ArticleComputer methods and programs in biomedicine2024
Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study.
Article in Computer methods and programs in biomedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- Impact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models.Insights into imaging · 2026Article
- Quantitative assessment of impact of technical and population-based factors on fairness of AI models for chest X-ray scans.Computers in biology and medicine · 2025Article
- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- Artificial intelligence in hepatocellular carcinoma screening: applications and challenges.Frontiers in medicine · 2025Review
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5 authors at 4 institutions in 1 country.
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Abstract
backgroundRadiomics 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.
methodsIn 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.
resultsAcross 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.
conclusionsThe software version and convolution kernel parameters impacted the radiomics feature the most.
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