. Support vector machine analysis of flutemetamol scans. Human Amyloid Imaging 2011 Meeting Abstracts. 2011 Jan 15;

Abstract:

Background: Support vector machines (SVM), a supervised learning approach, can perform a strictly automated pattern classification into one of two categories based on whole-brain images. In a purely data-driven manner, the feature weights defined by the classifier provide information on which image components are most discriminative. Clinically, a classifier may be useful in case of uncertainty about image interpretation.

Methods: 27 early-stage AD subjects (25 with raised uptake, eflut-pos ADf), 15 elderly controls (14 with normal uptake, eflut-neg HVf), 10 young controls and 20 mild cognitive impairment (MCI) patients participated in the 18F-flutemetamol phase 2 study (Vandenberghe et al. Ann Neurol 2010). First, we used a leave-one-out procedure to evaluate the diagnostic performance of an SVM with a linear kernel on the spatially normalised flut-pos AD and flut-neg HV scans and also determined the distribution of the feature weights. The absolute distance of a subject to the separating hyperplane normalised to unit vector (edf) gives an indication of the classificationfs robustness Second, we evaluated performance of the algorithm in the 2 flut-neg AD subjects, 1 flut-pos HV and MCI. For comparison, we determined how a structural MRI-based SVM (grey matter segmentation maps) categorized scans from the 25 flut-pos AD and the 24 flut-neg HV.

Results: SVM classified all scans from flut-pos AD and from flut-neg HV correctly, with the highest absolute feature weights in precuneus and striatum (d median 87.5, range 59.7-212.0). Automatized SVM classification of the flut-neg AD, the flut-pos HV and the MCI was fully concordant with the visual reads. MRI-based classification had a sensitivity and specificity of 84% and 70.8% for categorizing scans from the 25 flut-pos AD and the 24 flut-neg HV.

Conclusions: Flutemetamol scans can be reliably categorized in a strictly automated manner using an SVM.

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