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@ARTICLE{Wennmann:276232,
      author       = {M. Wennmann$^*$ and W. Ming$^*$ and F. Bauer$^*$ and J.
                      Chmelik$^*$ and A. Klein$^*$ and C. Uhlenbrock$^*$ and M.
                      Grözinger$^*$ and K.-C. Kahl$^*$ and T. Nonnenmacher and M.
                      Debic and T. Hielscher$^*$ and H. Thierjung$^*$ and L. T.
                      Rotkopf$^*$ and N. Stanczyk$^*$ and S. Sauer and A. Jauch
                      and M. Götz$^*$ and F. T. Kurz$^*$ and K. Schlamp and M.
                      Horger and S. Afat and B. Besemer and M. Hoffmann and J.
                      Hoffend and D. Kraemer and U. Graeven and A. Ringelstein and
                      D. Bonekamp$^*$ and J. Kleesiek$^*$ and R. O. Floca$^*$ and
                      J. Hillengass and E. K. Mai and N. Weinhold and T. F. Weber
                      and H. Goldschmidt and H.-P. Schlemmer$^*$ and K.
                      Maier-Hein$^*$ and S. Delorme$^*$ and P. Neher$^*$},
      title        = {{P}rediction of {B}one {M}arrow {B}iopsy {R}esults {F}rom
                      {MRI} in {M}ultiple {M}yeloma {P}atients {U}sing {D}eep
                      {L}earning and {R}adiomics.},
      journal      = {Investigative radiology},
      volume       = {58},
      number       = {10},
      issn         = {0020-9996},
      address      = {[Erscheinungsort nicht ermittelbar]},
      publisher    = {Ovid},
      reportid     = {DKFZ-2023-01041},
      pages        = {754-765},
      year         = {2023},
      note         = {#EA:E010#LA:E230#EA:E010#LA:E230# / 2023 Oct
                      1;58(10):754-765},
      abstract     = {In multiple myeloma and its precursor stages, plasma cell
                      infiltration (PCI) and cytogenetic aberrations are important
                      for staging, risk stratification, and response assessment.
                      However, invasive bone marrow (BM) biopsies cannot be
                      performed frequently and multifocally to assess the
                      spatially heterogenous tumor tissue. Therefore, the goal of
                      this study was to establish an automated framework to
                      predict local BM biopsy results from magnetic resonance
                      imaging (MRI).This retrospective multicentric study used
                      data from center 1 for algorithm training and internal
                      testing, and data from center 2 to 8 for external testing.
                      An nnU-Net was trained for automated segmentation of pelvic
                      BM from T1-weighted whole-body MRI. Radiomics features were
                      extracted from these segmentations, and random forest models
                      were trained to predict PCI and the presence or absence of
                      cytogenetic aberrations. Pearson correlation coefficient and
                      the area under the receiver operating characteristic were
                      used to evaluate the prediction performance for PCI and
                      cytogenetic aberrations, respectively.A total of 672 MRIs
                      from 512 patients (median age, 61 years; interquartile
                      range, 53-67 years; 307 men) from 8 centers and 370
                      corresponding BM biopsies were included. The predicted PCI
                      from the best model was significantly correlated (P ≤
                      0.01) to the actual PCI from biopsy in all internal and
                      external test sets (internal test set: r = 0.71 [0.51,
                      0.83]; center 2, high-quality test set: r = 0.45 [0.12,
                      0.69]; center 2, other test set: r = 0.30 [0.07, 0.49];
                      multicenter test set: r = 0.57 [0.30, 0.76]). The areas
                      under the receiver operating characteristic of the
                      prediction models for the different cytogenetic aberrations
                      ranged from 0.57 to 0.76 for the internal test set, but no
                      model generalized well to all 3 external test sets.The
                      automated image analysis framework established in this study
                      allows for noninvasive prediction of a surrogate parameter
                      for PCI, which is significantly correlated to the actual PCI
                      from BM biopsy.},
      cin          = {E010 / E230 / C060 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)C060-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37222527},
      doi          = {10.1097/RLI.0000000000000986},
      url          = {https://inrepo02.dkfz.de/record/276232},
}