Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data

Springer Science and Business Media LLC - Tập 8 - Trang 751-761 - 2013
Evangelia Tsolaki1, Patricia Svolos1, Evanthia Kousi1, Eftychia Kapsalaki2, Konstantinos Fountas3, Kyriaki Theodorou1, Ioannis Tsougos1
1Medical Physics Department, Medical School, University of Thessaly, Biopolis, Larissa, Greece
2Department of Radiology, Medical School, University of Thessaly, Biopolis, Larissa, Greece
3Department of Neurosurgery, Medical School, University of Thessaly, Biopolis, Larissa, Greece

Tóm tắt

Purpose   Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data. Materials and Methods   Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip  $$+$$  Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Naïve-Bayes, Support Vector Machine (SVM) and $$k$$ -nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics. Results   Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions ( $$p<0.05$$ ). SVM achieved the highest overall performance (accuracy 98 %) for both the intratumoral and peritumoral areas. Naïve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology. Conclusion   The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

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