A statistical analysis of prescreening alarms in a population of normal and abnormal gynecologic specimens.

Abstract

A multidimensional slit-scan flow system has been developed to serve as an automated prescreening instrument for gynecological cytology. Specimens are classified abnormal based on the number of cells having elevated nuclear fluorescence (alarms). An alarm region in a bivariate histogram of nuclear fluorescence versus nuclear-to-cell-diameter ratio is defined. Alarm region probability arrays are calculated to estimate the probability that an alarm falling in a particular bin of the alarm region is either from a normal or an abnormal specimen. From these arrays, a weighted alarm index is generated. In addition, summary indices are derived that measure how the distribution of alarms in each specimen compares with the average distributions for the normal and abnormal specimen populations. These indices together with current features are evaluated with respect to their utility in specimen classification using a nonparametric classification technique known as recursive partitioning. Resulting classification trees are presented that suggest information in the distribution of alarms in the bivariate histogram. In addition, they validate the features and rules currently used for specimen classification. Recursive partitioning appears to be useful for multivariate classification and is seen as a promising technique for other applications.