The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters
Open accessAbstract
Purpose To identify the reproducible and nonredundant radiomics features (RFs) for computed tomography (CT). Materials and Methods Two phantoms were used to test RF reproducibility by using test-retest analysis, by changing the CT acquisition parameters (hereafter, intra-CT analysis), and by comparing five different scanners with the same CT parameters (hereafter, inter-CT analysis). Reproducible RFs were selected by using the concordance correlation coefficient (as a measure of the agreement between variables) and the coefficient of variation (defined as the ratio of the standard deviation to the mean). Redundant features were grouped by using hierarchical cluster analysis. Results A total of 177 RFs including intensity, shape, and texture features were evaluated. The test-retest analysis showed that 91% (161 of 177) of the RFs were reproducible according to concordance correlation coefficient. Reproducibility of intra-CT RFs, based on coefficient of variation, ranged from 89.3% (151 of 177) to 43.1% (76 of 177) where the pitch factor and the reconstruction kernel were modified, respectively. Reproducibility of inter-CT RFs, based on coefficient of variation, also showed large material differences, from 85.3% (151 of 177; wood) to only 15.8% (28 of 177; polyurethane). Ten clusters were identified after the hierarchical cluster analysis and one RF per cluster was chosen as representative. Conclusion Many RFs were redundant and nonreproducible. If all the CT parameters are fixed except field of view, tube voltage, and milliamperage, then the information provided by the analyzed RFs can be summarized in only 10 RFs (each representing a cluster) because of redundancy.
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Cites
Radiomics: Images Are More than Pictures, They Are Data
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Radiomics in medical imaging—“how-to” guide and critical reflection
van Timmeren, J. E., Cester, D., Tanadini-Lang, S., Alkadhi, H., & Baessler, B. (2020) · Insights into Imaging · doi:10.1186/s13244-020-00887-2 · Cited by 1,316
AI applications to medical images: From machine learning to deep learning
Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D'Amico, N. C., & Sardanelli, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.006 · Cited by 710
Park, J. E., Park, S. Y., Kim, H. J., & Kim, H. S. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2018.0070 · Cited by 388
Radiomics in breast cancer classification and prediction
Conti, A., Duggento, A., Indovina, I., Guerrisi, M., & Toschi, N. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.04.002 · Cited by 386
Metadata
- Authors
- Berenguer, R., Pastor-Juan, M. D. R., Canales-Vázquez, J., Castro-García, M., Villas, M. V., Mansilla Legorburo, F., & Sabater, S.
- Venue
- Radiology
- Published
- 2018-08
- Volume
- 288
- Issue
- 2
- Pages
- 407-415
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2801894005
- Cited by
- 622
- References
- 32
- FWCI
- 43.78
- Type
- journal article
- Language
- en
- Open access
- green
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- Berenguer, R., Pastor-Juan, M. D. R., Canales-Vázquez, J., Castro-García, M., Villas, M. V., Mansilla Legorburo, F., & Sabater, S. (2018). Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. Radiology. https://doi.org/10.1148/radiol.2018172361
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