Performance of a computer‐aided digital dermoscopic image analyzer for melanoma detection in 1,076 pigmented skin lesion biopsies

Background: Digital dermoscopic image analysis of pigmented skin lesions (PSLs) has become increasingly popular, despite its unclear clinical utility. Unbiased, high‐powered studies investigating the efficacy of commercially available systems are limited. Objective: To investigate the diagnostic performance of the FotoFinder Mole‐Analyzer in assessing PSLs for cutaneous melanoma. Methods: In this 15‐year retrospective study, the histopathologies of 1076 biopsied PSLs among a total of 2500 imaged PSLs were collected. The biopsied PSLs were categorized as benign or malignant (cutaneous melanoma) based on histopathology. Analyzer scores (0‐1.00) for these PSLs were obtained and grouped according to histopathology. Results: At an optimized cutoff score of 0.50, a sensitivity of 56% and a specificity of 74% were achieved. The area under the receiver operating characteristics curve was 0.698, indicating poor accuracy as a diagnostic tool. Limitations: This study had a retrospective design and involved only a single institution. Conclusion: Our study reveals a low sensitivity of the scoring function of this digital dermoscopic image analyzer for detecting cutaneous melanomas. Physicians must apply keen clinical judgment when using such devices in the screening of suspicious PSLs.

[1]  Susan M Swetter,et al.  Evaluation of digital dermoscopy in a pigmented lesion clinic: clinician versus computer assessment of malignancy risk. , 2007, Journal of the American Academy of Dermatology.

[2]  N. Onsun,et al.  Pitfalls of an automated dermoscopic analysis system in the differential diagnosis of melanocytic lesions. , 2014, Acta dermatovenerologica Croatica : ADC.

[3]  Ammara Masood,et al.  Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms , 2013, Int. J. Biomed. Imaging.

[4]  A. Marghoob,et al.  Can automated dermoscopy image analysis instruments provide added benefit for the dermatologist? A study comparing the results of three systems , 2007, The British journal of dermatology.

[5]  J. Scharcanski,et al.  Computer Vision Techniques for the Diagnosis of Skin Cancer , 2013 .

[6]  R Hofmann-Wellenhof,et al.  Patient acceptance and diagnostic utility of automated digital image analysis of pigmented skin lesions , 2012, Journal of the European Academy of Dermatology and Venereology : JEADV.

[7]  P Altmeyer,et al.  Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer‐aided analysis of data from pigmented skin lesions using digital dermoscopy , 2003, The British journal of dermatology.

[8]  Fred Godtliebsen,et al.  Computer-Aided Decision Support for Melanoma Detection Applied on Melanocytic and Nonmelanocytic Skin Lesions: A Comparison of Two Systems Based on Automatic Analysis of Dermoscopic Images , 2015, BioMed research international.

[9]  Jorge S. Marques,et al.  A clinically oriented system for melanoma diagnosis using a color representation , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  R Hofmann-Wellenhof,et al.  Clinical performance of the Nevisense system in cutaneous melanoma detection: an international, multicentre, prospective and blinded clinical trial on efficacy and safety , 2014, The British journal of dermatology.

[11]  Alexandre Serov,et al.  Assessment of melanocytic skin lesions with a high‐definition laser Doppler imaging system , 2012, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[12]  Renato Marchesini,et al.  Automated melanoma detection with a novel multispectral imaging system: results of a prospective study , 2005, Physics in medicine and biology.

[13]  W. Chang,et al.  Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study , 2013, PloS one.

[14]  Scott W Menzies,et al.  Automated diagnostic instruments for cutaneous melanoma. , 2008, Seminars in cutaneous medicine and surgery.

[15]  G Rassner,et al.  Clinical and Laboratory Investigations Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions , 2004 .

[16]  Ralph Braun,et al.  The performance of SolarScan: an automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. , 2005, Archives of dermatology.

[17]  S. Menzies,et al.  Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. , 2003, Archives of dermatology.

[18]  G Pellacani,et al.  Digital videomicroscopy and image analysis with automatic classification for detection of thin melanomas. , 1999, Melanoma research.

[19]  João Manuel R. S. Tavares,et al.  A Review of the Quantification and Classification of Pigmented Skin Lesions: From Dedicated to Hand-Held Devices , 2015, Journal of Medical Systems.

[20]  Stein Olav Skrøvseth,et al.  Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists , 2014, Artif. Intell. Medicine.

[21]  I. Márquez-Rodas,et al.  Who detects melanoma? Impact of detection patterns on characteristics and prognosis of patients with melanoma. , 2016, Journal of the American Academy of Dermatology.

[22]  Rafael García,et al.  Computerized analysis of pigmented skin lesions: A review , 2012, Artif. Intell. Medicine.

[23]  Arash Taheri,et al.  Computer-aided dermoscopy for diagnosis of melanoma , 2005, BMC dermatology.

[24]  A. Green,et al.  Computer image analysis in the diagnosis of melanoma. , 1994, Journal of the American Academy of Dermatology.

[25]  D. Piccolo,et al.  Clinical and Laboratory Investigations Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer-aided diagnosis of 341 pigmented skin lesions: a comparative study , 2002 .

[26]  Rosamaria Corona.  Accuracy of Computer Diagnosis of Melanoma—Editor's Comment , 2003 .

[27]  Stephan Dreiseitl,et al.  Computer versus human diagnosis of melanoma: evaluation of the feasibility of an automated diagnostic system in a prospective clinical trial , 2009, Melanoma research.

[28]  M. Binder,et al.  Application of an artificial neural network in epiluminescence microscopy pattern analysis of pigmented skin lesions: a pilot study , 1994, The British journal of dermatology.

[29]  T. Schindewolf,et al.  Classification of melanocytic lesions with color and texture analysis using digital image processing. , 1993, Analytical and quantitative cytology and histology.

[30]  A. Ormerod,et al.  Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma , 2009, The British journal of dermatology.

[31]  P Barbini,et al.  Digital dermoscopy analysis for the differentiation of atypical nevi and early melanoma: a new quantitative semiology. , 1999, Archives of dermatology.

[32]  Staal A. Vinterbo,et al.  Applying a decision support system in clinical practice: Results from melanoma diagnosis , 2007, AMIA.

[33]  P Bauer,et al.  Digital epiluminescence microscopy: usefulness in the differential diagnosis of cutaneous pigmentary lesions. A statistical comparison between visual and computer inspection , 2000, Melanoma research.

[34]  M Fimiani,et al.  Dysplastic naevus vs. in situ melanoma: digital dermoscopy analysis , 2005, The British journal of dermatology.

[35]  T Fikrle,et al.  Digital computer analysis of dermatoscopical images of 260 melanocytic skin lesions; perimeter/area ratio for the differentiation between malignant melanomas and melanocytic nevi , 2007, Journal of the European Academy of Dermatology and Venereology : JEADV.