Abstract

The goal of the proposed research is to develop computer-aided diagnosis (CAD) schemes to assist radiologists in diagnosing breast cancer from mammograms. The hypothesis of this project is that CAD will improve radiologists' ability to interpret mammograms, so that both the number of missed cancers and the number of women unnecessarily sent to biopsy can be reduced. This proposal is specifically for the development of CAD schemes for the detection and the classification (benign versus malignant) of microcalcifications from digital mammograms. This project will continue to refine the current CAD schemes, towards our goal of clinically viable CAD schemes.
The specific aims of this proposal are: (1) To improve the performance of our detection scheme by detecting subtle clusters based on their brightest microcalcifications; by decreasing the number of false positives by eliminating those caused by calcified vessels or obvious benign calcifications; and by improving the robustness of the scheme; (2) To study the effect of scoring methodology on the measured performance of a detection scheme and then to devise the """"""""best"""""""" scoring method for clustered microcalcifications; (3) To improve the performance of the classification scheme: by enlarging our database of benign and malignant cases; by identifying additional computer-extracted features; and by utilizing information from multiple sources: magnification views, two views of the same breast, and the same views of the same breast taken at different times; (4) To combine the detection and classification schemes, thereby fully automating the classification scheme. This will entail examining the effects on the classification scheme of incomplete detection of all the microcalcifications within a cluster, of false positive signals being included in a true cluster, and of false positive clusters. Using this information, the applicants proposed to develop an interface between the detection and classification schemes; and (5) To perform a preliminary clinical evaluation of the classification scheme.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
2R01CA060187-05
Application #
2467277
Study Section
Special Emphasis Panel (ZRG7-DMG (01))
Program Officer
Torres-Anjel, Manuel J
Project Start
1994-01-01
Project End
2002-12-31
Budget Start
1998-01-12
Budget End
1998-12-31
Support Year
5
Fiscal Year
1998
Total Cost
Indirect Cost

  Institution

Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
225410919
City
Chicago
State
IL
Country
United States
Zip Code
60637

  Publications

Jiang, Yulei; Nishikawa, Robert M; Schmidt, Robert A et al. (2006) Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications. Acad Radiol 13:84-94
Edwards, Darrin C; Metz, Charles E; Nishikawa, Robert M (2005) The hypervolume under the ROC hypersurface of ""near-guessing"" and ""near-perfect"" observers in N-class classification tasks. IEEE Trans Med Imaging 24:293-9
Edwards, Darrin C; Lan, Li; Metz, Charles E et al. (2004) Estimating three-class ideal observer decision variables for computerized detection and classification of mammographic mass lesions. Med Phys 31:81-90
Salfity, Maria F; Nishikawa, Robert M; Jiang, Yulei et al. (2003) The use of a priori information in the detection of mammographic microcalcifications to improve their classification. Med Phys 30:823-31
Edwards, Darrin C; Kupinski, Matthew A; Metz, Charles E et al. (2002) Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model. Med Phys 29:2861-70
El-Naqa, Issam; Yang, Yongyi; Wernick, Miles N et al. (2002) A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging 21:1552-63
Jiang, Y; Nishikawa, R M; Schmidt, R A et al. (2001) Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications. Radiology 220:787-94
Jiang, Y; Nishikawa, R M; Papaioannou, J (2001) Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications. Med Phys 28:1949-57
Doi, K; MacMahon, H; Katsuragawa, S et al. (1999) Computer-aided diagnosis in radiology: potential and pitfalls. Eur J Radiol 31:97-109
Jiang, Y; Nishikawa, R M; Schmidt, R A et al. (1999) Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol 6:22-33

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