A hierarchical decision tree classification scheme for brain tumour astrocytoma grading using support vector machines

The use of the concepts of support vector machines (SVMs) and decision tree (DT) classification as a possible methodology for the characterization of the degree of malignancy of brain tumours astrocytomas (ASTs) is proposed in this paper. A two-level hierarchical DT model was constructed for the discrimination of 87 ASTs in accordance to the WHO grading system. The first level concerned the detection of low versus high-grade tumours and the second level the detection of less aggressive as opposed to highly aggressive tumours. The decision rule at each level was based on a SVM classification methodology comprising 3 steps: i) From each biopsy, images were digitized and segmented to isolate nuclei from surrounding tissue. ii) Descriptive quantitative variables related to chromatin distribution and DNA content were generated to encode the degree of tumour malignancy. iii) Exhaustive search was performed to determine best feature combination that led to the smallest classification error. SVM classifier training was based on the leave-one-out method. Finally, SVMs were comparatively evaluated with the Bayesian classifier and the probabilistic neural network. The SVM classifier discriminated low from high-grade tumours with an accuracy of 90.8% and less from highly aggressive tumours with 85.6%. The proposed decision tree classification scheme based on SVMs and the analysis of quantitative nuclear features provide means to reduce subjectivity in grading brain tumors.