title: Integrating static and dynamic features of melanoma: the DynaMel algorithm.
abstract: BACKGROUND
  Sequential digital dermatoscopy identifies dynamic changes in melanocytic lesions. However, no algorithm exists that systematically weights dynamic changes regarding their association with melanoma.
  
  
  OBJECTIVE
  We sought to identify relevant dynamic changes and to integrate these into a novel diagnostic algorithm.
  
  
  METHODS
  During follow-up (mean 44.28 months) of 688 patients at high risk, 675 pigmented lesions with prospectively documented dynamic changes were excised. The association between specific changes and melanoma was assessed.
  
  
  RESULTS
  We detected 61 melanomas (38 invasive, median thickness 0.42 mm) with dynamic changes. Multivariate logistic regression analyses revealed a significant association between the diagnosis of melanoma and 5 dynamic criteria. According to the observed odds ratios we defined two dynamic major criteria (2 points each: asymmetric-multifocal enlargement and architectural change) and 3 dynamic minor criteria (1 point each: focal increase in pigmentation, focal decrease in pigmentation, and overall decrease in pigmentation when not accompanied by a lighter pigmentation of the adjacent skin). The DynaMel score was generated by addition of dynamic and 7-point checklist scores with a threshold for excision of 3 or more points. Including information about dynamic changes increased the sensitivity of the 7-point checklist from 47.5% (29 of 61 melanomas detected) to 77.1% (47 of 61 melanomas detected). The specificity slightly decreased from 99.0% to 98.1%.
  
  
  CONCLUSIONS
  Before broad application the DynaMel algorithm needs to be validated using data from a different prospective study.
  
  
  CONCLUSIONS
  The DynaMel algorithm integrates a scoring system for dynamic dermatoscopic changes into the 7-point checklist for dermatoscopy and thereby increased the sensitivity of melanoma detection.
authors.affiliation: Department of Dermatology, Georg August University Göttingen, Göttingen, Germany.
authors.forename: Timo
authors.initials: T
authors.lastname: Buhl
authors.forename: Christian
authors.initials: C
authors.lastname: Hansen-Hagge
authors.forename: Bianca
authors.initials: B
authors.lastname: Korpas
authors.forename: Kjell M
authors.initials: KM
authors.lastname: Kaune
authors.forename: Ellen
authors.initials: E
authors.lastname: Haas
authors.forename: Albert
authors.initials: A
authors.lastname: Rosenberger
authors.forename: Michael P
authors.initials: MP
authors.lastname: Schön
authors.forename: Steffen
authors.initials: S
authors.lastname: Emmert
authors.forename: Holger A
authors.initials: HA
authors.lastname: Haenssle
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D001706:Biopsy; D002648:Child; D002675:Child, Preschool; D046169:Dermoscopy; D005260:Female; D006801:Humans; D008297:Male; D008545:Melanoma / Q000175:diagnosis* / Q000473:pathology; D008875:Middle Aged; D012372:ROC Curve; D012867:Skin / Q000473:pathology; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000473:pathology; D055815:Young Adult