title: Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients.
abstract: OBJECTIVE
  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.
  
  
  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.
authors.affiliation: Department 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 ; iMinds Medical IT, 3000 Leuven, Belgium.
authors.forename: Adrian
authors.initials: A
authors.lastname: Ion-Margineanu
authors.affiliation: Department of Radiology, University Hospitals of Leuven, Herestraat 49, 3000 Leuven, Belgium.
authors.forename: Sofie
authors.initials: S
authors.lastname: Van Cauter
authors.affiliation: Department 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 ; iMinds Medical IT, 3000 Leuven, Belgium.
authors.forename: Diana M
authors.initials: DM
authors.lastname: Sima
authors.affiliation: iMinds Medical IT, 3000 Leuven, Belgium ; Department of Electrical Engineering (ESAT), PSI Center for Processing Speech and Images, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
authors.forename: Frederik
authors.initials: F
authors.lastname: Maes
authors.affiliation: Department of Pedriatic Neuro-Oncology, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium.
authors.forename: Stefaan W
authors.initials: SW
authors.lastname: Van Gool
authors.affiliation: Department of Radiology, University Hospitals of Leuven, Herestraat 49, 3000 Leuven, Belgium.
authors.forename: Stefan
authors.initials: S
authors.lastname: Sunaert
authors.affiliation: Biomedical MRI/MoSAIC, Department of Imaging and Pathology, KU Leuven, 3000 Leuven, Belgium.
authors.forename: Uwe
authors.initials: U
authors.lastname: Himmelreich
authors.affiliation: Department 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 ; iMinds Medical IT, 3000 Leuven, Belgium.
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D015331:Cohort Studies; D005909:Glioblastoma / Q000175:diagnosis / Q000453:epidemiology / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009364:Neoplasm Recurrence, Local