Machine learning in preoperative glioma MRI: Survival associations by perfusion-based support vector machine outperforms traditional MRI

Authors

  • Kyrre E. Emblem PhD,

    Corresponding author
    1. Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
    2. Intervention Centre, Rikshospitalet, Oslo University Hospital, Oslo, Norway
    • Address reprint requests to: K.E.E., Massachusetts General Hospital, Building 149, 13th St., Charlestown, MA 02129. E-mail: kyrre@nmr.mgh.harvard.edu

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  • Paulina Due-Tonnessen MD,

    1. Department of Radiology, Rikshospitalet, Oslo University Hospital, Oslo, Norway
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  • John K. Hald MD, PhD,

    1. Department of Radiology, Rikshospitalet, Oslo University Hospital, Oslo, Norway
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  • Atle Bjornerud PhD,

    1. Intervention Centre, Rikshospitalet, Oslo University Hospital, Oslo, Norway
    2. Department of Physics, University of Oslo, Oslo, Norway
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  • Marco C. Pinho MD,

    1. Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
    2. Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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  • David Scheie MD,

    1. Department of Pathology, Rikshospitalet, Oslo University Hospital, Oslo, Norway
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  • Lothar R. Schad PhD,

    1. Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
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  • Torstein R. Meling MD, PhD,

    1. Department of Neurosurgery, Rikshospitalet, Oslo University Hospital, Oslo, Norway
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  • Frank G. Zoellner PhD

    1. Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
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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.

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