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@ARTICLE{Wennmann:302832,
      author       = {M. Wennmann$^*$ and J. Kächele$^*$ and A. von Salomon$^*$
                      and T. Nonnenmacher and M. Bujotzek$^*$ and S. Xiao$^*$ and
                      A. Martinez Mora$^*$ and T. Hielscher$^*$ and M. Hajiyianni
                      and E. Menis and M. Grözinger$^*$ and F. Bauer$^*$ and V.
                      Riebl and L. T. Rotkopf$^*$ and K. S. Zhang$^*$ and S. Afat
                      and B. Besemer and M. Hoffmann and A. Ringelstein and U.
                      Graeven and D. Fedders and M. Hänel and G. Antoch and R.
                      Fenk and A. H. Mahnken and C. Mann and T. Mokry and M.-S.
                      Raab and N. Weinhold and E. K. Mai and H. Goldschmidt and T.
                      F. Weber and S. Delorme$^*$ and P. Neher$^*$ and H.-P.
                      Schlemmer$^*$ and K. Maier-Hein$^*$},
      title        = {{A}utomated {D}etection of {F}ocal {B}one {M}arrow
                      {L}esions {F}rom {MRI}: {A} {M}ulti-center {F}easibility
                      {S}tudy in {P}atients with {M}onoclonal {P}lasma {C}ell
                      {D}isorders.},
      journal      = {Academic radiology},
      volume       = {nn},
      issn         = {1076-6332},
      address      = {Philadelphia, PA [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2025-01372},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:E010#LA:E010#LA:E230# / epub},
      abstract     = {To train and test an AI-based algorithm for automated
                      detection of focal bone marrow lesions (FL) from MRI.This
                      retrospective feasibility study included 444 patients with
                      monoclonal plasma cell disorders. For this feasibility
                      study, only FLs in the left pelvis were included. Using the
                      nnDetection framework, the algorithm was trained based on
                      334 patients with 494 FLs from center 1, and was tested on
                      an internal test set (36 patients, 89 FLs, center 1) and a
                      multicentric external test set (74 patients, 262 FLs,
                      centers 2-11). Mean average precision (mAP), F1-score,
                      sensitivity, positive predictive value (PPV), and Spearman
                      correlation coefficient between automatically determined and
                      actual number of FLs were calculated.On the
                      internal/external test set, the algorithm achieved a mAP of
                      0.44/0.34, F1-Score of 0.54/0.44, sensitivity of 0.49/0.34,
                      and a PPV of 0.61/0.61, respectively. In two subsets of the
                      external multicentric test set with high imaging quality,
                      the performance nearly matched that of the internal test
                      set, with mAP of 0.45/0.41, F1-Score of 0.50/0.53,
                      sensitivity of 0.44/0.43, and a PPV of 0.60/0.71,
                      respectively. There was a significant correlation between
                      the automatically determined and actual number of FLs on
                      both the internal (r=0.51, p=0.001) and external
                      multicentric test set (r=0.59, p<0.001).This study
                      demonstrates that the automated detection of FLs from MRI,
                      and thereby the automated assessment of the number of FLs,
                      is feasible.},
      keywords     = {AI (Other) / Detection (Other) / Focal lesions (Other) /
                      Monoclonal plasma cell disorders (Other) / Multicenter
                      (Other)},
      cin          = {E230 / C060 / HD01 / E010},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)E010-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40640054},
      doi          = {10.1016/j.acra.2025.06.034},
      url          = {https://inrepo02.dkfz.de/record/302832},
}