Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study

Year: 2023

Type: article

Abstract: BACKGROUND: Radiomics is a method within medical image analysis that involves the extraction of quantitative data from radiologic scans, often in conjunction with machine learning algorithms to phenotype disease appearance, prognosticate disease outcome, and predict treatment response. However, variance in CT scanner acquisition parameters, such as convolution kernels or pixel spacing, can impact radiomics texture feature values. PURPOSE: The extent to which the parameters influence radiomics features continues to be an active area of investigation. In this study, we describe a novel approach, Acquisition Impact on Radiomics Estimation (AcquIRE), to rank the impact of CT acquisition parameters on radiomic texture features. METHODS: In this work, we used three chest CT imaging datasets (n = 749 patients) from nine sites comprising: i) lung granulomas and adenocarcinomas (D1) (10 and 52 patients, respectively); ii) minimal and frank invasive adenocarcinoma (D2) (74 and 145 patients); and iii) early-stage NSCLC patients (D3) (315 patients). Datasets D2 and D3 were collected from four sites each, and D1 from a single site. For each patient, 744 texture features and nine acquisition parameters were extracted and utilized to evaluate which parameters impact radiomic features the most. The AcquIRE method establishes a relative assessment between acquisition parameters and radiomic texture featuresa through the creation of a classification model, which is then utilized to assess the rank of the acquisition parameters. RESULTS: Across the use cases, CT software version and convolution kernel parameters were found to have the most variance. In D1, it was observed that the Haralick texture feature family was the least affected by variations in acquisition parameters, while the Gabor feature family was the most impacted. However, in datasets D2 and D3, the Gabor features were found to be the least affected. Our findings suggest that the impact on radiomic parameters is as much a function of the problem in question as it is acquisition parameters. CONCLUSIONS: The software version and convolution kernel parameters impacted the radiomics feature the most.

Source: Computer Methods and Programs in Biomedicine

DOI: 10.1016/j.cmpb.2023.107990

Authors: Dmitry Cherezov, Vidya Sankar Viswanathan, Pingfu Fu, Amit Gupta, Anant Madabhushi

Institutions: Emory University, Case Western Reserve University, University Hospitals of Cleveland, University Hospitals Cleveland Medical Center, Veterans Health Administration, Atlanta VA Medical Center

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,880

Funders: U.S. Department of Veterans Affairs, Eli Lilly and Company, AstraZeneca, Biomedical Laboratory Research and Development, VA Office of Research and Development, Frederick National Laboratory for Cancer Research, Military Health System, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, National Center for Advancing Translational Sciences, DOD Prostate Cancer Research Program, DOD Peer Reviewed Cancer Research Program

OpenAlex ID: W4390145965