Accuracy and variability of texture-based radiomics features of lung lesions across CT imaging conditions

Zheng, Yuese; Solomon, Justin; Choudhury, Kingshuk Roy; Marin, Daniele; Samei, Ehsan

doi:10.1117/12.2255806

Cited by 5 publications

(3 citation statements)

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“…Some studies have shown that CT image acquisition can influence measurement of a single quantitative feature [8][9][10] or an ensemble of features. [11][12][13] Other phantom studies show that intrascanner and interscanner variabilities can depend on several factors including the phantom material composition, radiomic feature calculation method, and scan settings. [13][14][15][16] While prior studies offer valuable information on feature extractions, they are limited in using either physical phantoms with limited realism and complexity or patient data where ground truth is often unknown and unobtainable.…”

Section: Introductionmentioning

confidence: 99%

Systematic analysis of bias and variability of texture measurements in computed tomography

et al. 2019

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Texture is a key radiomics measurement for quantification of disease and disease progression. The sensitivity of the measurements to image acquisition, however, is uncertain. We assessed bias and variability of computed tomography (CT) texture feature measurements across many clinical image acquisition settings and reconstruction algorithms. Diverse, anatomically informed textures (texture A, B, and C) were simulated across 1188 clinically relevant CT imaging conditions representing four in-plane pixel sizes (0.4, 0.5, 0.7, and 0.9 mm), three slice thicknesses (0.625, 1.25, and 2.5 mm), three dose levels (CTDI vol 1.90, 3.75, and 7.50 mGy), and 33 reconstruction kernels. Imaging conditions corresponded to noise and resolution properties representative of five commercial scanners (GE LightSpeed VCT, GE Discovery 750 HD, GE Revolution, Siemens Definition Flash, and Siemens Force) in filtered backprojection and iterative reconstruction. About 21 texture features were calculated and compared between the ground-truth phantom (i.e., preimaging) and its corresponding images. Each feature was measured with four unique volumes of interest (VOIs) sizes (244, 579, 1000, and 1953 mm 3. To characterize the bias, the percentage relative difference [PRD(%)] in each feature was calculated between the imaged scenario and the ground truth for all VOI sizes. Feature variability was assessed in terms of (1) σ PRD ð%Þ indicating the variability between the ground truth and simulated image scenario based on the PRD(%), (2) COV f indicating the simulation-based variability, and (3) COV T indicating the natural variability present in the ground-truth phantom. The PRD ranged widely from −97% to 1220%, with an underlying variability (σ) of up to 241%. Features such as gray-level nonuniformity, texture entropy, sum average, and homogeneity exhibited low susceptibility to reconstruction kernel effects (PRD < 3%) with relatively small σ PRD ð%Þ (≤5%) across imaging conditions. The dynamic range of results indicates that image acquisition and reconstruction conditions of in-plane pixel sizes, slice thicknesses, dose levels, and reconstruction kernels can lead to significant bias and variability in feature measurements.

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Section: Introductionmentioning

confidence: 99%

Systematic analysis of bias and variability of texture measurements in computed tomography

et al. 2019

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“…These tools have enabled the development of reconstruction pipelines that are not dependent on the availability of clinical CT scanners, can be configured to operate in high‐throughput batch modes, can incorporate simulated dose reduction techniques and therefore can produce a large collection of image datasets that represent a wide range of acquisition and reconstruction settings such as different slice thicknesses and reconstruction kernels used in wFBP. The results of these reconstruction pipelines have contributed to the growing list of investigations evaluating the robustness of quantitative imaging, radiomics, and CAD methods across a range of scanner platforms, acquisition conditions and reconstruction parameters …”

Section: Introductionmentioning

confidence: 99%

“…The results of these reconstruction pipelines have contributed to the growing list of investigations evaluating the robustness of quantitative imaging, radiomics, and CAD methods across a range of scanner platforms, acquisition conditions and reconstruction parameters. 2,[4][5][6][7][8][9] While this previous work was valuable and informative, it was limited to conventional wFBP reconstructions. Modern scanners offer advanced image reconstruction techniques that use some form of statistical or iterative reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: FreeCT_ICD: An open‐source implementation of a model‐based iterative reconstruction method using coordinate descent optimization for CT imaging investigations

et al. 2018

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FreeCT_ICD is an open-source implementation of a model-based iterative reconstruction method that extends the capabilities of previously released open-source reconstruction software and provides the ability to perform vendor-independent reconstructions of clinically acquired raw projection data. This implementation represents a reasonable tradeoff between storage and computational requirements and has demonstrated acceptable image quality in both simulated and clinical image datasets.

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CT-Based Quantification

Samei

Hoye

2019

Computed Tomography

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Accuracy and variability of texture-based radiomics features of lung lesions across CT imaging conditions

Cited by 5 publications

References 0 publications

Systematic analysis of bias and variability of texture measurements in computed tomography

Systematic analysis of bias and variability of texture measurements in computed tomography

Technical Note: FreeCT_ICD: An open‐source implementation of a model‐based iterative reconstruction method using coordinate descent optimization for CT imaging investigations

CT-Based Quantification

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