y. Lo, and J. A. Baker
Breast Mass Lesions: Computer-aided Diagnosis Models with Mammographic and Sonographic Descriptors
Radiology, August 1, 2007; 244(2): 390 - 398.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Roentgenol.Home page
R. J. Brenner, M. J. Ulissey, and R. M. Wilt
Computer-Aided Detection as Evidence in the Courtroom: Potential Implications of an Appellate Court's Ruling
Am. J. Roentgenol., January 1, 2006; 186(1): 48 - 51.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Roentgenol.Home page
A. S. Hong, E. L. Rosen, M. S. Soo, and J. A. Baker
BI-RADS for Sonography: Positive and Negative Predictive Values of Sonographic Features
Am. J. Roentgenol., April 1, 2005; 184(4): 1260 - 1265.
[Abstract] [Full Text] [PDF]

Home page
 RadiologyHome page
W. A. Berg, C. J. D'Orsi, V. P. Jackson, L. W. Bassett, C. A. Beam, R. S. Lewis, and P. E. Crewson
Does Training in the Breast Imaging Reporting and Data System (BI-RADS) Improve Biopsy Recommendations or Feature Analysis Agreement with Experienced Breast Imagers at Mammography?
Radiology, September 1, 2002; 224(3): 871 - 880.
[Abstract] [Full Text] [PDF]

Home page
 RadiologyHome page
M. K. Markey, J. Y. Lo, and C. E. Floyd Jr
Differences between Computer-aided Diagnosis of Breast Masses and That of Calcifications
Radiology, March 21, 2002; (2002) 2232011257.
[Abstract] [Full Text]

Home page
 Am. J. Roentgenol.Home page
J. Y. Lo, M. K. Markey, J. A. Baker, and C. E. Floyd Jr.
Cross-Institutional Evaluation of BI-RADS Predictive Model for Mammographic Diagnosis of Breast Cancer
Am. J. Roentgenol., February 1, 2002; 178(2): 457 - 463.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Roentgenol.Home page
C. E. Floyd Jr., J. Y. Lo, and G. D. Tourassi
Case-Based Reasoning Computer Algorithm that Uses Mammographic Findings for Breast Biopsy Decisions
Am. J. Roentgenol., November 1, 2000; 175(5): 1347 - 1352.
[Abstract] [Full Text] [PDF]

Home page
 Arch SurgHome page
R.-F. Chang, W.-J. Kuo, D.-R. Chen, Y.-L. Huang, J.-H. Lee, and Y.-H. Chou
Computer-Aided Diagnosis for Surgical Office-Based Breast Ultrasound
Arch Surg, June 1, 2000; 135(6): 696 - 699.
[Abstract] [Full Text] [PDF]

Home page
 RadiologyHome page
E. I. Bluth, J. H. Sunshine, J. B. Lyons, C. A. Beam, L. A. Troxclair, L. Althans-Kopecky, P. E. Crewson, M. A. Sullivan, D. H. Smetherman, P. A. Heidenreich, et al.
Power Doppler Imaging: Initial Evaluation as a Screening Examination for Carotid Artery Stenosis
Radiology, June 1, 2000; 215(3): 791 - 800.
[Abstract] [Full Text]

Home page
 Am. J. Roentgenol.Home page
W. A. Berg, C. Campassi, P. Langenberg, and M. J. Sexton
Breast Imaging Reporting and Data System: Inter- and Intraobserver Variability in Feature Analysis and Final Assessment
Am. J. Roentgenol., June 1, 2000; 174(6): 1769 - 1777.
[Abstract] [Full Text]

Home page
 Med Decis MakingHome page
G. D. Tourassi and C. E. Floyd
The Effect of Data Sampling on the Performance Evaluation of Artificial Neural Networks in Medical Diagnosis
Med Decis Making, April 1, 1997; 17(2): 186 - 192.
[Abstract] [PDF]

Home page
 RadiologyHome page
M. K. Markey, J. Y. Lo, and C. E. Floyd Jr
Differences between Computer-aided Diagnosis of Breast Masses and That of Calcifications
Radiology, May 1, 2002; 223(2): 489 - 493.
[Abstract] [Full Text] [PDF]


HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
RADIOLOGY RADIOGRAPHICS RSNA JOURNALS ONLINE
Copyright © 1995 by the Radiological Society of North America.