% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Bickelhaupt:125374,
      author       = {S. Bickelhaupt$^*$ and D. Paech$^*$ and P.
                      Kickingereder$^*$ and F. Steudle$^*$ and W. Lederer and H.
                      Daniel and M. Götz$^*$ and N. Gählert$^*$ and D. Tichy$^*$
                      and M. Wiesenfarth$^*$ and F. Laun$^*$ and K. Maier-Hein$^*$
                      and H.-P. Schlemmer$^*$ and D. Bonekamp$^*$},
      title        = {{P}rediction of malignancy by a radiomic signature from
                      contrast agent-free diffusion {MRI} in suspicious breast
                      lesions found on screening mammography.},
      journal      = {Journal of magnetic resonance imaging},
      volume       = {46},
      number       = {2},
      issn         = {1053-1807},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {DKFZ-2017-01507},
      pages        = {604 - 616},
      year         = {2017},
      abstract     = {To assess radiomics as a tool to determine how well lesions
                      found suspicious on breast cancer screening X-ray
                      mammography can be categorized into malignant and benign
                      with unenhanced magnetic resonance (MR) mammography with
                      diffusion-weighted imaging and T2 -weighted sequences.From
                      an asymptomatic screening cohort, 50 women with
                      mammographically suspicious findings were examined with
                      contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this
                      protocol an unenhanced, abbreviated diffusion-weighted
                      imaging protocol (ueMRI) including T2 -weighted, (T2 w),
                      diffusion-weighted imaging (DWI), and DWI with background
                      suppression (DWIBS) sequences and corresponding apparent
                      diffusion coefficient (ADC) maps were extracted. From
                      ueMRI-derived radiomic features, three Lasso-supervised
                      machine-learning classifiers were constructed and compared
                      with the clinical performance of a highly experienced
                      radiologist: 1) univariate mean ADC model, 2) unconstrained
                      radiomic model, 3) constrained radiomic model with mandatory
                      inclusion of mean ADC.The unconstrained and constrained
                      radiomic classifiers consisted of 11 parameters each and
                      achieved differentiation of malignant from benign lesions
                      with a .632 + bootstrap receiver operating
                      characteristics (ROC) area under the curve (AUC) of
                      $84.2\%/85.1\%,$ compared to $77.4\%$ for mean ADC and
                      $95.9\%/95.9\%$ for the experienced radiologist using
                      ceMRI/ueMRI.In this pilot study we identified two ueMRI
                      radiomics classifiers that performed well in the
                      differentiation of malignant from benign lesions and
                      achieved higher performance than the mean ADC parameter
                      alone. Classification was lower than the almost perfect
                      performance of a highly experienced breast radiologist. The
                      potential of radiomics to provide a training-independent
                      diagnostic decision tool is indicated. A performance
                      reaching the human expert would be highly desirable and
                      based on our results is considered possible when the concept
                      is extended in larger cohorts with further development and
                      validation of the technique.1 Technical Efficacy: Stage 2 J.
                      MAGN. RESON. IMAGING 2017;46:604-616.},
      cin          = {E010 / E012 / E132 / C060 / E020},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E012-20160331 /
                      I:(DE-He78)E132-20160331 / I:(DE-He78)C060-20160331 /
                      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:28152264},
      doi          = {10.1002/jmri.25606},
      url          = {https://inrepo02.dkfz.de/record/125374},
}