Reports Outline Support Vector Machines Findings from Nottingham University Hospital (Metrics and Textural Features of MRI Diffusion to Improve Classification of Pediatric Posterior Fossa Tumors)
By a News Reporter-Staff News Editor at Robotics & Machine Learning -- Investigators publish new report on Support Vector Machines. According to news reporting originating in Nottingham, United Kingdom, by VerticalNews journalists, research stated, "Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features."
The news reporters obtained a quote from the research from Nottingham University Hospital, "This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. Support vector machine-based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination."
According to the news reporters, the research concluded: "These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology."
For more information on this research see: Metrics and Textural Features of MRI Diffusion to Improve Classification of Pediatric Posterior Fossa Tumors. American Journal of Neuroradiology, 2014;35(5):1009-1015. American Journal of Neuroradiology can be contacted at: Amer Soc Neuroradiology, PO Box 3000, Denville, NJ 07834-9349, USA.
Our news correspondents report that additional information may be obtained by contacting D.R. Gutierrez, Nottingham Univ Hosp Trust, Nottingham, United Kingdom. Additional authors for this research include A. Awwad, L. Meijer, M. Manita, T. Jaspan, R.A. Dineen, R.G. Grundy and D.P. Auer.
Keywords for this news article include: Europe, Nottingham, Pediatrics, United Kingdom, Machine Learning, Emerging Technologies, Support Vector Machines
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