Supervised pattern recognition for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy

Abstract

Objective: The purpose of this study was to develop a pattern classification algorithm for use in predicting the location of new contrast-enhancement in brain tumor patients using data obtained via multivariate magnetic resonance imaging from a prior scan. We also explore the use of feature selection or weighting in improving the accuracy of the pattern classifier. Methods and materials:Contrast-enhanced MR images, perfusion images, diffusion images, and proton spectroscopic imaging data were obtained from 26 patients with glioblastoma multiforme brain tumors, divided into a design set and an unseen test set for verification of results. A k-NN algorithm was implemented to classify unknown data based on a set of training data with ground truth derived from post-treatment contrast enhanced images; the quality of the k-NN results was evaluated using a leave-one-out cross-validation method. A genetic algorithm was implemented to select optimal features and feature weights for the k-NN algorithm. The binary representation of the weights was varied from 1 to 4 bits. Each individual parameter was thresholded as a simple classification technique, and the results compared with the k-NN. Results:The feature selection k-NN was able to achieve a sensitivity of 0.78 ± 0.18 and specificity of 0.79 ± 0.06 on the holdout test data using only 7 of the 38 original features. Similar results were obtained with non-binary weights, but using a larger number of features. Overfitting was also observed in the higher bit representations. The best single-variable classifier, based on a choline-to-NAA abnormality index computed from spectroscopic data, achieved a sensitivity of 0.79 ± 0.20 and specificity of 0.71 ± 0.11. The k-NN results had lower variation across patients than the single-variable classifiers. Conclusions:We have demonstrated that the an optimized k-NN rule could be used for quantitative analysis of multivariate images, and be applied to a specific clinical research question. Selecting features was found to be useful in improving the accuracy of feature weighting algorithms and improving the comprehensibility of the results. We believe that in addition to

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“…On other hand, Lee and Nelson [55] used the CHC algorithm to found optimal feature weights of a pattern recognition technique for the prediction of contrast-enhancement appearance in brain tumors from multivariate magnetic resonance imaging and spectroscopy.…”

“…In this study, evolutionary and direct-search algorithms which had been successfully used in optimization problems in the biomedical field [50,51,52,53,54,55,56,57,58,59,60,61,62,63] were selected for their comparison. Simulations were performed fixing as stopping criterion a maximum number of evaluations of the analyzed cost functions.…”

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Multiparametric Magnetic Resonance Imaging of Brain Disorders

“…The HME was shown to improve prediction of tissue outcome compared to the GLM 58 . Non-parametric approaches such as k-nearest neighbors (kNN) algorithm have been proposed for combining multivariate data for both stroke 56, 59 and for tumor patient evaluation 60 . The kNN algorithm, which does not have a previous assumption regarding data distribution, classifies each new input vector according to the label of its k-nearest neighbors in the training data, in which distance is often measured with a Euclidean metric.…”

“…The kNN algorithm, which does not have a previous assumption regarding data distribution, classifies each new input vector according to the label of its k-nearest neighbors in the training data, in which distance is often measured with a Euclidean metric. For a human stroke study (N=14) 59 , the label was “infarcted” or “non-infarcted” tissue on follow-up imaging (more than 5-days from onset), while for the tumor study in grade IV tumor patients (N=26) 60 , the label was “enhanced” or “non-enhanced” tissue on post-radiotherapy imaging (within one month of conclusion of therapy) for tissue that was originally non-enhancing. The number of labels can be more than two, as was the case for the experimental stroke study for which the labels were normal, abnormal, or CSF 56 .…”