title: Which melanoma patient carries a BRAF-mutation? A comparison of predictive models.
abstract: BACKGROUND
  In patients with advanced melanoma the detection of BRAF mutations is considered mandatory before the initiation of an expensive treatment with BRAF/MEK inhibitors. Sometimes it is difficult to perform such an analysis if archival tumor tissue is not available and fresh tissue has to be collected.
  
  
  RESULTS
  514 of 1170 patients (44%) carried a BRAF mutation. All models revealed age and histological subtype of melanoma as the two major predictive variables. Accuracy ranged from 0.65-0.71, being best in the random forest model. Sensitivity ranged 0.76-0.84, again best in the random forest model. Specificity was low in all models ranging 0.51-0.55.
  
  
  METHODS
  We collected the clinical data and mutational status of 1170 patients with advanced melanoma and established three different predictive models (binary logistic regression, classification and regression trees, and random forest) to forecast the BRAF status.
  
  
  CONCLUSIONS
  Up to date statistical models are not able to predict BRAF mutations in an acceptable accuracy. The analysis of the mutational status by sequencing or immunohistochemistry must still be considered as standard of care.
authors.affiliation: Department of Dermatology, Center for Dermato Oncology, University Medical Center Tübingen, Tübingen, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Eigentler
authors.affiliation: Department of Dermatology, Center for Dermato Oncology, University Medical Center Tübingen, Tübingen, Germany.
authors.forename: Zeinab
authors.initials: Z
authors.lastname: Assi
authors.affiliation: Department of Dermatology and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany.
authors.forename: Jessica C
authors.initials: JC
authors.lastname: Hassel
authors.affiliation: Department of Dermatology, University Hospital Erlangen, Erlangen, Germany.
authors.forename: Lucie
authors.initials: L
authors.lastname: Heinzerling
authors.affiliation: Department of Dermatology and Allergology, Klinikum Augsburg, Augsburg, Germany.
authors.forename: Hans
authors.initials: H
authors.lastname: Starz
authors.affiliation: Department of Dermatology, University of Regensburg, Regensburg, Germany.
authors.forename: Mark
authors.initials: M
authors.lastname: Berneburg
authors.affiliation: Department of Dermatology, Center for Dermato Oncology, University Medical Center Tübingen, Tübingen, Germany.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bauer
authors.affiliation: Department of Dermatology, Center for Dermato Oncology, University Medical Center Tübingen, Tübingen, Germany.
authors.forename: Claus
authors.initials: C
authors.lastname: Garbe
mesh_terms: D000328:Adult; D000368:Aged; D004252:DNA Mutational Analysis / Q000379:methods; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008545:Melanoma / Q000175:diagnosis / Q000235:genetics*; D008875:Middle Aged; D009154:Mutation; D049451:Nomograms; D048493:Proto-Oncogene Proteins B-raf / Q000235:genetics*; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000175:diagnosis / Q000235:genetics*; D013223:Statistics as Topic / Q000379:methods
chemical_list: D048493:Proto-Oncogene Proteins B-raf
keywords: BRAF; advanced melanoma; binary logistic regression; classification and regression trees; predictive models