Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions

Mojtaba Zarei; Ehsan Abadi; Liesbeth Vancoillie; Ehsan Samei

doi:10.1117/1.JMI.11.2.025501

Protocol selection formalism for minimizing detectable differences in morphological radiomics features of lung lesions in repeated CT acquisitions

J Med Imaging (Bellingham). 2024 Mar;11(2):025501. doi: 10.1117/1.JMI.11.2.025501. Epub 2024 Apr 26.

Authors

Mojtaba Zarei^{1

2

3

4}, Ehsan Abadi^{1

2

3

4}, Liesbeth Vancoillie^{1

2

3

4}, Ehsan Samei^{1

2

3

4}

Affiliations

¹ Duke University, Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States.
² Duke University, Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States.
³ Duke University, Department of Radiology, Durham, North Carolina, United States.
⁴ Duke University, Duke University School of Medicine, Durham, North Carolina, United States.

PMID: 38680209
PMCID: PMC11047768 (available on 2025-04-26)
DOI: 10.1117/1.JMI.11.2.025501

Abstract

Background: The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use.

Purpose: The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs.

Method: MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners' reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA).

Results: The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions.

Conclusion: The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening.

Keywords: image acquisition optimization; minimum detectable difference; morphological radiomics feature; quantification.