![Sample our Medicine, Dentistry, Nursing & Allied Health journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5944/TOC-Subject-Sample-2021-Medicine-Dentistry-Nursing-Allied-Health.jpg"})
Abstract
Glioblastoma is the most aggressive malignant brain tumor with poor prognosis. Radiomics is a newly emerging and promising technique to reveal the complex relationships between high-throughput medical image features and deep information of disease including pathology, biomarkers and genomics. An approach was developed to investigate the internal relationship between magnetic resonance imaging (MRI) features and the age-related origins of glioblastomas based on a quantitative radiomics method. A fully automatic image segmentation method was applied to segment the tumor regions from three dimensional MRI images. 555 features were then extracted from the image data. By analyzing large numbers of quantitative image features, some predictive and prognostic information could be obtained by the radiomics approach. 96 patients diagnosed with glioblastoma pathologically have been divided into two age groups (<45 and ≥45 years old). As expected, there are 101 features showing the consistency with the age groups (T test, p < .05), and unsupervised clustering results of those features also show coherence with the age difference (T test, p= .006). In conclusion, glioblastoma in different age groups present different radiomics-feature patterns with statistical significance, which indicates that glioblastoma in different age groups should have different pathologic, protein, or genic origins.
Acknowledgements
This work was supported by the National Basic Research Program of China (2015CB755500), the National Natural Science Foundation of China (61271071). Thanks to Dr. Zhifeng Shi, Dr. Liang Chen and Dr. Ying Mao for providing clinical guidance.
Disclosure statement
No potential conflict of interest was reported by the authors.