In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning
Abstract 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....
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Nature Portfolio
2025-08-01
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| Online Access: | https://doi.org/10.1038/s41598-025-09989-7 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Kevin Sun Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-6324e3a231f748aab83d97a5c6732eb0 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6324e3a231f748aab83d97a5c6732eb02025-08-20T03:04:29ZengNature PortfolioScientific Reports2045-23222025-08-0115111410.1038/s41598-025-09989-7In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioningKevin Sun Zhang0Christian Jan Oliver Neelsen1Markus Wennmann2Thomas Hielscher3Balint Kovacs4Philip Alexander Glemser5Magdalena Görtz6Albrecht Stenzinger7Klaus H. Maier-Hein8Johannes Huber9Heinz-Peter Schlemmer10David Bonekamp11German Cancer Research Center (DKFZ), Division of RadiologyGerman Cancer Research Center (DKFZ), Division of RadiologyGerman Cancer Research Center (DKFZ), Division of RadiologyGerman Cancer Research Center (DKFZ), Division of BiostatisticsGerman Cancer Research Center (DKFZ), Division of Medical Image ComputingGerman Cancer Research Center (DKFZ), Division of RadiologyDepartment of Urology, Heidelberg University HospitalInstitute of Pathology, Heidelberg University HospitalGerman Cancer Research Center (DKFZ), Division of Medical Image ComputingDepartment of Urology, Heidelberg University HospitalGerman Cancer Research Center (DKFZ), Division of RadiologyGerman Cancer Research Center (DKFZ), Division of RadiologyAbstract 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.https://doi.org/10.1038/s41598-025-09989-7ProstateMagnetic resonance imagingReproducibility of resultsObserver variationRadiomics |
| spellingShingle | 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 In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning Scientific Reports Prostate Magnetic resonance imaging Reproducibility of results Observer variation Radiomics |
| title | In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning |
| title_full | In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning |
| title_fullStr | In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning |
| title_full_unstemmed | In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning |
| title_short | In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning |
| title_sort | in vivo variability of mri radiomics features in prostate lesions assessed by a test retest study with repositioning |
| topic | Prostate Magnetic resonance imaging Reproducibility of results Observer variation Radiomics |
| url | https://doi.org/10.1038/s41598-025-09989-7 |
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