Literature DB >> 15691253

Predictive power of irregular border shapes for malignant melanomas.

Tim K Lee1, Ela Claridge.   

Abstract

BACKGROUND/
PURPOSE: 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.
CONCLUSION: 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.

Entities:  

Mesh:

Year:  2005        PMID: 15691253     DOI: 10.1111/j.1600-0846.2005.00076.x

Source DB:  PubMed          Journal:  Skin Res Technol        ISSN: 0909-752X            Impact factor:   2.365


  7 in total

1.  A systems biology approach to invasive behavior: comparing cancer metastasis and suburban sprawl development.

Authors:  John J Ryan; Benjamin L Dows; Michael V Kirk; Xueming Chen; Jeffrey R Eastman; Rodney J Dyer; Lemont B Kier
Journal:  BMC Res Notes       Date:  2010-02-10

Review 2.  Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas.

Authors:  Zhao Liu; Jiuai Sun; Lyndon Smith; Melvyn Smith; Robert Warr
Journal:  Med Biol Eng Comput       Date:  2012-03-22       Impact factor: 2.602

3.  Role of the Interplay Between the Internal and External Conditions in Invasive Behavior of Tumors.

Authors:  Youness Azimzade; Abbas Ali Saberi; Muhammad Sahimi
Journal:  Sci Rep       Date:  2018-04-13       Impact factor: 4.379

4.  A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images.

Authors:  Abder-Rahman Ali; Jingpeng Li; Guang Yang; Sally Jane O'Shea
Journal:  PeerJ Comput Sci       Date:  2020-06-29

5.  Novel Method for Border Irregularity Assessment in Dermoscopic Color Images.

Authors:  Joanna Jaworek-Korjakowska
Journal:  Comput Math Methods Med       Date:  2015-10-29       Impact factor: 2.238

6.  Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence.

Authors:  Joanna Jaworek-Korjakowska; Paweł Kłeczek
Journal:  Biomed Res Int       Date:  2016-01-17       Impact factor: 3.411

7.  A Novel Fuzzy Multilayer Perceptron (F-MLP) for the Detection of Irregularity in Skin Lesion Border Using Dermoscopic Images.

Authors:  Abder-Rahman Ali; Jingpeng Li; Summrina Kanwal; Guang Yang; Amir Hussain; Sally Jane O'Shea
Journal:  Front Med (Lausanne)       Date:  2020-07-07
  7 in total

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