Literature DB >> 16301386

The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma.

Scott W Menzies1, Leanne Bischof, Hugues Talbot, Alex Gutenev, Michelle Avramidis, Livian Wong, Sing Kai Lo, Geoffrey Mackellar, Victor Skladnev, William McCarthy, John Kelly, Brad Cranney, Peter Lye, Harold Rabinovitz, Margaret Oliviero, Andreas Blum, Alexandra Varol, Alexandra Virol, Brian De'Ambrosis, Roderick McCleod, Hiroshi Koga, Caron Grin, Ralph Braun, Robert Johr.   

Abstract

OBJECTIVE: To describe the diagnostic performance of SolarScan (Polartechnics Ltd, Sydney, Australia), an automated instrument for the diagnosis of primary melanoma.
DESIGN: Images from a data set of 2430 lesions (382 were melanomas; median Breslow thickness, 0.36 mm) were divided into a training set and an independent test set at a ratio of approximately 2:1. A diagnostic algorithm (absolute diagnosis of melanoma vs benign lesion and estimated probability of melanoma) was developed and its performance described on the test set. High-quality clinical and dermoscopy images with a detailed patient history for 78 lesions (13 of which were melanomas) from the test set were given to various clinicians to compare their diagnostic accuracy with that of SolarScan.
SETTING: Seven specialist referral centers and 2 general practice skin cancer clinics from 3 continents. Comparison between clinician diagnosis and SolarScan diagnosis was by 3 dermoscopy experts, 4 dermatologists, 3 trainee dermatologists, and 3 general practitioners. PATIENTS: Images of the melanocytic lesions were obtained from patients who required either excision or digital monitoring to exclude malignancy. MAIN OUTCOME MEASURES: Sensitivity, specificity, the area under the receiver operator characteristic curve, median probability for the diagnosis of melanoma, a direct comparison of SolarScan with diagnoses performed by humans, and interinstrument and intrainstrument reproducibility.
RESULTS: The melanocytic-only diagnostic model was highly reproducible in the test set and gave a sensitivity of 91% (95% confidence interval [CI], 86%-96%) and specificity of 68% (95% CI, 64%-72%) for melanoma. SolarScan had comparable or superior sensitivity and specificity (85% vs 65%) compared with those of experts (90% vs 59%), dermatologists (81% vs 60%), trainees (85% vs 36%; P =.06), and general practitioners (62% vs 63%). The intraclass correlation coefficient of intrainstrument repeatability was 0.86 (95% CI, 0.83-0.88), indicating an excellent repeatability. There was no significant interinstrument variation (P = .80).
CONCLUSIONS: SolarScan is a robust diagnostic instrument for pigmented or partially pigmented melanocytic lesions of the skin. Preliminary data suggest that its performance is comparable or superior to that of a range of clinician groups. However, these findings should be confirmed in a formal clinical trial.

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Year:  2005        PMID: 16301386     DOI: 10.1001/archderm.141.11.1388

Source DB:  PubMed          Journal:  Arch Dermatol        ISSN: 0003-987X


  31 in total

1.  A methodological approach to the classification of dermoscopy images.

Authors:  M Emre Celebi; Hassan A Kingravi; Bakhtiyar Uddin; Hitoshi Iyatomi; Y Alp Aslandogan; William V Stoecker; Randy H Moss
Journal:  Comput Med Imaging Graph       Date:  2007-03-26       Impact factor: 4.790

Review 2.  Strategies for early melanoma detection: Approaches to the patient with nevi.

Authors:  Agnessa Gadeliya Goodson; Douglas Grossman
Journal:  J Am Acad Dermatol       Date:  2009-05       Impact factor: 11.527

Review 3.  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

4.  Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.

Authors:  Philipp Tschandl; Noel Codella; Bengü Nisa Akay; Giuseppe Argenziano; Ralph P Braun; Horacio Cabo; David Gutman; Allan Halpern; Brian Helba; Rainer Hofmann-Wellenhof; Aimilios Lallas; Jan Lapins; Caterina Longo; Josep Malvehy; Michael A Marchetti; Ashfaq Marghoob; Scott Menzies; Amanda Oakley; John Paoli; Susana Puig; Christoph Rinner; Cliff Rosendahl; Alon Scope; Christoph Sinz; H Peter Soyer; Luc Thomas; Iris Zalaudek; Harald Kittler
Journal:  Lancet Oncol       Date:  2019-06-12       Impact factor: 41.316

5.  Combination of 3D skin surface texture features and 2D ABCD features for improved melanoma diagnosis.

Authors:  Yi Ding; Nigel W John; Lyndon Smith; Jiuai Sun; Melvyn Smith
Journal:  Med Biol Eng Comput       Date:  2015-05-07       Impact factor: 2.602

6.  Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.

Authors:  Philipp Tschandl; Cliff Rosendahl; Bengu Nisa Akay; Giuseppe Argenziano; Andreas Blum; Ralph P Braun; Horacio Cabo; Jean-Yves Gourhant; Jürgen Kreusch; Aimilios Lallas; Jan Lapins; Ashfaq Marghoob; Scott Menzies; Nina Maria Neuber; John Paoli; Harold S Rabinovitz; Christoph Rinner; Alon Scope; H Peter Soyer; Christoph Sinz; Luc Thomas; Iris Zalaudek; Harald Kittler
Journal:  JAMA Dermatol       Date:  2019-01-01       Impact factor: 10.282

7.  The role of spectrophotometry in the diagnosis of melanoma.

Authors:  Paolo A Ascierto; Marco Palla; Fabrizio Ayala; Ileana De Michele; Corrado Caracò; Antonio Daponte; Ester Simeone; Stefano Mori; Maurizio Del Giudice; Rocco A Satriano; Antonio Vozza; Giuseppe Palmieri; Nicola Mozzillo
Journal:  BMC Dermatol       Date:  2010-08-13

8.  Prevention of malignant melanoma.

Authors:  G Chaidemenos; A Stratigos; M Papakonstantinou; F Tsatsou
Journal:  Hippokratia       Date:  2008-01       Impact factor: 0.471

Review 9.  Current and emerging technologies in melanoma diagnosis: the state of the art.

Authors:  Estee L Psaty; Allan C Halpern
Journal:  Clin Dermatol       Date:  2009 Jan-Feb       Impact factor: 3.541

10.  Lacunarity analysis: a promising method for the automated assessment of melanocytic naevi and melanoma.

Authors:  Stephen Gilmore; Rainer Hofmann-Wellenhof; Jim Muir; H Peter Soyer
Journal:  PLoS One       Date:  2009-10-13       Impact factor: 3.240

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