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The effect of preprocessing filters on predictive performance in radiomics
Open accessAbstract
Background: Radiomics is a noninvasive method using machine learning to support personalised medicine. Preprocessing filters such as wavelet and Laplacian-of-Gaussian filters are commonly used being thought to increase predictive performance. However, the use of preprocessing filters increases the number of features by up to an order of magnitude and can produce many correlated features. Both substantially increase the dataset complexity, which in turn makes modeling with machine learning techniques more challenging, possibly leading to poorer performance. We investigated the impact of these filters on predictive performance. Methods: Using seven publicly available radiomic datasets, we measured the impact of adding features preprocessed with eight different preprocessing filters to the unprocessed features on the predictive performance of radiomic models. Modeling was performed using five feature selection methods and five classifiers, while predictive performance was measured using area-under-the-curve at receiver operating characteristics analysis (AUC-ROC) with nested, stratified 10-fold cross-validation. Results: Significant improvements of up to 0.08 in AUC-ROC were observed when all image preprocessing filters were applied compared to using only the original features (up to p = 0.024). Decreases of -0.04 and -0.10 were observed on some data sets, but these were not statistically significant ( p > 0.179). Tuning of the image preprocessing filters did not result in decreases in AUC-ROC but further improved results by up to 0.1; however, these improvements were not statistically significant ( p > 0.086) except for one data set ( p = 0.023). Conclusions: Preprocessing filters can have a significant impact on the predictive performance and should be used in radiomic studies.
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Some studies reported that use of pre-processing image filters improved predictive performance of models.
“Previously, some studies reported the use of pre-processing image filters was shown to improve predictive performance of models [47, 48].”
From Radiomics-based prediction of FIGO grade for placenta accreta spectrum (2023) · cites this paper as [47]
When datasets are small, limiting analysis to radiomic features from the original image only showed similar results to when all radiomic features with image filters were included.
“However, when datasets are small, limiting the analysis to radiomic features from the original image only showed similar results to when all radiomic features with image filters were included [47].”
From Radiomics-based prediction of FIGO grade for placenta accreta spectrum (2023) · cites this paper as [47]
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Metadata
- Authors
- Demircioğlu, A.
- ORCID
- Demircioğlu, Aydin
- Venue
- European Radiology Experimental
- Published
- 2022-09-01
- Volume
- 6
- Issue
- 1
- Pages
- 40-40
- ISSN
- 2509-9280 (ISSN-L)
- PMC
- PMC9433552
- OpenAlex
- W4294123628
- Cited by
- 75
- References
- 26
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 28 · 2023: 14 · 2022: 1
- FWCI
- 8.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- European Radiology Experimental (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DuEPublico (University of Duisburg-Essen) (submittedVersion, cc-by)
- Funders
- Universitätsklinikum Essen
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Keywords
- radiomics, wavelet filters, Laplacian of Gaussian, predictive performance, feature selection, feature correlation, filter tuning, radiomic feature extraction
- MeSH terms
- Machine Learning, ROC Curve
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