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Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

By Paul E Kinahan

University of Washington

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Abstract

Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and res- olution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative per- formance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes 

Acknowledgments

We thank Larry Pierce, Adam Alessio, and William Yuh for helpful discussions and review. Supported by NIH grants R01 CA169072, U01 CA148131, NCI Contract (SAIC-Frederick) 24XS036-004, and a research contract from GE Healthcare. 

References

M. J. Nyflot, F. Yang, D. Byrd, S. R. Bowen, G. A. Sandison, and P. E. Kinahan, “Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards,” J. Med. Imag, vol. 2, no. 4, pp. 1–13, Oct. 2015.

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Researchers should cite this work as follows:

  • Paul E Kinahan (2015), "Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards," https://ncihub.cancer.gov/resources/1421.

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