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@ARTICLE{Salome:274490,
      author       = {P. Salome$^*$ and F. Sforazzini$^*$ and G. Grugnara and A.
                      Kudak$^*$ and M. Dostal$^*$ and C. Herold-Mende and S.
                      Heiland and J. Debus$^*$ and A. Abdollahi$^*$ and M.
                      Knoll$^*$},
      title        = {{MR}-{C}lass: {A} {P}ython {T}ool for {B}rain {MR} {I}mage
                      {C}lassification {U}tilizing {O}ne-vs-{A}ll {DCNN}s to
                      {D}eal with the {O}pen-{S}et {R}ecognition {P}roblem.},
      journal      = {Cancers},
      volume       = {15},
      number       = {6},
      issn         = {2072-6694},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {DKFZ-2023-00633},
      pages        = {1820},
      year         = {2023},
      note         = {#EA:E210#LA:E210#},
      abstract     = {MR image classification in datasets collected from multiple
                      sources is complicated by inconsistent and missing DICOM
                      metadata. Therefore, we aimed to establish a method for the
                      efficient automatic classification of MR brain
                      sequences.Deep convolutional neural networks (DCNN) were
                      trained as one-vs-all classifiers to differentiate between
                      six classes: T1 weighted (w), contrast-enhanced T1w, T2w,
                      T2w-FLAIR, ADC, and SWI. Each classifier yields a
                      probability, allowing threshold-based and relative
                      probability assignment while excluding images with low
                      probability (label: unknown, open-set recognition problem).
                      Data from three high-grade glioma (HGG) cohorts was
                      assessed; C1 (320 patients, 20,101 MRI images) was used for
                      training, while C2 (197, 11,333) and C3 (256, 3522) were for
                      testing. Two raters manually checked images through an
                      interactive labeling tool. Finally, MR-Class' added value
                      was evaluated via radiomics model performance for
                      progression-free survival (PFS) prediction in C2, utilizing
                      the concordance index (C-I).Approximately $10\%$ of
                      annotation errors were observed in each cohort between the
                      DICOM series descriptions and the derived labels. MR-Class
                      accuracy was $96.7\%$ $[95\%$ Cl: 95.8, 97.3] for C2 and
                      $94.4\%$ [93.6, 96.1] for C3. A total of 620 images were
                      misclassified; manual assessment of those frequently showed
                      motion artifacts or alterations of anatomy by large tumors.
                      Implementation of MR-Class increased the PFS model C-I by
                      $14.6\%$ on average, compared to a model trained without
                      MR-Class.We provide a DCNN-based method for the sequence
                      classification of brain MR images and demonstrate its
                      usability in two independent HGG datasets.},
      keywords     = {artificial intelligence (AI) (Other) / content-based image
                      classification (Other) / convolutional neural networks (CNN)
                      (Other) / data curation and preparation (Other) / deep
                      learning (Other)},
      cin          = {E210 / HD01 / E050},
      ddc          = {610},
      cid          = {I:(DE-He78)E210-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)E050-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36980707},
      doi          = {10.3390/cancers15061820},
      url          = {https://inrepo02.dkfz.de/record/274490},
}