ECR 2019 / C-2005
How to Perform Radiomic Studies in the Clinical Setting Taking Imaging and Biological Factors into Account
Type:
Educational Exhibit
Keywords:
Artificial Intelligence, MR, Computer Applications-Detection, diagnosis, Cancer
Authors:
M. D. Patel, V. Sawlani; Birmingham/UK
DOI:
10.26044/ecr2019/C-2005
References
- Erickson BJ,
Korfiatis P,
Akkus Z,
Kline TL.
Machine Learning for Medical Imaging.
Radiographics.
2017;37(2):505-515.
- Gillies RJ,
Kinahan PE,
Hricak H.
Radiomics: Images Are More than Pictures,
They Are Data.
Radiology.
2016;278(2):563-77.
- Zhou M,
Scott J,
Chaudhury B,
et al.
Radiomics in Brain Tumor: Image Assessment,
Quantitative Feature Descriptors,
and Machine-Learning Approaches.
AJNR Am J Neuroradiol.
2017.
- Narang,
S.,
Lehrer,
M.,
Yang,
D.,
Lee,
J.,
& Rao,
A.
(2016).
Radiomics in glioblastoma: current status,
challenges and potential opportunities.
Translational Cancer Research,
5(4),
383-397.
doi:10.21037/8806
- Avanzo M,
Stancanello J,
El naqa I.
Beyond imaging: The promise of radiomics.
Phys Med.
2017;38:122-139.
- Hyare H,
Thust S,
Rees J.
Advanced MRI Techniques in the Monitoring of Treatment of Gliomas.
Curr Treat Options Neurol.
2017;19(3):11.
- Acharya UR,
Hagiwara Y,
Sudarshan VK,
Chan WY,
Ng KH.
Towards precision medicine: from quantitative imaging to radiomics.
J Zhejiang Univ Sci B.
2018;19(1):6-24.
- Sala E,
Mema E,
Himoto Y,
et al.
Unravelling tumour heterogeneity using next-generation imaging: radiomics,
radiogenomics,
and habitat imaging.
Clin Radiol.
2017;72(1):3-10.