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@ARTICLE{Bickelhaupt:135948,
      author       = {S. Bickelhaupt$^*$ and P. F. Jaeger$^*$ and F. Laun$^*$ and
                      W. Lederer$^*$ and H. Daniel and T. A. Kuder$^*$ and L.
                      Wuesthof and D. Paech$^*$ and D. Bonekamp$^*$ and A.
                      Radbruch$^*$ and S. Delorme$^*$ and H.-P. Schlemmer$^*$ and
                      F. H. Steudle and K. Maier-Hein$^*$},
      title        = {{R}adiomics {B}ased on {A}dapted {D}iffusion {K}urtosis
                      {I}maging {H}elps to {C}larify {M}ost {M}ammographic
                      {F}indings {S}uspicious for {C}ancer.},
      journal      = {Radiology},
      volume       = {287},
      number       = {3},
      issn         = {1527-1315},
      address      = {Oak Brook, Ill.},
      publisher    = {Soc.},
      reportid     = {DKFZ-2018-00685},
      pages        = {761 - 770},
      year         = {2018},
      abstract     = {Purpose To evaluate a radiomics model of Breast Imaging
                      Reporting and Data System (BI-RADS) 4 and 5 breast lesions
                      extracted from breast-tissue-optimized kurtosis magnetic
                      resonance (MR) imaging for lesion characterization by using
                      a sensitivity threshold similar to that of biopsy. Materials
                      and Methods This institutional study included 222 women at
                      two independent study sites (site 1: training set of 95
                      patients; mean age ± standard deviation, 58.6 years ± 6.6;
                      61 malignant and 34 benign lesions; site 2: independent test
                      set of 127 patients; mean age, 58.2 years ± 6.8; 61
                      malignant and 66 benign lesions). All women presented with a
                      finding suspicious for cancer at x-ray mammography (BI-RADS
                      4 or 5) and an indication for biopsy. Before biopsy,
                      diffusion-weighted MR imaging (b values, 0-1500 sec/mm2) was
                      performed by using 1.5-T imagers from different MR imaging
                      vendors. Lesions were segmented and voxel-based kurtosis
                      fitting adapted to account for fat signal contamination was
                      performed. A radiomics feature model was developed by using
                      a random forest regressor. The fixed model was tested on an
                      independent test set. Conventional interpretations of MR
                      imaging were also assessed for comparison. Results The
                      radiomics feature model reduced false-positive results from
                      66 to 20 (specificity $70.0\%$ [46 of 66]) at the predefined
                      sensitivity of greater than $98.0\%$ [60 of 61] in the
                      independent test set, with BI-RADS 4a and 4b lesions
                      benefiting from the analysis (specificity $74.0\%,$ [37 of
                      50]; $60.0\%$ [nine of 15]) and BI-RADS 5 lesions showing no
                      added benefit. The model significantly improved specificity
                      compared with the median apparent diffusion coefficient (P <
                      .001) and apparent kurtosis coefficient (P = .02) alone.
                      Conventional reading of dynamic contrast material-enhanced
                      MR imaging provided sensitivity of $91.8\%$ (56 of 61) and a
                      specificity of $74.2\%$ (49 of 66). Accounting for fat
                      signal intensity during fitting significantly improved the
                      area under the curve of the model (P = .001). Conclusion A
                      radiomics model based on kurtosis diffusion-weighted imaging
                      performed by using MR imaging machines from different
                      vendors allowed for reliable differentiation between
                      malignant and benign breast lesions in both a training and
                      an independent test data set. © RSNA, 2018 Online
                      supplemental material is available for this article.},
      cin          = {E010 / E230 / E132 / E020},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E230-20160331 /
                      I:(DE-He78)E132-20160331 / I:(DE-He78)E020-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29461172},
      doi          = {10.1148/radiol.2017170273},
      url          = {https://inrepo02.dkfz.de/record/135948},
}