Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T

Er, FüsunHatay, Gökçe HaleYıldırım, MuhammedÖztürk , Esin Işık2024-02-132024-02-132014Er, F. C.. vd. (2014). "Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T". IEEE Engineering in Medicine and Biology Society Conference Proceedings, 2014 36. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 2392-2395.978-1-4244-7929-0https://hdl.handle.net/11452/39639This 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.eninfo:eu-repo/semantics/closedAccessRadiatonEngineeringArtificial intelligenceTumorsBrainSupport vector machinesLearning algorithmsRegression analysisMagnetic resonance spectroscopyPhosphorusMetabolitesBrain tumorsClassification algorithmPeak intensityHuman brain tumorsLogistic regressionsMagnetic resonance spectroscopic imagingMagnetic resonanceAdultBrain neoplasmsFemaleHumansLogistic modelsMagnetic resonance imagingMagnetic resonance spectroscopyMiddle agedPhosphorusRoc curveSupport vector machinesClassification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3TArticle0003500447020942-s2.0-849294843732392239525570471Engineering, biomedicalEngineering, electrical & electronicCerebral Blood Volume; N Acetylaspartic Acid; GliomaAdultBrain neoplasmsDiagnostic useFemaleHumanMiddle agedNuclear magnetic resonance imagingNuclear magnetic resonance spectroscopyProceduresReceiver operating characteristicStatistical modelSupport vector machinePhosphorus1557-170X