title: Predictive power of irregular border shapes for malignant melanomas.
abstract: OBJECTIVE
  The Irregularity Index is a measure of border irregularity from pigmented skin lesion images. The measure attempts to quantify the degree of irregularity of the structural indentations and protrusions along a lesion border. A carefully designed study has shown that the parameters derived from the Irregularity Index were highly correlated with expert dermatologists' notion of border shape. This paper investigates the predictive power of these parameters on a set of data with known histological diagnosis.
  
  
  METHODS
  A set of 188 pigmented skin lesions (30 malignant melanomas and 158 benign lesions) was selected for the study. Their images were segmented and their border shapes were analysed by the Irregularity Index, producing four border irregularity parameters. The predictive power of these four parameters was estimated by a series of statistical tests.
  
  
  RESULTS
  The mean values of the four border irregularity parameters were significantly different between the melanoma group and the benign lesion group. When using the four parameters to predict its disease status, the leave-one-out classification rate was 82.4%, and the area under the receiver operating characteristic curve was 0.77. A malignant melanoma was 8.9 times more likely to have an irregular border than a benign lesion.
  
  
  CONCLUSIONS
  This study confirmed that border irregularity is an important clinical feature for the diagnosis of malignant melanoma. It also indicates that the computer-derived measures based on the Irregularity Index capture to certain extent the kind of irregularity which is exhibited by melanomas.
authors.affiliation: Cancer Control Research Program, BC Cancer Agency, Vancouver, BC, Canada. tlee@bccancer.bc.ca
authors.forename: Tim K
authors.initials: TK
authors.lastname: Lee
authors.forename: Ela
authors.initials: E
authors.lastname: Claridge
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008545:Melanoma / Q000145:classification* / Q000473:pathology*; D008954:Models, Biological; D015233:Models, Statistical; D009367:Neoplasm Staging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012878:Skin Neoplasms / Q000145:classification* / Q000473:pathology*