In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning
Despite academic success, radiomics-based machine learning algorithms have not reached clinical practice, partially due to limited repeatability/reproducibility. To address this issue, this work aims to identify a stable subset of radiomics features in prostate MRI for radiomics modelling. A prospec...
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| Hauptverfasser: | , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
13 August 2025
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| In: |
Scientific reports
Year: 2025, Jahrgang: 15, Pages: 1-14 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-025-09989-7 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41598-025-09989-7 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41598-025-09989-7 
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| Verfasserangaben: | Kevin Sun Zhang, Christian Jan Oliver Neelsen, Markus Wennmann, Thomas Hielscher, Balint Kovacs, Philip Alexander Glemser, Magdalena Görtz, Albrecht Stenzinger, Klaus H. Maier-Hein, Johannes Huber, Heinz-Peter Schlemmer & David Bonekamp |
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| 520 | |a Despite academic success, radiomics-based machine learning algorithms have not reached clinical practice, partially due to limited repeatability/reproducibility. To address this issue, this work aims to identify a stable subset of radiomics features in prostate MRI for radiomics modelling. A prospective study was conducted in 43 patients who received a clinical MRI examination and a research exam with repetition of T2-weighted and two different diffusion-weighted imaging (DWI) sequences with repositioning in between. Radiomics feature (RF) extraction was performed from MRI segmentations accounting for intra-rater and inter-rater effects, and three different image normalization methods were compared. Stability of RFs was assessed using the concordance correlation coefficient (CCC) for different comparisons: rater effects, inter-scan (before and after repositioning) and inter-sequence (between the two diffusion-weighted sequences) variability. In total, only 64 out of 321 (~ 20%) extracted features demonstrated stability, defined as CCC ≥ 0.75 in all settings (5 high-b value, 7 ADC- and 52 T2-derived features). For DWI, primarily intensity-based features proved stable with no shape feature passing the CCC threshold. T2-weighted images possessed the largest number of stable features with multiple shape (7), intensity-based (7) and texture features (28). Z-score normalization for high-b value images and muscle-normalization for T2-weighted images were identified as suitable. | ||
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