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@ARTICLE{Wennmann:182154,
      author       = {M. Wennmann$^*$ and P. Neher$^*$ and N. Stanczyk$^*$ and
                      K.-C. Kahl$^*$ and J. Kächele$^*$ and V. Weru$^*$ and T.
                      Hielscher$^*$ and M. Grözinger$^*$ and J. Chmelik$^*$ and
                      K. S. Zhang$^*$ and F. Bauer$^*$ and T. Nonnenmacher and M.
                      Debic and S. Sauer and L. T. Rotkopf$^*$ and A. Jauch and K.
                      Schlamp and E. K. Mai and N. Weinhold and S. Afat and M.
                      Horger and H. Goldschmidt and H.-P. Schlemmer$^*$ and T. F.
                      Weber and S. Delorme$^*$ and F. T. Kurz$^*$ and K.
                      Maier-Hein$^*$},
      title        = {{D}eep {L}earning for {A}utomatic {B}one {M}arrow
                      {A}pparent {D}iffusion {C}oefficient {M}easurements {F}rom
                      {W}hole-{B}ody {M}agnetic {R}esonance {I}maging in
                      {P}atients {W}ith {M}ultiple {M}yeloma: {A} {R}etrospective
                      {M}ulticenter {S}tudy.},
      journal      = {Investigative radiology},
      volume       = {58},
      number       = {4},
      issn         = {0020-9996},
      address      = {[s.l.]},
      publisher    = {Ovid},
      reportid     = {DKFZ-2022-02465},
      pages        = {273-282},
      year         = {2023},
      note         = {#EA:E010#LA:E010#LA:E230# / 2023 Apr 1;58(4):273-282},
      abstract     = {Diffusion-weighted magnetic resonance imaging (MRI) is
                      increasingly important in patients with multiple myeloma
                      (MM). The objective of this study was to train and test an
                      algorithm for automatic pelvic bone marrow analysis from
                      whole-body apparent diffusion coefficient (ADC) maps in
                      patients with MM, which automatically segments pelvic bones
                      and subsequently extracts objective, representative ADC
                      measurements from each bone.In this retrospective
                      multicentric study, 180 MRIs from 54 patients were annotated
                      (semi)manually and used to train an nnU-Net for automatic,
                      individual segmentation of the right hip bone, the left hip
                      bone, and the sacral bone. The quality of the automatic
                      segmentation was evaluated on 15 manually segmented
                      whole-body MRIs from 3 centers using the dice score. In 3
                      independent test sets from 3 centers, which comprised a
                      total of 312 whole-body MRIs, agreement between
                      automatically extracted mean ADC values from the nnU-Net
                      segmentation and manual ADC measurements from 2 independent
                      radiologists was evaluated. Bland-Altman plots were
                      constructed, and absolute bias, relative bias to mean,
                      limits of agreement, and coefficients of variation were
                      calculated. In 56 patients with newly diagnosed MM who had
                      undergone bone marrow biopsy, ADC measurements were
                      correlated with biopsy results using Spearman
                      correlation.The ADC-nnU-Net achieved automatic segmentations
                      with mean dice scores of 0.92, 0.93, and 0.85 for the right
                      pelvis, the left pelvis, and the sacral bone, whereas the
                      interrater experiment gave mean dice scores of 0.86, 0.86,
                      and 0.77, respectively. The agreement between radiologists'
                      manual ADC measurements and automatic ADC measurements was
                      as follows: the bias between the first reader and the
                      automatic approach was 49 × 10-6 mm2/s, 7 × 10-6 mm2/s,
                      and -58 × 10-6 mm2/s, and the bias between the second
                      reader and the automatic approach was 12 × 10-6 mm2/s, 2 ×
                      10-6 mm2/s, and -66 × 10-6 mm2/s for the right pelvis, the
                      left pelvis, and the sacral bone, respectively. The bias
                      between reader 1 and reader 2 was 40 × 10-6 mm2/s, 8 ×
                      10-6 mm2/s, and 7 × 10-6 mm2/s, and the mean absolute
                      difference between manual readers was 84 × 10-6 mm2/s, 65
                      × 10-6 mm2/s, and 75 × 10-6 mm2/s. Automatically extracted
                      ADC values significantly correlated with bone marrow plasma
                      cell infiltration (R = 0.36, P = 0.007).In this study, a
                      nnU-Net was trained that can automatically segment pelvic
                      bone marrow from whole-body ADC maps in multicentric data
                      sets with a quality comparable to manual segmentations. This
                      approach allows automatic, objective bone marrow ADC
                      measurements, which agree well with manual ADC measurements
                      and can help to overcome interrater variability or
                      nonrepresentative measurements. Automatically extracted ADC
                      values significantly correlate with bone marrow plasma cell
                      infiltration and might be of value for automatic staging,
                      risk stratification, or therapy response assessment.},
      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:36256790},
      doi          = {10.1097/RLI.0000000000000932},
      url          = {https://inrepo02.dkfz.de/record/182154},
}