ArticleEuropean radiology2025
Multicollinearity and redundancy of the PET radiomic feature set.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- MRI-based radiomics analysis and machine learning in the differentiation of endometriomas from hemorrhagic ovarian cysts.European journal of radiology open · 2026Article
- CT radiomics showed no improvement beyond volume dynamics for early lesion-level size response to immunotherapy in metastatic melanoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Observational
- Development and validation of an interpretable CT-based scoring model for gastric cancer aggressiveness.Abdominal radiology (New York) · 2026Article
- Pseudotime-Based derivation of a PET-Based metabolic progression index for prognostic stratification in Extensive-Stage SCLC.Annals of nuclear medicine · 2026Article
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16 authors.
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Abstract
introductionThe aim of this study was to map multicollinearity of the radiomic feature set in five independent [
methodsFive [
resultsSeventy-two and 90 out of 105 features showed a (very) strong correlation with another feature in the correlation matrix in all five cohorts. Cluster analysis resulted in 35 and 15 non-redundant features at thresholds of ρ = 0.9 and ρ = 0.7, including 6 and 3 shape features, 4 and 2 intensity features, and 25 and 10 texture features, respectively. Seventy or 90 redundant features could be omitted at these thresholds, respectively.
conclusionAt least two-thirds of the radiomic feature set could be omitted because of strong multicollinearity in multiple independent cohorts. More redundant features could be identified using a less conservative threshold. Future research should indicate whether multicollinearity of the radiomic feature set is similar for other radiopharmaceuticals and imaging modalities. KEY POINTS: Question Radiomic feature sets contain many strongly correlating features, which results in statistical challenges. Findings Analysis of the correlation matrices showed that the same radiomic features were strongly correlated in five independent [
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