Abstract
A rise of radiomics studies and techniques could be observed over the past few years, which centers around the extraction and analysis of quantitative features from medical images. Radiomics offers numerous advantages in disease characterization and treatment response prediction. Despite its promise, radiomics faces challenges in standardizing features and techniques, leading to large variations of approaches across studies and centers, making it difficult to determine the most suitable techniques for any given clinical scenario. Additionally, manually constructing optimized radiomics pipelines can be time-consuming. Recent works (WORC, Autoradiomics) have addressed the aforementioned shortcomings by introducing radiomics-based frameworks for automated pipeline optimization. Both approaches comprehensively span the entire radiomics workflow, enabling consistent, comprehensive, and reproducible radiomics analyses. In contrast, finding the ideal solutions for the workflow’s feature extractor and feature selection components, has received less attention. To address this, we propose the Radiomics Processing Toolkit (RPTK), which adds comprehensive feature extraction and selection components from PyRadiomics and from the Medical Image Radiomics Processor (MIRP) to the radiomics automation pipeline. To validate our approach and demonstrate benefits from the feature-centered components, we comprehensively compared RPTK with results from WORC and Autoradiomics on six public benchmark data sets. We show that we can achieve higher performance by incorporating the proposed feature processing and selection techniques. Our results provide additional guidance in selecting suitable components for optimized radiomics analyses in clinical use cases such as treatment response prediction.
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Notes
- 1.
The license is comparable to the Creative Commons AttributionNonCommercial-ShareAlike 4.0 (CC BY-NC-SA) license, with the main adjustment that the data cannot be redistributed.
- 2.
Downloaded on the 17th of March 2023 from: xnat.bmia.nl/data/projects/worc.
- 3.
Accessed on 15th of May 2023: github.com/pwoznicki/radiomics-benchmark.
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Bohn, J.R. et al. (2023). RPTK: The Role of Feature Computation on Prediction Performance. In: Woo, J., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops. MICCAI 2023. Lecture Notes in Computer Science, vol 14394. Springer, Cham. https://doi.org/10.1007/978-3-031-47425-5_11
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