The overall aim of this project is to improve automated cervical smear image analysis techniques sufficiently to: 1) routinely provide additional diagnostic information to supplement that provided by human visual examination, and 2) provide accurate, cost effective prescreening capabilities. By furnishing additional quantitative diagnostic information, such automated systems would permit the practicing cytopathologist to make more accurate and refined diagnostic judgements, which would lead to improved patient management. By providing a practical prescreening capability, such systems would increase the efficiency and efficacy of health care delivery. Current automated cervical smear analysis research employs only the analysis of isolated cells, and practical automated analysis is not yet a reality. The primary contribution of this project will be the completion of development and testing of image analysis techniques that extract information from cells and other objects as seen in the context of the """"""""background"""""""" of the smear. Specifically, cells, cell clusters, bare nuclei, and cytoplasmic fragments are analyzed and the resulting contextual features, slide-averaged """"""""features"""""""" and high-resolution features describing single cells are combined to produce a more complete and accurate description of the smear. This description will provide information not obtainable by human visual analysis, which can be used, 1) directly, to ascertain diagnostic clues, and 2) indirectly to make accurate prescreening possible. An extensive pilot study has shown that the contextual analysis provides complementary information to that provided by single cell analysis. That is, where single cell analysis seems to have difficulty, contextual analysis is most accurate - and vice versa. Thus we expect to demonstrate that the combination of our contextual analysis techniques with single cell analysis will give automated Pap smear analysis the additional screening accuracy that is needed. The proposed project is a followup intended to: a) validate the techniques of the pilot study, b) to expand the study to include parameters that provide additional diagnostic and prognostic information, and c) to generate specifications for a system to accomplish the analysis in a routine clinical laboratory environment.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA043133-03
Application #
3185092
Study Section
(SSS)
Project Start
1987-06-01
Project End
1991-05-31
Budget Start
1989-06-01
Budget End
1991-05-31
Support Year
3
Fiscal Year
1989
Total Cost
Indirect Cost
Name
Tufts University
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02111
Isenstein, L M; Zahniser, D J; Hutchinson, M L (1995) Combined malignancy associated change and contextual analysis for computerized classification of cervical cell monolayers. Anal Cell Pathol 9:83-93
Hutchinson, M L; Isenstein, L M; Goodman, A et al. (1994) Homogeneous sampling accounts for the increased diagnostic accuracy using the ThinPrep Processor. Am J Clin Pathol 101:215-9
Hutchinson, M L; Isenstein, L M; Martin, J J et al. (1992) Measurement of subvisual changes in cervical squamous metaplastic cells for detecting abnormality. Anal Quant Cytol Histol 14:330-4
Zahniser, D J; Wong, K L; Brenner, J F et al. (1991) Contextual analysis and intermediate cell markers enhance high-resolution cell image analysis for automated cervical smear diagnosis. Cytometry 12:10-4
Hutchinson, M L; Cassin, C M; Ball 3rd, H G (1991) The efficacy of an automated preparation device for cervical cytology. Am J Clin Pathol 96:300-5
Hutchinson, M; Fertitta, L; Goldbaum, B et al. (1991) Cervex-Brush and Cytobrush. Comparison of their ability to sample abnormal cells for cervical smears. J Reprod Med 36:581-6