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@ARTICLE{Gabrys:141833,
      author       = {H. Gabrys$^*$ and F. Buettner and F. Sterzing$^*$ and H.
                      Hauswald$^*$ and M. Bangert$^*$},
      title        = {{D}esign and {S}election of {M}achine {L}earning {M}ethods
                      {U}sing {R}adiomics and {D}osiomics for {N}ormal {T}issue
                      {C}omplication {P}robability {M}odeling of {X}erostomia.},
      journal      = {Frontiers in oncology},
      volume       = {8},
      issn         = {2234-943X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2018-02101},
      pages        = {35},
      year         = {2018},
      abstract     = {The purpose of this study is to investigate whether machine
                      learning with dosiomic, radiomic, and demographic features
                      allows for xerostomia risk assessment more precise than
                      normal tissue complication probability (NTCP) models based
                      on the mean radiation dose to parotid glands.A cohort of 153
                      head-and-neck cancer patients was used to model xerostomia
                      at 0-6 months (early), 6-15 months (late),
                      15-24 months (long-term), and at any time (a longitudinal
                      model) after radiotherapy. Predictive power of the features
                      was evaluated by the area under the receiver operating
                      characteristic curve (AUC) of univariate logistic regression
                      models. The multivariate NTCP models were tuned and tested
                      with single and nested cross-validation, respectively. We
                      compared predictive performance of seven classification
                      algorithms, six feature selection methods, and ten data
                      cleaning/class balancing techniques using the Friedman test
                      and the Nemenyi post hoc analysis.NTCP models based on the
                      parotid mean dose failed to predict xerostomia
                      (AUCs < 0.60). The most informative predictors were
                      found for late and long-term xerostomia. Late xerostomia
                      correlated with the contralateral dose gradient in the
                      anterior-posterior (AUC = 0.72) and the right-left
                      (AUC = 0.68) direction, whereas long-term xerostomia was
                      associated with parotid volumes (AUCs > 0.85), dose
                      gradients in the right-left (AUCs > 0.78), and the
                      anterior-posterior (AUCs > 0.72) direction. Multivariate
                      models of long-term xerostomia were typically based on the
                      parotid volume, the parotid eccentricity, and the
                      dose-volume histogram (DVH) spread with the generalization
                      AUCs ranging from 0.74 to 0.88. On average, support vector
                      machines and extra-trees were the top performing
                      classifiers, whereas the algorithms based on logistic
                      regression were the best choice for feature selection. We
                      found no advantage in using data cleaning or class balancing
                      methods.We demonstrated that incorporation of organ- and
                      dose-shape descriptors is beneficial for xerostomia
                      prediction in highly conformal radiotherapy treatments. Due
                      to strong reliance on patient-specific, dose-independent
                      factors, our results underscore the need for development of
                      personalized data-driven risk profiles for NTCP models of
                      xerostomia. The facilitated machine learning pipeline is
                      described in detail and can serve as a valuable reference
                      for future work in radiomic and dosiomic NTCP modeling.},
      cin          = {E040 / E050},
      ddc          = {610},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)E050-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29556480},
      pmc          = {pmc:PMC5844945},
      doi          = {10.3389/fonc.2018.00035},
      url          = {https://inrepo02.dkfz.de/record/141833},
}