Classification of Phosphorus Magnetic Resonance Spectroscopic Imaging of Brain Tumors Using Support Vector Machine and Logistic Regression at 3T

Er F. C., Hatay G. H., Okeer E., Yildirim M., HAKYEMEZ B., Ozturk-Isik E.

36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Illinois, United States Of America, 26 - 30 August 2014, pp.2392-2395, (Full Text) identifier identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/embc.2014.6944103
  • City: Illinois
  • Country: United States Of America
  • Page Numbers: pp.2392-2395
  • Bursa Uludag University Affiliated: Yes

Abstract

This study aims classification of phosphorus magnetic resonance spectroscopic imaging (P-31-MRSI) data of human brain tumors using machine-learning algorithms. The metabolite peak intensities and ratios were estimated for brain tumor and healthy P-31 MR spectra acquired at 3T. The spectra were classified based on metabolite characteristics using logistic regression and support vector machine. This study showed that machine learning could be successfully applied for classification of P-31-MR spectra of brain tumors. Future studies will measure the performance of classification algorithms for P-31-MRSI of brain tumors in a larger patient cohort.