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J Magn Reson Imaging. 2014 Jul;40(1):47-54. doi: 10.1002/jmri.24390. Epub 2013 Nov 13.

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

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Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Intervention Centre, Rikshospitalet, Oslo University Hospital, Oslo, Norway.



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.


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.


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).


The automatic machine learning routine presented in our study may provide the operator with a reliable instrument for assessing survival in patients with glioma.


computer aided diagnosis (CAD); glioma; histogram analysis; perfusion MRI; survival associations

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