Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients.
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,...
| Published in: | BioMed Research International Vol. 2015; pp. 1 - 14 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Published: |
Wiley-Blackwell
8/27/2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109322435&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109322435 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/27/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109322435 109322435 NLM26413548 10.1155/2015/842923 NLM26413548 PMC4564625 109322435 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Tumour Relapse Prediction Using Multiparametric MR Data Recorded during Follow-Up of GBM Patients. aug: au: Ion-Margineanu, Adrian Van Cauter, Sofie Sima, Diana M. Maes, Frederik Van Gool, Stefaan W. Sunaert, Stefan Himmelreich, Uwe Van Huffel, Sabine affil: 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 sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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