Chapter title |
Glioblastoma and Survival Prediction
|
---|---|
Chapter number | 31 |
Book title |
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
|
Published in |
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries : third International Workshop, BrainLes 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised selected papers. Bra..., January 2018
|
DOI | 10.1007/978-3-319-75238-9_31 |
Pubmed ID | |
Book ISBNs |
978-3-31-975237-2, 978-3-31-975238-9
|
Authors |
Zeina A. Shboul, Lasitha Vidyaratne, Mahbubul Alam, Khan M. Iftekharuddin |
Abstract |
Glioblastoma is a stage IV highly invasive astrocytoma tumor. Its heterogeneous appearance in MRI poses critical challenge in diagnosis, prognosis and survival prediction. This work extracts a total of 1207 different types of texture and other features, tests their significance and prognostic values, and then utilizes the most significant features with Random Forest regression model to perform survival prediction. We use 163 cases from BraTS17 training dataset for evaluation of the proposed model. A 10-fold cross validation offers normalized root mean square error of 30% for the training dataset and the cross validated accuracy of 63%, respectively. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 27 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 4 | 15% |
Student > Ph. D. Student | 4 | 15% |
Student > Bachelor | 3 | 11% |
Student > Postgraduate | 3 | 11% |
Professor > Associate Professor | 2 | 7% |
Other | 3 | 11% |
Unknown | 8 | 30% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 7 | 26% |
Engineering | 6 | 22% |
Medicine and Dentistry | 4 | 15% |
Biochemistry, Genetics and Molecular Biology | 1 | 4% |
Physics and Astronomy | 1 | 4% |
Other | 0 | 0% |
Unknown | 8 | 30% |