Machine learning methods for MRI biomarkers analysis of pediatric posterior fossa tumors

We present a machine learning based magnetic resonance imaging biomarkers analysis framework for two kinds of pediatric posterior fossa tumors. In details, three feature extraction methods are used to obtain 300 imaging biomarkers. 10 feature selection methods and 11 classifiers are evaluated by the quantified predictive performance and stability, and importance consistency of features and the influence of the experimental factors are also analyzed. Our results demonstrate that the CFS feature selection method (accuracy: 83.85 ± 5.51%, stability: [0.84, 0.06]) and SVM classifier (accuracy: 85.38 ± 3.47%, RSD: 4.77%) show relatively better performance than others and should be preferred. Among all the biomarkers, 17 texture features seem to be more important. Multifactor analysis results indicate the choice of classifier accounts for the most contribution to the variability in performance (37.25%). The machine learning based framework is efficient for pediatric posterior fossa tumors biomarkers analysis, and could provide valuable references and decision support for assisted clinical diagnosis.
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research