Accurate grading of brain gliomas by soft independent modeling of class analogy based on non-negative matrix factorization of proton magnetic resonance spectra.
Hydrogen magnetic resonance spectroscopy ((1) H-MRS) is a non-invasive technique which provides a 'frequency-signal intensity' spectrum of biochemical compounds of tissues in the body. Although this method is currently used in human brain studies, accurate classification of in-vivo (1) H-MRS is a challenging task in the diagnosis of brain tumors. Problems such as overlapping metabolite peaks, incomplete information on background component and low signal-to-noise ratio disturb classification results of this spectroscopic method. This study presents an alternative approach to the soft independent modeling of class analogy (SIMCA) technique, using non-negative matrix factorization (NMF) for dimensionality reduction. In the adopted strategy, the performance of SIMCA was improved by application of a robust algorithm for classification in the presence of noisy measurements. Total of 219 spectra from two databases were taken by water-suppressed short echo-time (1) H-MRS, acquired from different subjects with different stages of glial brain tumors (Grade II (26 cases), grade III (24 cases), grade IV (41 cases), as well as 25 healthy cases). The SIMCA was performed using two approaches: (i) principal component analysis (PCA) and (ii) non-negative matrix factorization (NMF), as a modified approach. Square prediction error was considered to assess the class membership of the external validation set. Finally, several figures of merit such as the correct classification rate (CCR), sensitivity and specificity were calculated. Results of SIMCA based on NMF showed significant improvement in percentage of correctly classified samples, 91.4% versus 83.5% for PCA-based model in an independent test set.
Chemistry Department, Faculty of Science, Imam Khomeini International University, Qazvin, Iran.
K
K
Ghasemi
Chemistry Department, Faculty of Science, Imam Khomeini International University, Qazvin, Iran.
M
M
Khanmohammadi
Tehran University of Medical Sciences, Medical Physics and Biomedical Engineering Department, Keshavarz Boulevard, Tehran, Iran.
H
H
Saligheh Rad
D000465:Algorithms; D001224:Aspartic Acid / Q000031:analogs & derivatives / Q000032:analysis; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D002794:Choline / Q000032:analysis; D003401:Creatine / Q000032:analysis; D005910:Glioma / Q000175:diagnosis* / Q000473:pathology; D005998:Glycine / Q000032:analysis; D006801:Humans; D060787:Neoplasm Grading; D025341:Principal Component Analysis; D066244:Proton Magnetic Resonance Spectroscopy / Q000379:methods
D001224:Aspartic Acid; C000179:N-acetylaspartate; D003401:Creatine; D002794:Choline; D005998:Glycine
1H-magnetic resonance spectroscopy; NMF; PCA; SIMCA; brain tumors; glioma