Use of nuclear morphometry characteristics to distinguish between normal and abnormal cervical glandular histologies.

Richard Swartz, Loyd West, Iouri Boiko, Anais Malpica, Calum MacAulay, Anita Carraro, Martial Guillaud, Dennis Cox, Michele Follen
Author Information
  1. Richard Swartz: Department of Statistics, Rice University, P.O. Box 1892, Houston, TX 77005, USA.

Abstract

This is a methodological study exploring the use of quantitative histopathology applied to the cervix to discriminate between normal and cancerous (consisting of adenocarcinoma and adenocarcinoma in situ) tissue samples. The goal is classifying tissue samples, which are populations of cells, from measurements on the cells. Our method uses one particular feature, the IODs-Index, to create a tissue level feature. The specific goal of this study is to find a threshold for the IODs-Index that is used to create the tissue level feature. The main statistical tool is Receiver Operating Characteristic (ROC) curve analysis. When applied to the data, our method achieved promising results with good estimated sensitivity and specificity for our data set. The optimal threshold for the IODs-Index was found to be 2.12.

Grants

  1. 2P01 CA82710/NCI NIH HHS
  2. R25 CA57730/NCI NIH HHS

MeSH Term

Adenocarcinoma
Algorithms
Cell Nucleus
Cervix Uteri
Data Interpretation, Statistical
Diagnosis, Differential
Diagnostic Errors
Female
Humans
Image Cytometry
Predictive Value of Tests
ROC Curve
Reference Values
Reproducibility of Results
Retrospective Studies
Software
Uterine Cervical Neoplasms

Word Cloud

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