Purpose. We have focused on finding a classifier that best discriminates between @entity5 progression and regression based on multiparametric MR data retrieved from follow-up @entity11 @entity1 . Materials and Methods. Multiparametric MR data consisting of conventional and advanced MRI (perfusion, diffusion, and spectroscopy) were acquired from 29   @entity11 @entity1 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 @entity5 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 @entity5 progression earlier than established radiological criteria and should be further investigated.

Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of @entity11 XXXX .

@entity5 :: ('MESH:D009369', 'Disease') :: ['tumour'] @entity1 :: ('9606', 'Species') :: ['patients'] @entity11 :: ('MESH:D005910', 'Disease') :: ['GBM']
@entity1 :: (9606,Species) :: ['Patients']
