Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma

J Magn Reson Imaging. 2011 Feb;33(2):296-305. doi: 10.1002/jmri.22432.


Purpose: To automatically differentiate radiation necrosis from recurrent tumor at high spatial resolution using multiparametric MRI features.

Materials and methods: MRI data retrieved from 31 patients (15 recurrent tumor and 16 radiation necrosis) who underwent chemoradiation therapy after surgical resection included post-gadolinium T1, T2, fluid-attenuated inversion recovery, proton density, apparent diffusion coefficient (ADC), and perfusion-weighted imaging (PWI) -derived relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time maps. After alignment to post contrast T1WI, an eight-dimensional feature vector was constructed. An one-class-support vector machine classifier was trained using a radiation necrosis training set. Classifier parameters were optimized based on the area under receiver operating characteristic (ROC) curve. The classifier was then tested on the full dataset.

Results: The sensitivity and specificity of optimized classifier for pseudoprogression was 89.91% and 93.72%, respectively. The area under ROC curve was 0.9439. The distribution of voxels classified as radiation necrosis was supported by the clinical interpretation of follow-up scans for both nonprogressing and progressing test cases. The ADC map derived from diffusion-weighted imaging and rCBV, rCBF derived from PWI were found to make a greater contribution to the discrimination than the conventional images.

Conclusion: Machine learning using multiparametric MRI features may be a promising approach to identify the distribution of radiation necrosis tissue in resected glioblastoma multiforme patients undergoing chemoradiation.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Artificial Intelligence
  • Brain Neoplasms / diagnosis*
  • Brain Neoplasms / therapy
  • Diagnosis, Differential
  • Glioblastoma / diagnosis*
  • Glioblastoma / therapy
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods*
  • Neoplasm Recurrence, Local / diagnosis*
  • Neoplasm Recurrence, Local / prevention & control
  • Pattern Recognition, Automated / methods*
  • Radiation Injuries / diagnosis*
  • Radiation Injuries / etiology
  • Radiotherapy, Adjuvant / adverse effects*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Treatment Outcome