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@ARTICLE{Aldraimli:180190,
      author       = {M. Aldraimli and S. Osman and D. Grishchuck and S. Ingram
                      and R. Lyon and A. Mistry and J. Oliveira and R. Samuel and
                      L. E. A. Shelley and D. Soria and M. V. Dwek and M. E.
                      Aguado-Barrera and D. Azria and J. Chang-Claude$^*$ and A.
                      Dunning and A. Giraldo and S. Green and S.
                      Gutiérrez-Enríquez and C. Herskind and H. van Hulle and M.
                      Lambrecht and L. Lozza and T. Rancati and V. Reyes and B. S.
                      Rosenstein and D. de Ruysscher and M. C. de Santis and P.
                      Seibold$^*$ and E. Sperk and R. P. Symonds and H. Stobart
                      and B. Taboada-Valadares and C. J. Talbot and V. J. L.
                      Vakaet and A. Vega and L. Veldeman and M. R. Veldwijk and A.
                      Webb and C. Weltens and C. M. West and T. J. Chaussalet and
                      T. Rattay},
      collaboration = {R. consortium},
      title        = {{D}evelopment and {O}ptimization of a {M}achine-{L}earning
                      {P}rediction {M}odel for {A}cute {D}esquamation {A}fter
                      {B}reast {R}adiation {T}herapy in the {M}ulticenter
                      {REQUITE} {C}ohort.},
      journal      = {Advances in radiation oncology},
      volume       = {7},
      number       = {3},
      issn         = {2452-1094},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2022-01162},
      pages        = {100890},
      year         = {2022},
      abstract     = {Some patients with breast cancer treated by surgery and
                      radiation therapy experience clinically significant
                      toxicity, which may adversely affect cosmesis and quality of
                      life. There is a paucity of validated clinical prediction
                      models for radiation toxicity. We used machine learning (ML)
                      algorithms to develop and optimise a clinical prediction
                      model for acute breast desquamation after whole breast
                      external beam radiation therapy in the prospective
                      multicenter REQUITE cohort study.Using demographic and
                      treatment-related features (m = 122) from patients (n =
                      2058) at 26 centers, we trained 8 ML algorithms with 10-fold
                      cross-validation in a 50:50 random-split data set with class
                      stratification to predict acute breast desquamation. Based
                      on performance in the validation data set, the logistic
                      model tree, random forest, and naïve Bayes models were
                      taken forward to cost-sensitive learning optimisation.One
                      hundred and ninety-two patients experienced acute
                      desquamation. Resampling and cost-sensitive learning
                      optimisation facilitated an improvement in classification
                      performance. Based on maximising sensitivity (true
                      positives), the 'hero' model was the cost-sensitive random
                      forest algorithm with a false-negative: false-positive
                      misclassification penalty of 90:1 containing m = 114
                      predictive features. Model sensitivity and specificity were
                      0.77 and 0.66, respectively, with an area under the curve of
                      0.77 in the validation cohort.ML algorithms with resampling
                      and cost-sensitive learning generated clinically valid
                      prediction models for acute desquamation using patient
                      demographic and treatment features. Further external
                      validation and inclusion of genomic markers in ML prediction
                      models are worthwhile, to identify patients at increased
                      risk of toxicity who may benefit from supportive
                      intervention or even a change in treatment plan.},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35647396},
      pmc          = {pmc:PMC9133391},
      doi          = {10.1016/j.adro.2021.100890},
      url          = {https://inrepo02.dkfz.de/record/180190},
}