ArticleEuropean radiology experimental2022
The effect of preprocessing filters on predictive performance in radiomics.
Article in European radiology experimental, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Are deep models in radiomics performing better than generic models? A systematic review.European radiology experimental · 2023Pooled it
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- Interpretable Deep Learning Radiomics Model for Preoperative Prediction of High-Grade Soft Tissue Sarcomas: A Multicenter MRI Study.Journal of imaging informatics in medicine · 2026Article
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
- A practical 10-step recipe for conducting radiomic studies.European radiology experimental · 2026Review
- Multi-Phasic CECT Peritumoral Radiomics Predict Treatment Response to Bevacizumab-Based Chemotherapy in RAS-Mutated Colorectal Liver Metastases.Bioengineering (Basel, Switzerland) · 2026Article
- Using Radiomics and Explainable Ensemble Learning to Predict Radiation Pneumonitis and Survival in NSCLC Patients Post-VMAT.Life (Basel, Switzerland) · 2025Article
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Enhanced diagnosis of osteoporosis using vision transformer with lumbar MRI.BMC medical imaging · 2025Article
- Multiomic random forest toxicity modeling of radiation esophagitis.Physics and imaging in radiation oncology · 2025Article
- Benchmarking feature projection methods in radiomics.Scientific reports · 2025Article
- Rethinking feature reproducibility in radiomics: the elephant in the dark.European radiology experimental · 2025Article
- Segmentation variability and radiomics stability for predicting triple-negative breast cancer subtype using magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Reproducibility and interpretability in radiomics: a critical assessmentDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- A comprehensive study on the radiomic score derived from perineural invasion in gastric cancer and its correlation with the overall survival of patients.La Radiologia medica · 2025Article
- ESR Essentials: radiomics-practice recommendations by the European Society of Medical Imaging Informatics.European radiology · 2025Review
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
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Abstract
backgroundRadiomics 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.
methodsUsing 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.
resultsSignificant 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).
conclusionsPreprocessing filters can have a significant impact on the predictive performance and should be used in radiomic studies.
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