Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards.
发表日期： 2015.05.12 来源：Journal of Medical Imaging, 2015, 2 (4) :041002
Nyflot MJ11, Yang F11, Byrd D22, Bowen SR33 , Sandison GA11, Kinahan PE22.
1. University of Washington , Department of Radiation Oncology, 1959 NE Pacific Street, Box 356043, Seattle, Washington 98195-6043, United States.
2. University of Washington , Department of Radiology, 1959 NE Pacific Street, Box 356043, Seattle, Washington 98195-6043, United States.
3. University of Washington , Department of Radiation Oncology, 1959 NE Pacific Street, Box 356043, Seattle, Washington 98195-6043, United States ; University of Washington , Department of Radiology, 1959 NE Pacific Street, Box 356043, Seattle, Washington 98195-6043, United States.
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 resolution 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 performance 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.