Abstract
During the last decade, the Magnetic Resonance Spectroscopy modality has become an integrant part of the diagnostic routine. However, the visual interpretation of these spectra is difficult and few clinicians are trained to use the technique. In this study, sixty-eight spectra obtained from twenty-two multi-voxel spectroscopies were classified using three well-known classification algorithms: K-Nearest Neighbors (KNN), Decision Trees and Naïve Bayes. The best results were obtained using NaïveBayes that presented an average balanced accuracy rate around 75%, although K-Nearest Neighbors presented very good results in some situations. The obtained results lead us to conclude that it is possible to classify magnetic resonance spectra with data mining techniques for further integration in a Clinical Decision Support System which may help in the diagnosis of new cases.
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References
Bushberg, J.T., Seibert, J.A., Leidholdt, E.M., Boone, J.M.: The Essential Physics of Medical Imaging, 2nd edn. Lippincott Williams & Wilkins, Philadelphia (2002)
Sibtain, N.A., Howe, F.A., Saunders, D.E.: The clinical value of proton magnetic resonance spectroscopy in adult brain tumours. Clinical Radiology 62, 109–119 (2007)
Callot, V., et al.: 1H MR spectroscopy of human brain tumours: a practical approach. European Journal of Radiology 67, 268–274 (2008)
Tate, A.R., et al.: Automated Classification of Short Echo Time in In Vivo 1H Brain Tumor Spectra: A Multicenter Study. Magnetic Resonance in Medicine 49, 29–36 (2003)
Übeyli, E.D.: Comparison of different classification algorithms in clinical decision-making. Expert systems 24(1), 17–31 (2007)
Eom, J., Kim, S., Zhang, B.: AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction. Expert Systems with Applications 34, 2465–2479 (2008)
Bezabeh, T., et al.: Statistical classification strategy for proton magnetic resonance spectra of soft tissue sarcoma: an exploratory study with potential clinical utility. Sarcoma 6, 97–103 (2002)
Siddall, et al.: Magnetic Resonance Spectroscopy Detects Biochemical Changes in the Brain Associated with Chronic Low Back Pain: A Preliminary Report. Anesthesia and Analgesia 102, 1164-1168 (2006)
Luts, et al.: A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. Artificial Intelligence in Medicine 40(2), 87–102 (2007)
García-Gómez, et al.: Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy. Magnetic Resonance Materials in Physics 22(1), 5–18 (2009)
Siemens, MR Spectroscopy Operator Manual: Version syngo MR 2002B. Erlangen (2002)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)
Luts, J., et al.: Effect of Feature Extraction for Brain Tumor Classification Based on Short Echo Time 1H MR Spectra. Magnetic Resonance in Medicine 60, 288–298 (2008)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Hoboken (2000)
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, Heidelberg (2003)
Berry, M.J.A., Linoff, G.S.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley Computer Publishing, Indianapolis (2004)
Kantardzic, M.: Data Mining: Concepts, Models, Methods and Algorithms. Wiley-IEEE Press (2002)
An, A.: Classification Methods. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, pp. 144–149. Idea Group Publishing, Hershey (2006)
Kertész-Farkas, A., et al.: Benchmarking protein classification algorithms via supervised cross-validation. Journal of Biochemical and Biophysical Methods 70, 1215–1223 (2008)
Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: IJCAI (International Joint Conferences on Artificial Intelligence), Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Québec, Canada, August 20-25, Morgan Kaufmann, San Francisco (1995)
Guillet, F., Hamilton, H.J.: Quality Measures in Data Mining. Spinger, Warsaw (2007)
Lukas, L., et al.: Brain tumor classification based on long echo proton MRS signals. Artificial Intelligence in Medicine 31, 73–89 (2004)
Devos, A., et al.: Classification of brain tumours using short echo time 1H MR spectra. Journal of Magnetic Resonance 170, 164–175 (2004)
Manocha, S., Girolami, M.A.: An empirical analysis of the probabilistic K-nearest neighbour classifier. Pattern Recognition Letters 28, 1818–1824 (2007)
Kazmierska, J., Malicki, J.: Application of the Naïve Bayesian Classifier to optimize treatment decisions. Radiotherapy and Oncology 86, 211–216 (2008)
Opstad, et al.: Linear discriminant analysis of brain tumour 1H MR spectra: a comparison of classification using whole spectra versus metabolite quantification. NMR in Biomedicine 20, 763–770 (2007)
Giudici, P.: Applied Data Mining: Statistical Methods for Business and Industry. Wiley, England (2003)
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Oliveira, S., Rocha, J., Alves, V. (2010). Brain Magnetic Resonance Spectroscopy Classifiers. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds) Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010). Advances in Intelligent and Soft Computing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13161-5_26
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DOI: https://doi.org/10.1007/978-3-642-13161-5_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13160-8
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