Use of nuclear morphometry characteristics to distinguish between normal and abnormal cervical glandular histologies.
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.
D000230:Adenocarcinoma / Q000473:pathology*; D000465:Algorithms; D002467:Cell Nucleus / Q000473:pathology*; D002584:Cervix Uteri / Q000473:pathology*; D003627:Data Interpretation, Statistical; D003937:Diagnosis, Differential; D003951:Diagnostic Errors / Q000517:prevention & control*; D005260:Female; D006801:Humans; D019044:Image Cytometry / Q000379:methods* / Q000592:standards; D011237:Predictive Value of Tests; D012372:ROC Curve; D012016:Reference Values; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012984:Software / Q000592:standards; D002583:Uterine Cervical Neoplasms / Q000473:pathology*