Brain Tumour Classification by Machine Learning Applications with Selected Biological Features: Towards A Newer Diagnostic Regime

: Histopathologically classified low-grade brain tumours show overlapping biological characteristics making them difficult to distinguish. In the present study low-grade brain tumour patient samples of three different histopathological types have been trained through machine learning technique using selected features for its classification. We used specifically the fundamental proliferation, invasion, macrophage infiltration triangle of cancer hallmark with propidium iodide (PI) marked cell-cycle, Ki67 marked proliferative indexing, invasion with MMP2 expression and presence of macrophage/microglia by silver-gold staining, CD11b+ and Iba1+ cell presence as biological parameters. These parameters when trained with proper machine learning protocol through extraction of underling features and represented in a 2D perceivable space are found capable of distinguishing the tumour types. Extracted features from such parameters in a six-dimensional featured space were trained through statistical learning theory while support vector machine (SVM) maximizes their predictive precision. The leave one out (LOO) cross validation process was applied to judge the accuracy of training followed by auto-encoder (AE) to reduce feature dimension at two which is visually perceptible. From the biological features quantified with standard methods it was found impossible to demarcate the three types of low grade brain tumours. However, after training through SVM and LOO cross validation when the six- dimensional featured space had been reduced into two-dimension using AE, the combined output of the features showed clear zonation in that 2D space. This indicates that the overlapping biological characteristics of these tumour types, when trained through proper support vector machine and reduced from multiple to two dimensional space provides a clear patho-clinical classification edge using a combination of common biological features. Hence, machine learning applications may potentially be used as a complementary diagnostic protocol with the conventional practice.