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Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering
Author(s) -
Emblem Kyrre E.,
Nedregaard Baard,
Hald John K.,
Nome Terje,
DueTonnessen Paulina,
Bjornerud Atle
Publication year - 2009
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21815
Subject(s) - glioma , medicine , cluster analysis , segmentation , histogram , contrast (vision) , brain tumor , computer science , radiology , artificial intelligence , nuclear medicine , pattern recognition (psychology) , pathology , image (mathematics) , cancer research
Purpose To assess whether glioma volumes from knowledge‐based fuzzy c‐means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. Materials and Methods Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. Results The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas ( P = 0.576–0.970). When identifying a high‐risk patient group (expected survival <2 years) and a low‐risk patient group (expected survival >2 years), a higher log‐rank value from Kaplan‐Meier survival analysis was observed when using automatic tumor volumes (14.403; P < 0.001) compared with the manual volumes (10.650–12.761; P = 0.001–0.002). Conclusion Our results suggest that knowledge‐based FCM clustering of multiple MR image classes provides a completely automatic, user‐independent approach to selecting the target region for presurgical glioma characterization J. Magn. Reson. Imaging 2009;30:1–10. © 2009 Wiley‐Liss, Inc.