Classification using the cumulative log-odds in the quantitative pathologic diagnosis of adenocarcinoma of the cervix

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.

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.

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%.

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.