Literature DB >> 28196213

Ugly Duckling Sign as a Major Factor of Efficiency in Melanoma Detection.

Caroline Gaudy-Marqueste1, Yanal Wazaefi2, Yvane Bruneu1, Raoul Triller3, Luc Thomas4, Giovanni Pellacani5, Josep Malvehy6, Marie-Françoise Avril7, Sandrine Monestier1, Marie-Aleth Richard1, Bernard Fertil2, Jean-Jacques Grob1.   

Abstract

Importance: Understanding the contribution of the ugly duckling sign (a nevus that is obviously different from the others in a given individual) in intrapatient comparative analysis (IPCA) of nevi may help improve the detection of melanoma.
Objectives: To assess the agreement of dermatologists on identification of the ugly duckling sign and estimate the contribution of IPCA to the diagnosis of melanoma. Design, Setting, and Participants: The same 2089 digital images of the nevi of a sample of 80 patients (mean age, 42 years [range, 19-80 years]; 33 men and 47 women), as well as 766 dermoscopic images from a subset of 30 patients (mean age, 40 years [range, 21-75 years]; 12 men and 18 women), were randomly presented to the same 9 dermatologists for blinded assessment from September 22, 2011, to April 1, 2013. The first experiment was designed to mimic an IPCA situation, with images of all nevi of each patient shown to the dermatologists, who were asked to identify ugly duckling nevi (UDN). The second experiment was designed to mimic a lesion-focused analysis to identify morphologically suspicious nevi. Data analysis was conducted from November 1, 2012, to June 1, 2013. Main Outcomes and Measures: Number of nevi labeled UDN and morphologically suspicious nevi, specificity of lesion-focused analysis and IPCA, and number of nevi identified for biopsy.
Results: Of the 2089 clinical images of nevi from 80 patients (median number of nevi per patient, 26 [range, 8-81]) and 766 dermoscopic images (median number of nevi per patient, 19 [range, 8-81]), all melanomas were labeled UDN and as morphologically suspicious nevi by the 9 dermatologists. The median number of UDN detected per patient was 0.8 among the clinical images of nevi (mean, 1.0; range, 0.48-2.03) and 1.26 among the dermoscopic images (mean, 1.4; range, 1.00-2.06). The propensity to consider more or fewer nevi as having ugly duckling signs was independent of the presentation (clinical or dermoscopic). The agreement among the dermatologists regarding UDN was lower with dermoscopic images (mean pairwise agreement, 0.53 for clinical images and 0.50 for dermoscopic images). The specificity of IPCA was 0.96 for clinical images and 0.95 for dermoscopic images vs 0.88 and 0.85, respectively, for lesion-focused analysis. When both IPCA and lesion-focused analyses were used, the number of nevi considered for biopsy was reduced by a factor of 6.9 compared with lesion-focused analysis alone. Conclusions and Relevance: Intrapatient comparative analysis is of major importance to the effectiveness of the diagnosis of melanoma. Introducing IPCA using the ugly duckling sign in computer-assisted diagnosis systems would be expected to improve performance.

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Mesh:

Year:  2017        PMID: 28196213     DOI: 10.1001/jamadermatol.2016.5500

Source DB:  PubMed          Journal:  JAMA Dermatol        ISSN: 2168-6068            Impact factor:   10.282


  11 in total

1.  Skin Cancer: Have melanoma and skin cancer finally met their match?

Authors:  Jeffrey E Gershenwald; Kelly C Nelson
Journal:  Nat Rev Clin Oncol       Date:  2017-04-19       Impact factor: 66.675

2.  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

3.  Extramedullary malignant melanotic schwannoma of the spine: Case report and an up to date systematic review of the literature.

Authors:  Georgios Solomou; Adikarige Haritha Dulanka Silva; Adrianna Wong; Ute Pohl; Nikolaos Tzerakis
Journal:  Ann Med Surg (Lond)       Date:  2020-10-07

4.  The Additive Value of 3D Total Body Imaging for Sequential Monitoring of Skin Lesions: A Case Series.

Authors:  Katarzyna Grochulska; Brigid Betz-Stablein; Chantal Rutjes; Frank Po-Chao Chiu; Scott W Menzies; H Peter Soyer; Monika Janda
Journal:  Dermatology       Date:  2021-08-11       Impact factor: 5.366

5.  Toward automated assessment of mole similarity on dermoscopic images.

Authors:  Yao Zhang; Kamil Ali; Jacob A George; Jason S Reichenberg; Matthew C Fox; Adewole S Adamson; James W Tunnell; Mia K Markey
Journal:  J Med Imaging (Bellingham)       Date:  2021-02-10

6.  DICOM in Dermoscopic Research: an Experience Report and a Way Forward.

Authors:  Liam Caffery; Jochen Weber; Nicholas Kurtansky; David Clunie; Steve Langer; George Shih; Allan Halpern; Veronica Rotemberg
Journal:  J Digit Imaging       Date:  2021-07-09       Impact factor: 4.903

7.  Dermoscopy comparative approach for early diagnosis in familial melanoma: influence of MC1R genotype.

Authors:  C Longo; V Barquet; E Hernandez; A A Marghoob; M Potrony; C Carrera; P Aguilera; C Badenas; J Malvehy; S Puig
Journal:  J Eur Acad Dermatol Venereol       Date:  2020-07-28       Impact factor: 9.228

8.  Screening for malignant melanoma-a critical assessment in historical perspective.

Authors:  Wolfgang Weyers
Journal:  Dermatol Pract Concept       Date:  2018-04-30

9.  Hypomelanotic melanoma detected by the "little red riding hood sign" in a patient with neurofibromatosis type 1.

Authors:  Roberta Giuffrida; Maximilian Uranitsch; Karin Schmid; Teresa Deinlein; Fabrizio Favero; Iris Zalaudek
Journal:  Dermatol Pract Concept       Date:  2017-07-15

10.  Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation.

Authors:  Xinzi He; Zhen Yu; Tianfu Wang; Baiying Lei; Yiyan Shi
Journal:  Technol Health Care       Date:  2018       Impact factor: 1.285

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