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Exploring feature-based approaches in PET images for predicting cancer treatment outcomes
Open accessAbstract
Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.
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Cited by
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132
Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology · doi:10.1148/radiol.2020191145 · Cited by 4,177
Self-citationRadiomics: the process and the challenges
Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.010 · Cited by 2,258
“, mean, median, min, max, range, skewness, kurtosis), but also more complex values, such as metabolic volume above an absolute SUV of 5 or the fraction of highdensity tissue measured with CT [60,61].”
BackgroundAssessment of tumor heterogeneity: an emerging imaging tool for clinical practice?
Davnall, F., Yip, C. S. P., Ljungqvist, G., Selmi, M., Ng, F., Sanghera, B., Ganeshan, B., Miles, K. A., Cook, G. J., & Goh, V. (2012) · Insights into Imaging · doi:10.1007/s13244-012-0196-6 · Cited by 878
Metadata
- Authors
- El Naqa, I., Grigsby, P. W., Apte, A., Kidd, E., Donnelly, E., Khullar, D., Chaudhari, S., Yang, D., Schmitt, M., Laforest, R., Thorstad, W. L., & Deasy, J. O.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2009-06
- Volume
- 42
- Issue
- 6
- Pages
- 1162-1171
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2074445922
- Cited by
- 480
- References
- 0
- FWCI
- 3.82
- Type
- journal article
- Language
- en
- Open access
- green
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- El Naqa, I., Grigsby, P. W., Apte, A., Kidd, E., Donnelly, E., Khullar, D., Chaudhari, S., Yang, D., Schmitt, M., Laforest, R., Thorstad, W. L., & Deasy, J. O. (2009). Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognition. https://doi.org/10.1016/j.patcog.2008.08.011
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