Clinical Decision Support System (CDSS) for the Classification of Atypical Cells in Pleural Effusions

Abstract

The objective of this research is to develop a prototype Clinical Decision Support System (CDSS) to aid pathologists in correctly discriminating between reactive mesothelial cells and malignant epithelial cells. Currently, there is great difficulty in visually discriminating between cells that are malignant and cells that are otherwise reactive to antigens present in the effusion. Features have been identified, which can correctly discriminate between benign epithelial cells and malignant epithelial cells with a validation AZ accuracy of ∼ 0.934, training AZ of ∼ 0.937. Using these features, the system trained on visually known cases was shown to find discriminating information in the feature subset of the atypical cases by examining probabilities generated from subjecting the system to atypical cells. While these results are preliminary, they do demonstrate that an intelligent CDSS, which has the potential to discriminate between reactive mesothelial cells and malignant epithelial cells, designed using newly developed and/or revised statistical learning theory (SLT) algorithms, has the potential to be used as a second opinion diagnostic aid by physicians, as they deem appropriate.