Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
Add to list
  1. Search results
  2. Automated grade prediction of glioma ...

Automated grade prediction of glioma patients based on magnetic resonance imaging and a random forests approach

Stijn Bonte, Ingeborg Goethals (UGent) and Roel Van Holen (UGent)
(2016) Neuro-Oncology. 18(Supplement 4). p.iv38-iv38
Author
Organization
Abstract
An automated brain tumour classification method is presented which is able to distinguish between low-grade and high-grade glioma on conventional MRI scans. Per patient, 208 quantitative features are extracted from a manually annotated brain tumour database of 274 patients. These features were then used to train a Random Forests classification algorithm. We achieved a high-grade prediction sensitivity of 85.5% and specificity of 83.3%, with a global accuracy of 85.0%.
Keywords
Neuro-oncology, Machine Learning, Radiomics, Glioma, Random Forests, MRI

Citation

Please use this url to cite or link to this publication:

MLA
Bonte, Stijn, et al. “Automated Grade Prediction of Glioma Patients Based on Magnetic Resonance Imaging and a Random Forests Approach.” Neuro-Oncology, vol. 18, no. Supplement 4, Society for Neuro-Oncology, 2016, pp. iv38–iv38.
APA
Bonte, S., Goethals, I., & Van Holen, R. (2016). Automated grade prediction of glioma patients based on magnetic resonance imaging and a random forests approach. Neuro-Oncology, 18(Supplement 4), iv38–iv38. Society for Neuro-Oncology.
Chicago author-date
Bonte, Stijn, Ingeborg Goethals, and Roel Van Holen. 2016. “Automated Grade Prediction of Glioma Patients Based on Magnetic Resonance Imaging and a Random Forests Approach.” In Neuro-Oncology, 18:iv38–iv38. Society for Neuro-Oncology.
Chicago author-date (all authors)
Bonte, Stijn, Ingeborg Goethals, and Roel Van Holen. 2016. “Automated Grade Prediction of Glioma Patients Based on Magnetic Resonance Imaging and a Random Forests Approach.” In Neuro-Oncology, 18:iv38–iv38. Society for Neuro-Oncology.
Vancouver
1.
Bonte S, Goethals I, Van Holen R. Automated grade prediction of glioma patients based on magnetic resonance imaging and a random forests approach. In: Neuro-Oncology. Society for Neuro-Oncology; 2016. p. iv38–iv38.
IEEE
[1]
S. Bonte, I. Goethals, and R. Van Holen, “Automated grade prediction of glioma patients based on magnetic resonance imaging and a random forests approach,” in Neuro-Oncology, Mannheim/Heidelberg, 2016, vol. 18, no. Supplement 4, pp. iv38–iv38.
@inproceedings{8117504,
  abstract     = {{An automated brain tumour classification method is presented which is able to distinguish between low-grade and high-grade glioma on conventional MRI scans. Per patient, 208 quantitative features are extracted from a manually annotated brain tumour database of 274 patients. These features were then used to train a Random Forests classification algorithm. We achieved a high-grade prediction sensitivity of 85.5% and specificity of 83.3%, with a global accuracy of 85.0%.}},
  author       = {{Bonte, Stijn and Goethals, Ingeborg and Van Holen, Roel}},
  booktitle    = {{Neuro-Oncology}},
  issn         = {{1522-8517}},
  keywords     = {{Neuro-oncology,Machine Learning,Radiomics,Glioma,Random Forests,MRI}},
  language     = {{eng}},
  location     = {{Mannheim/Heidelberg}},
  number       = {{Supplement 4}},
  pages        = {{iv38--iv38}},
  publisher    = {{Society for Neuro-Oncology}},
  title        = {{Automated grade prediction of glioma patients based on magnetic resonance imaging and a random forests approach}},
  volume       = {{18}},
  year         = {{2016}},
}