부산대학교 도서관

Abstract: Introduction.: This study develops a method that discriminates between normal and cancerous tissue sections (i.e., populations of cells) using a statistical model applied to high-dimensional quantitative measurements made on a sample of cells. Materials and methods.: We use a cumulative log-odds model to create a score for a tissue section using the information from the cells within that tissue section. Then, a threshold is determined using receiver operating characteristic (ROC) curve analysis. The method was tested using data from cervical adenocarcinomas, adenocarcinoma in situ, and normal columnar tissue. Results.: Using 120 potential features, we analyzed the data for staining-independent features. Twenty-two features were statistically significant. We then calculated the log-odds and created a score, followed by ROC curve analysis. The operating point which maximizes the sum of the specificity and sensitivity achieved a sensitivity of 100% with a specificity of 85%. Conclusion.: The cumulative log-odds performs well in classifying tissue sections using high-dimensional data measured at the cellular level, like that of quantitative pathology. This methodology potentially has applications in pathology, radiology, and optical technologies. [Copyright &y& Elsevier]