# IMPROVED CANCER DIAGNOSIS BY MORPHOMETRIC ANALYSIS

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 1987 · $273,945

## Abstract

Visually imperceptible nuclear abnormalities detectable by high resolution
image analysis, may serve as predictors of the ultimate outcome of disease
states.  The scope of this study is to demonstrate that the cells which we
call "moderate dysplasia" are in some way a pivotal point in the
progression of dysplasia.  As the majority of cases of cervical
intraepithelial neoplasia (CIN) do not progress to life-threatening
disease, the therapy of an individual could be based upon the predicted
outcome of the lesion, thus avoiding expensive and possibly unnecessary
operations, as well as undue psychologic trauma.

In constructing a data base of uterine-cervical epithelial cells for an
automated cytologic screening system, individual cells were classified
according to their disease related cytomorphologic characteristics,
independent of the histopathologic diagnosis of the tissue from which these
cells originated.  The cell class labeled "moderate dysplasia" demonstrated
significant differences among selected nuclear features, and to a lesser
extent, so did the cells classified as "severe dysplasia" and "invasive
carcinoma."  In contrast, those cells categorized as most probably
originating in a carcinoma in situ showed no significant differences
regardless of the tissue diagnosis.

In order to be sure that our preliminary data are not influenced by bias or
artifact, many more cases and much more in depth analysis is necessary.
Algorithms to more clearly define the differences among the nuclei need to
be developed.  Both of these goals, the collection of more data, and
development of algorithms, are the reason for application for further
funding.  If this most abundant abnormal cell type, the "moderate
dysplasia" cell, can be used as a predictor of ultimate outcome of disease,
an automated screening system can be made to function efficiently and
economically, as theoretically shown in our previous work.  Most
importantly, patient care and therapeutic regimens will be optimized, and
medical costs minimized.  (3)

## Key facts

- **NIH application ID:** 3175175
- **Project number:** 5R01CA037349-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **SAM.gov UEI:** RN64EPNH8JC6
- **Principal Investigator:** DOROTHY L ROSENTHAL
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 1987
- **Award amount:** $273,945
- **Award type:** 5
- **Project period:** 1985-01-01 → 1988-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/3175175

## Citation

> US National Institutes of Health, RePORTER application 3175175, IMPROVED CANCER DIAGNOSIS BY MORPHOMETRIC ANALYSIS (5R01CA037349-03). Retrieved via MORVS 2026-10-24 from https://api.morvs.ai/grant/nih/3175175. Licensed CC0.

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