title: Assessment of a Diagnostic Predictive Probability Model Provided by a Multispectral Digital Skin Lesion Analysis Device for Melanoma and Other High-risk Pigmented Lesions and its Impact on Biopsy Decisions.
abstract: OBJECTIVE
  Risk prediction models for primary malignant melanoma thus far have relied on qualitative patient information. The authors propose a quantitative diagnostic predictive probability model using Multispectral Digital Skin Lesion Analysis for melanoma and other high-risk pigmented lesions and evaluate its effectiveness optimizing biopsy decisions by dermatologists.
  
  
  METHODS
  Data from 1,632 pigmented lesions analyzed by a Multispectral Digital Skin Lesion Analysis device were used to perform a logistic regression analysis. This new quantitative melanoma or melanoma/atypical melanocytic hyperplasia/high-grade dysplastic nevus probability model was then evaluated to determine its impact on dermatologist decisions to biopsy pigmented lesions clinically suggestive of melanoma. Participants were given an electronic keypad and answered "yes" or "no" if they would biopsy each of 12 pigmented lesions when presented first with patient history, clinical images, and dermoscopic images and again when subsequently shown Multispectral Digital Skin Lesion Analysis data.
  
  
  METHODS
  Study of 191 dermatologists at a medical conference.
  
  
  METHODS
  Sensitivity, specificity, biopsy accuracy, overall biopsy rate, and percentage dermatologists biopsying all five melanomas.
  
  
  RESULTS
  Dermatologists were significantly more sensitive, specific, and accurate while decreasing overall biopsy rates with Multispectral Digital Skin Lesion Analysis probability information.
  
  
  CONCLUSIONS
  Integration of Multispectral Digital Skin Lesion Analysis probability information in the biopsy evaluation and selection process of pigmented lesions has the potential to improve melanoma sensitivity of dermatologists without the concomitant costs associated with additional biopsies being performed.
authors.affiliation: Rigel Dermatology, New York, New York;
authors.forename: Richard R
authors.initials: RR
authors.lastname: Winkelmann
authors.affiliation: Department of Dermatology, Yale School of Medicine, Yale University, New Haven, Connecticut;
authors.forename: Jane
authors.initials: J
authors.lastname: Yoo
authors.affiliation: MELA Sciences Inc., Irvington, New York;
authors.forename: Natalie
authors.initials: N
authors.lastname: Tucker
authors.affiliation: Iris Interactive System, Cody, Wyoming;
authors.forename: Richard
authors.initials: R
authors.lastname: White
authors.affiliation: Department of Dermatology, New York University School of Medicine, New York, New York.
authors.forename: Darrell S
authors.initials: DS
authors.lastname: Rigel