Structural and Metabolic Pattern Classification for Detection of Glioblastoma Recurrence and Treatment-Related Effects

Artificial neuronal network (ANN) in classification of glioblastoma multiforme (GBM) recurrence from treatment effects using advanced magnetic resonance imaging techniques was evaluated. In 56 patients with treated GBM, normalised minimal and mean apparent-diffusion coefficient (ADC) values, vessels...

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Bibliographic Details
Published in:Applied magnetic resonance 2017-09, Vol.48 (9), p.921-931
Main Authors: Jovanovic, Marija, Selmic, Milica, Macura, Dragana, Lavrnic, Slobodan, Gavrilovic, Svetlana, Dakovic, Marko, Radenkovic, Sandra, Soldatovic, Ivan, Stosic-Opincal, Tatjana, Maksimovic, Ruzica
Format: Article
Language:English
Subjects:
Artificial intelligence
Atoms and Molecules in Strong Fields
Back propagation
Brain cancer
Chemotherapy
Datasets
Diffusion coefficient
Edema
Imaging techniques
Laser Matter Interaction
Magnetic resonance imaging
Neural networks
Organic Chemistry
Original Paper
Patients
Pattern classification
Physical Chemistry
Physics
Physics and Astronomy
Radiation therapy
Sensitivity
Software
Solid State Physics
Spectroscopy/Spectrometry
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Summary:Artificial neuronal network (ANN) in classification of glioblastoma multiforme (GBM) recurrence from treatment effects using advanced magnetic resonance imaging techniques was evaluated. In 56 patients with treated GBM, normalised minimal and mean apparent-diffusion coefficient (ADC) values, vessels number on susceptibility-weighted images (SWI) and Cho/Cr ratio were analysed statistically and by ANN. Significant correlation exists between normalised minimal and mean ADC values, and no correlation between ADC and Cho/Cr values. Cut-off values for tumour presence were: 1.14 for normalised minimal ADC (54% sensitivity, 71% specificity), 1.13 for normalised mean ADC (51% sensitivity, 71% specificity), 1.8 for Cho/Cr ratio (92% sensitivity, 82% specificity), grade 2 for SWI (87% sensitivity, 82% specificity). An accurate prediction of ANN to classify patients into GBM progression or treatment effects group was 99% during the training and 96.8% during the testing phase. Multi-parametric ANN allows distinction between GBM recurrence and treatment effects, and can be used in clinical practice.
ISSN:0937-9347
1613-7507
DOI:10.1007/s00723-017-0913-x