Machine learning in preoperative glioma MRI: Survival associations by perfusion‐based support vector machine outperforms traditional MRI. Issue 1 (13th November 2013)

• Record Type: Journal Article
• Authors: Emblem, Kyrre E.; Due‐Tonnessen, Paulina; Hald, John K.; Bjornerud, Atle; Pinho, Marco C.; Scheie, David; Schad, Lothar R.; Meling, Torstein R.; Zoellner, Frank G.
• Is Part Of: Journal of magnetic resonance imaging. Volume 40:Issue 1(2014)
• Page Start: 47
• Page End: 54
• Publication Date: 2013-11-13
• Subjects: perfusion MRI -- glioma -- computer aided diagnosis (CAD) -- histogram analysis -- survival associations; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.24390
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Purpose: To retrospectively evaluate the performance of an automatic support vector machine (SVM) routine in combination with perfusion‐based dynamic susceptibility contrast magnetic resonance imaging (DSC‐MRI) for preoperative survival associations in patients with gliomas and compare our results to traditional MRI. Materials and Methods: The study was approved by the Ethics Committee and informed consent was signed. Structural, diffusion‐ and perfusion‐weighted MRI was performed at 1.5‐T preoperatively in 94 adult patients (49 males, 45 females, 23–82 years; mean 51 years) later diagnosed with a primary glioma. Patients were randomly assigned in training and test datasets and the resulting DSC‐based survival associations by SVM were compared to traditional MRI features including contrast‐agent enhancement, perfusion‐ and diffusion‐weighted imaging, tumor size, and location. The results were adjusted for age, neurological status, and postoperative factors associated with survival, including surgery and adjuvant therapy. Results: For 1‐ (26/33 alive, 11/14 deceased), 2‐ (15/21, 21/26), 3‐ (12/16, 27/31) and 4‐ (12/15, 28/32) year survival associations in the test dataset (47 patients), the SVM routine was the only biomarker to consistently associate with survival (Cox; P < 0.001). Conclusion: The automatic machine learning routine presented in our study may provide the operator with a reliable instrument for assessing survival in patients with glioma.J. Magn. Reson. Imaging 2014;40:47–54 . ©2013 Wiley Periodicals, Inc .