Quantification of Minimum Detectable Difference in Radiomics Features Across Lesions and CT Imaging Conditions.

Authors

Hoye, J; Solomon, JB; Sauer, TJ; Samei, E

Abstract

RATIONALE AND OBJECTIVES: The 3-fold purpose of this study was to (1) develop a method to relate measured differences in radiomics features in different computed tomography (CT) scans to one another and to true feature differences; (2) quantify minimum detectable change in radiomics features based on measured radiomics features from pairs of synthesized CT images acquired under variable CT scan settings, and (3) ascertain and inform the recommendations of the Quantitative Imaging Biomarkers Alliance (QIBA) for nodule volumetry. MATERIALS AND METHODS: Images of anthropomorphic lung nodule models were simulated using resolution and noise properties for 297 unique imaging conditions. Nineteen morphology features were calculated from both the segmentation masks derived from the imaged nodules and from ground truth nodules. Analysis was performed to calculate minimum detectable difference of radiomics features as a function of imaging protocols in comparison to QIBA guidelines. RESULTS: The minimum detectable differences ranged from 1% to 175% depending on the specific feature and set of imaging protocols. The results showed that QIBA protocol recommendations result in improved minimum detectable difference as compared to the range of possible protocols. The results showed that the minimum detectable differences may be improved from QIBA's current recommendation by further restricting the slice thickness requirement to be between 0.5 mm and 1 mm. CONCLUSION: Minimum detectable differences of radiomics features were quantified for lung nodules across a wide range of possible protocols. The results can be used prospectively to inform decision-making about imaging protocols to provide superior quantification of radiomics features.

Citation

Hoye, Jocelyn, Justin B. Solomon, Thomas J. Sauer, and Ehsan Samei. “Quantification of Minimum Detectable Difference in Radiomics Features Across Lesions and CT Imaging Conditions.” Acad Radiol, August 20, 2020. https://doi.org/10.1016/j.acra.2020.07.029.

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