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BioMed Research International
Volume 2015 (2015), Article ID 842923, 13 pages
Research Article

Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients

1Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, P.O. Box 2446, 3001 Leuven, Belgium
2iMinds Medical IT, 3000 Leuven, Belgium
3Department of Radiology, University Hospitals of Leuven, Herestraat 49, 3000 Leuven, Belgium
4Department of Electrical Engineering (ESAT), PSI Center for Processing Speech and Images, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium
5Department of Pedriatic Neuro-Oncology, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium
6Biomedical MRI/MoSAIC, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium

Received 5 September 2014; Accepted 16 December 2014

Academic Editor: Zhengchao Dong

Copyright © 2015 Adrian Ion-Margineanu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Purpose. We have focused on finding a classifier that best discriminates between tumour progression and regression based on multiparametric MR data retrieved from follow-up GBM patients. Materials and Methods. Multiparametric MR data consisting of conventional and advanced MRI (perfusion, diffusion, and spectroscopy) were acquired from 29 GBM patients treated with adjuvant therapy after surgery over a period of several months. A 27-feature vector was built for each time point, although not all features could be obtained at all time points due to missing data or quality issues. We tested classifiers using LOPO method on complete and imputed data. We measure the performance by computing BER for each time point and wBER for all time points. Results. If we train random forests, LogitBoost, or RobustBoost on data with complete features, we can differentiate between tumour progression and regression with 100% accuracy, one time point (i.e., about 1 month) earlier than the date when doctors had put a label (progressive or responsive) according to established radiological criteria. We obtain the same result when training the same classifiers solely on complete perfusion data. Conclusions. Our findings suggest that ensemble classifiers (i.e., random forests and boost classifiers) show promising results in predicting tumour progression earlier than established radiological criteria and should be further investigated.