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@ARTICLE{Zhang:303498,
      author       = {K. S. Zhang$^*$ and C. J. O. Neelsen$^*$ and M.
                      Wennmann$^*$ and T. Hielscher$^*$ and B. Kovacs$^*$ and P.
                      A. Glemser$^*$ and M. Görtz$^*$ and A. Stenzinger and K. H.
                      Maier-Hein$^*$ and J. Huber and H.-P. Schlemmer$^*$ and D.
                      Bonekamp$^*$},
      title        = {{I}n vivo variability of {MRI} radiomics features in
                      prostate lesions assessed by a test-retest study with
                      repositioning.},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DKFZ-2025-01689},
      pages        = {29703},
      year         = {2025},
      note         = {#EA:E010#LA:E010#},
      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.},
      keywords     = {Magnetic resonance imaging (Other) / Observer variation
                      (Other) / Prostate (Other) / Radiomics (Other) /
                      Reproducibility of results (Other)},
      cin          = {E010 / C060 / E230 / E250},
      ddc          = {600},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)E230-20160331 / I:(DE-He78)E250-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40804076},
      doi          = {10.1038/s41598-025-09989-7},
      url          = {https://inrepo02.dkfz.de/record/303498},
}