Simulation model for gynecologic specimen classification in a high-resolution prescreening system.
Presentation is made of the design of a statistical model for the generation of "artificial specimens" to be used in the development and testing of a high-resolution prescreening system for gynecologic specimen classification. The model is based on two considerations: (1) the nature of the biologic material to be examined and (2) the system to be studied, which in this case is the FAZYTAN cervical prescreening system. Since gynecologic specimens that belong to the same clinical class (Papanicolaou group) have similar compositions of the different cytologic cell types, the simulation model presented is based on the close relationship between the degree of cancer suspiciousness expressed in the clinical diagnostic group and the composition of the cellular samples on a specimen. Statistically, the model considered here is based on an analysis of the single-cell classification (SCC) output process, taking the inherent system properties into account. The statistical information obtained by evaluating large sets of labelled cells is then used to produce artificially generated point distributions in the SCC decision space ("artificial specimens"), which can be used for examination of system reactions under controlled conditions. False-positive and false-negative error rates and system operation characteristics can be measured, and the effects of varying cell compositions as well as the relative performance of different specimen classifiers can be investigated. Although the "artificial specimens" thus created allow the investigation of system reactions with respect to a great variety of input processes, they cannot replace experiments on thousands of original specimens in order to measure system quality under realistic conditions.
D003584:Cytological Techniques; D006176:Gynecology / Q000379:methods*; D008403:Mass Screening / Q000379:methods*; D008954:Models, Biological; D010338:Pathology, Clinical / Q000379:methods; D013223:Statistics as Topic