A Chemometric Approach for Brain Tumor Classification Using Magnetic Resonance Imaging and Spectroscopy

Simonetti, Arjan W.; Melssen, W.J.; Graaf, Marinette van der; Postma, G.J.; Heerschap, A.; Buydens, L.M.C.

doi:10.1021/ac034541t

Cited by 68 publications

(88 citation statements)

References 29 publications

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“…This was performed by averaging the image pixels which were covered by each spectroscopic voxel. See Simonetti et al 9 for more details.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…9 These so-called distribution plots are important in the assessment of the input data. For classification, the Mahalanobis distance was used as the criterion.…”

Section: Classificationmentioning

confidence: 99%

“…The interpretation of this data is difficult and subjective and therefore classification based on pattern recognition is under development. [5][6][7][8][9][10] At the moment, most MR-related strategies to obtain information about brain tumors use either MRI or MRSI. A combination of both data sources has not been extensively investigated yet.…”

Section: Introductionmentioning

confidence: 99%

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Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

et al. 2005

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The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.

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“…This was performed by averaging the image pixels which were covered by each spectroscopic voxel. See Simonetti et al 9 for more details.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…9 These so-called distribution plots are important in the assessment of the input data. For classification, the Mahalanobis distance was used as the criterion.…”

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

et al. 2005

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“…This study complements previous work in using combined MRI and spectroscopy features in classifying brain tumors [13][14][15][16]. We have chosen to apply similar methods of supervised pattern recognition and computer-aided diagnosis to study a very specific question in glioma imaging, thereby relating machine learning techniques with image segmentation and clinical prediction.…”

Section: Discussionmentioning

confidence: 89%

“…However, there has been somewhat less work on combining imaging data with spectroscopy. When this combination has been performed, the accuracy of the classification has been shown to be superior to using images or spectroscopy alone [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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

Lee¹,

Nelson²

2008

Artificial Intelligence in Medicine

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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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Pattern Recognition Analysis of MR Spectra

Ortega‐Martorell

Julià‐Sapé

Lisboa

et al. 2016

eMagRes

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The need for multivariate analysis of magnetic resonance spectroscopy (MRS) data was recognized about 20 years ago, when it became evident that spectral patterns were characteristic of some diseases. Despite this, there is no generally accepted methodology for performing pattern recognition (PR) analysis of MRS data sets. Here, the data acquisition and processing requirements for performing successful PR as applied to human MRS studies are introduced, and the main techniques for feature selection, extraction, and classification are described. These include methods of dimensionality reduction such as principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), and feature selection. Supervised methods such as linear discriminant analysis (LDA), logistic regression (LogR), and nonlinear classification are discussed separately from unsupervised and semisupervised classification techniques, including k -means clustering. Methods for testing and metrics for gauging the performance of PR models (sensitivity and specificity, the 'Confusion Matrix', 'k -fold cross-validation', 'Leave One Out', 'Bootstrapping', the 'Receiver Operating Characteristic curve', and balanced error and accuracy rates) are briefly described. This article ends with a summary of the main lessons learned from PR applied to MRS to date.

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A Chemometric Approach for Brain Tumor Classification Using Magnetic Resonance Imaging and Spectroscopy

Cited by 68 publications

References 29 publications

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

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

Pattern Recognition Analysis of MR Spectra

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