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@ARTICLE{Wennmann:182096,
      author       = {M. Wennmann$^*$ and A. Klein$^*$ and F. Bauer$^*$ and J.
                      Chmelik$^*$ and M. Grözinger$^*$ and C. Uhlenbrock$^*$ and
                      J. Lochner$^*$ and T. Nonnenmacher and L. T. Rotkopf$^*$ and
                      S. Sauer and T. Hielscher$^*$ and M. Götz$^*$ and R. O.
                      Floca$^*$ and P. Neher$^*$ and D. Bonekamp$^*$ and J.
                      Hillengass and J. Kleesiek and N. Weinhold and T. F. Weber
                      and H. Goldschmidt$^*$ and S. Delorme$^*$ and K.
                      Maier-Hein$^*$ and H.-P. Schlemmer$^*$},
      title        = {{C}ombining {D}eep {L}earning and {R}adiomics for
                      {A}utomated, {O}bjective, {C}omprehensive {B}one {M}arrow
                      {C}haracterization {F}rom {W}hole-{B}ody {MRI}: {A}
                      {M}ulticentric {F}easibility {S}tudy.},
      journal      = {Investigative radiology},
      volume       = {57},
      number       = {11},
      issn         = {0020-9996},
      address      = {[s.l.]},
      publisher    = {Ovid},
      reportid     = {DKFZ-2022-02414},
      pages        = {752 - 763},
      year         = {2022},
      note         = {#EA:E010#EA:E230#LA:E010#LA:E230#},
      abstract     = {Disseminated bone marrow (BM) involvement is frequent in
                      multiple myeloma (MM). Whole-body magnetic resonance imaging
                      (wb-MRI) enables to evaluate the whole BM. Reading of such
                      whole-body scans is time-consuming, and yet radiologists can
                      transfer only a small fraction of the information of the
                      imaging data set to the report. This limits the influence
                      that imaging can have on clinical decision-making and in
                      research toward precision oncology. The objective of this
                      feasibility study was to implement a concept for automatic,
                      comprehensive characterization of the BM from wb-MRI, by
                      automatic BM segmentation and subsequent radiomics analysis
                      of 30 different BM spaces (BMS).This retrospective
                      multicentric pilot study used a total of 106 wb-MRI from 102
                      patients with (smoldering) MM from 8 centers. Fifty wb-MRI
                      from center 1 were used for training of segmentation
                      algorithms (nnU-Nets) and radiomics algorithms. Fifty-six
                      wb-MRI from 8 centers, acquired with a variety of different
                      MRI scanners and protocols, were used for independent
                      testing. Manual segmentations of 2700 BMS from 90 wb-MRI
                      were performed for training and testing of the segmentation
                      algorithms. For each BMS, 296 radiomics features were
                      calculated individually. Dice score was used to assess
                      similarity between automatic segmentations and manual
                      reference segmentations.The 'multilabel nnU-Net'
                      segmentation algorithm, which performs segmentation of 30
                      BMS and labels them individually, reached mean dice scores
                      of 0.88 ± 0.06/0.87 ± 0.06/0.83 ± 0.11 in independent
                      test sets from center 1/center 2/center 3-8 (interrater
                      variability between radiologists, 0.88 ± 0.01). The subset
                      from the multicenter, multivendor test set (center 3-8) that
                      was of high imaging quality was segmented with high
                      precision (mean dice score, 0.87), comparable to the
                      internal test data from center 1. The radiomic BM phenotype
                      consisting of 8880 descriptive parameters per patient, which
                      result from calculation of 296 radiomics features for each
                      of the 30 BMS, was calculated for all patients. Exemplary
                      cases demonstrated connections between typical BM patterns
                      in MM and radiomic signatures of the respective BMS. In
                      plausibility tests, predicted size and weight based on
                      radiomics models of the radiomic BM phenotype significantly
                      correlated with patients' actual size and weight ( P = 0.002
                      and P = 0.003, respectively).This pilot study demonstrates
                      the feasibility of automatic, objective, comprehensive BM
                      characterization from wb-MRI in multicentric data sets. This
                      concept allows the extraction of high-dimensional phenotypes
                      to capture the complexity of disseminated BM disorders from
                      imaging. Further studies need to assess the clinical
                      potential of this method for automatic staging, therapy
                      response assessment, or prediction of biopsy results.},
      keywords     = {Bone Marrow: diagnostic imaging / Deep Learning /
                      Feasibility Studies / Humans / Magnetic Resonance Imaging:
                      methods / Neoplasms / Pilot Projects / Precision Medicine /
                      Retrospective Studies / Whole Body Imaging},
      cin          = {E010 / E230 / C060},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)C060-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35640004},
      doi          = {10.1097/RLI.0000000000000891},
      url          = {https://inrepo02.dkfz.de/record/182096},
}