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Oligodendrogliomas are characterized by 1p/19q co-deletion, which generally correlates with subjective morphologic features like nuclei circularity ratio and texture features of the cancer nuclei. As cost control becomes more of an issue in medicine, upfront reflex molecular diagnostic testing for lesions like 1p/19q co-deletion may not be appropriate. This paper aims to develop a rigorous, unbiased digital imaging segmentation algorithm and statistical models that can accurately predict the likelihood of 1p/19q co-deletion based on morphology and texture, which would greatly improve cost-effectiveness in clinic trail. In this study, totally 28 gliomas of haematoxylin and eosin stained slides are comprised in this test cohort. Selected areas that had high tumour cell density were digitally analysed with a high-throughput image segmentation algorithm to automatically delineate the boundaries of the cell nuclei. Then we extracted the morphologic features and texture features based on the segmentation result, and applied them in to Lasso-logistic regression to build the correlation between these features with 1p/19q co-deletion status. As a comparison, we also used PAM (Prediction Analysis of Microarrays), RPA (Recursive Partitioning Analysis) to compare the predication performance. We find out that the circularity ratio of the cell, the variance of cell area for each patient, and parts of texture features effect the 1p/19q co-deletion status, and the false prediction rate of leave one out cross validation is at most around 10%. Moreover, we conduct survival analysis and find out two morphologic features and one texture features are significant influential to patients’ survival time.

Quantitative Measurement of Oligodendrogliomas Histologica Features in Predicting the 1p/19q Co-Deletion Status