The DOI matches its Crossref record; 151 of the 151 references it cites were found in a registry or an index.
Radiomics: a new application from established techniques
Open accessAbstract
The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.
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Cited by
Scapicchio, C., Gabelloni, M., Barucci, A., Cioni, D., Saba, L., & Neri, E. (2021) · La radiologia medica · doi:10.1007/s11547-021-01389-x · Cited by 435
Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226
Grosu, S., Wesp, P., Graser, A., Maurus, S., Schulz, C., Knösel, T., Cyran, C. C., Ricke, J., Ingrisch, M., & Kazmierczak, P. M. (2021) · Radiology · doi:10.1148/radiol.2021202363 · Cited by 75
Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma
Ahmed, A. A., Elmohr, M. M., Fuentes, D., Habra, M. A., Fisher, S. B., Perrier, N. D., Zhang, M., & Elsayes, K. M. (2020) · Clinical Radiology · doi:10.1016/j.crad.2020.01.012 · Cited by 41
Metadata
- Authors
- Parekh, V., & Jacobs, M. A.
- Venue
- Expert Review of Precision Medicine and Drug Development
- Publisher
- Informa UK Limited
- Published
- 2016-03-03
- Volume
- 1
- Issue
- 2
- Pages
- 207-226
- ISSN
- 2380-8993 (ISSN-L)
- OpenAlex
- W2313422996
- Cited by
- 369
- References
- 151
- FWCI
- 20.50
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
