We propose to evaluate state-of-the-art NMR imaging for breast cancer detection. Low dose-film/screen mammography and NMR imaging will be compared in a clinical setting to determine the optimal role of NMR. We are particularly interested in the usefulness of NMR in evaluating cases of indeterminate mammography. The work will be accomplished at Washington University School of Medicine. Approximately 200 women will receive NMR evaluations over a two year period. Women will be randomly selected from 9 strate for participation. The sampling population will consist of approximately 3000 woman referred to our Mallinckrodt Institute of Radiology for mammography during the first 18 months of the project. All women referred will receive breast palpation and mammorgraphy. Informed consent will be required for NMR evaluation. Siemens Mammomat (mammography) and Siemens Magnetom NMR systems at Mallinckrodt will be used. For evaluation purposes images will be interpreted independently for each modality by different radiologists. Radiologists and pathologists will collaborate in the tissue classification efforts. Collaboration with NASA Kennedy Space Center image processing engineers will result in modifications and transfer of exceptional spectral analysis (tissue classification) software to our NMR imager early in the project. The new software will facilitate interpretation and increase NMR sensitivity and specificity. Pilot studies with NASA have already begun.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA037072-03
Application #
3174742
Study Section
Diagnostic Radiology Study Section (RNM)
Project Start
1984-04-01
Project End
1988-12-31
Budget Start
1986-07-01
Budget End
1988-12-31
Support Year
3
Fiscal Year
1986
Total Cost
Indirect Cost
Name
Washington University
Department
Type
Schools of Medicine
DUNS #
062761671
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Adams, A H; Brookeman, J R; Merickel, M B (1991) Breast lesion discrimination using statistical analysis and shape measures on magnetic resonance imagery. Comput Med Imaging Graph 15:339-49
Gohagan, J K; Spitznagel, E L; Murphy, W A et al. (1987) Multispectral analysis of MR images of the breast. Radiology 163:703-7