MKI67 and glioma: We assessed the importance of visual features, sub-visual features and Ki-67 separately, and the highest predictive power was achieved by visual features, achieving an accuracy of 0.76, which indicates that the histomorphology of glioma, such as nuclear morphological features and nuclear staining features and patterns play an important role in glioma grading.