While screening mammography has been shown to be an effective method for the early detection of breast cancer, currently, 5-30 percent of women with breast cancer have a marnmogram that is interpreted as normal. It has been reported that interpretation errors (when the radiologist sees the cancer but reports it as benign) are the cause of 54 percent of missed cancers. In addition, only 10-40 percent of women who have a biopsy actually have breast cancer; with biopsies being expensive, invasive and traumatic to the patient. In addition, there is large inter-observer variability in the interpretation of mammographic lesions. The long-term goal of this research is to develop and evaluate computer-aided diagnosis and prognosis methods for multi-modality imaging of the breast. The main hypotheses to be tested are that, combined information from the computerized analysis of mammography, breast ultrasound, and MR images, along with clinical data, should yield improved methods for (a) distinguishing between malignant and benign lesions, i.e., diagnosis and (b) predicting prognosis. The objectives are to create databases containing mammogram, ultrasound, and MR images along with clinical information, malignant/benign status, and patient outcomes; to develop computerized methods for characterizing the essential morphological, textural, sonographic, and vascular features of the lesions; and to evaluate the accuracy of these methods in distinguishing between malignant and benign lesions and in predicting patient prognosis. It is expected that the results from this research will aid radiologists/oncologists in determining the likelihood of malignancy and in predicting patient prognosis. The proposed work is novel in that such a comprehensive system for computer-aided diagnosis has not yet been attempted. We believe that with the combined information from multimodality imaging and clinical information, overall patient outcome will improve.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA089452-02
Application #
6514858
Study Section
Diagnostic Imaging Study Section (DMG)
Program Officer
Liu, Guoying
Project Start
2001-04-24
Project End
2006-03-31
Budget Start
2002-04-01
Budget End
2003-03-31
Support Year
2
Fiscal Year
2002
Total Cost
$311,428
Indirect Cost
Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
225410919
City
Chicago
State
IL
Country
United States
Zip Code
60637
Li, Hui; Giger, Maryellen L; Sun, Chang et al. (2014) Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys 41:031917
Reiser, I; Nishikawa, R M; Giger, M L et al. (2012) Automated detection of mass lesions in dedicated breast CT: a preliminary study. Med Phys 39:866-73
Drukker, Karen; Pesce, Lorenzo; Giger, Maryellen (2010) Repeatability in computer-aided diagnosis: application to breast cancer diagnosis on sonography. Med Phys 37:2659-69
Chen, Weijie; Metz, Charles E; Giger, Maryellen L et al. (2010) A novel hybrid linear/nonlinear classifier for two-class classification: theory, algorithm, and applications. IEEE Trans Med Imaging 29:428-41
Gruszauskas, Nicholas P; Drukker, Karen; Giger, Maryellen L et al. (2009) Breast US computer-aided diagnosis system: robustness across urban populations in South Korea and the United States. Radiology 253:661-71
Drukker, Karen; Sennett, Charlene A; Giger, Maryellen L (2009) Automated method for improving system performance of computer-aided diagnosis in breast ultrasound. IEEE Trans Med Imaging 28:122-8
Yuan, Yading; Giger, Maryellen L; Li, Hui et al. (2008) Correlative feature analysis on FFDM. Med Phys 35:5490-500
Li, Hui; Giger, Maryellen L; Olopade, Olufunmilayo I et al. (2008) Power spectral analysis of mammographic parenchymal patterns for breast cancer risk assessment. J Digit Imaging 21:145-52
Gruszauskas, Nicholas P; Drukker, Karen; Giger, Maryellen L et al. (2008) Performance of breast ultrasound computer-aided diagnosis: dependence on image selection. Acad Radiol 15:1234-45
Li, Hui; Giger, Maryellen L; Yuan, Yading et al. (2008) Evaluation of computer-aided diagnosis on a large clinical full-field digital mammographic dataset. Acad Radiol 15:1437-45

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