title: Preoperative melanoma thickness determination by 20-MHz sonography and digital videomicroscopy in combination.
abstract: OBJECTIVE
  To identify accurately thick melanomas preoperatively by means of a combined approach based on sonography and clinical-videomicroscopic evaluation.
  
  
  METHODS
  Ultrasonographic thickness measurement, obtained by means of a 20-MHz B-scanner, and identification of clinical and videomicroscopic variables useful in distinguishing between thick and thin melanomas were performed on a training set of 40 melanomas. An algorithm based on echographic, clinical, and videomicroscopic criteria was constructed to develop a method for preoperative evaluation of melanoma thickness and was validated on a test set of 48 melanomas.
  
  
  METHODS
  University medical department.
  
  
  METHODS
  Eighty-eight patients affected by primary cutaneous melanoma.
  
  
  METHODS
  Sensitivity and specificity of the algorithm, with the use of sonographic, clinical, and videomicroscopic data, in thick melanoma identification.
  
  
  RESULTS
  Echographic thickness was calculated for each lesion. On the training set, 2 clinical and 7 videomicroscopic features were identified for distinction between thick and thin melanomas: nonpalpability, central pigment network, central brown globules, and blotches were characteristic of thin melanomas; clinical regression, localized peripheral pigment network, veil, grayish polygonal areas, and blood vessels were characteristic of thick ones. A coefficient was attributed to each variable and a score was obtained for each lesion. The algorithm, developed for preoperative thickness prediction, was validated on the test set, enabling the distinction of thick melanomas with an 86.7% sensitivity and a 100% specificity.
  
  
  CONCLUSIONS
  The correct classification of all thin melanomas as such renders this approach suitable in clinical practice.
authors.affiliation: Department of Dermatology, University of Modena and Reggio Emilia, 41100 Modena, Italy.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Pellacani
authors.forename: Stefania
authors.initials: S
authors.lastname: Seidenari
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000592:standards*; D008545:Melanoma / Q000000981:diagnostic imaging / Q000473:pathology* / Q000601:surgery; D018715:Microscopy, Video / Q000379:methods / Q000592:standards; D011300:Preoperative Care; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology* / Q000601:surgery; D014463:Ultrasonography / Q000379:methods / Q000592:standards