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

Commercially available clinical decision support systems (CDSSs) for skin cancer have been designed for the detection of melanoma only. Correct use of the systems requires expert knowledge, hampering their utility for nonexperts. Furthermore, there are no systems to detect other common skin cancer types, that is, nonmelanoma skin cancer (NMSC). As early diagnosis of skin cancer is essential, there is a need for a CDSS that is applicable to all types of skin lesions and is suitable for nonexperts. Nevus Doctor (ND) is a CDSS being developed by the authors. We here investigate ND's ability to detect both melanoma and NMSC and the opportunities for improvement. An independent test set of dermoscopic images of 870 skin lesions, including 44 melanomas and 101 NMSCs, were analysed by ND. Its sensitivity to melanoma and NMSC was compared to that of Mole Expert (ME), a commercially available CDSS, using the same set of lesions. ND and ME had similar sensitivity to melanoma. For ND at 95% melanoma sensitivity, the NMSC sensitivity was 100%, and the specificity was 12%. The melanomas misclassified by ND at 95% sensitivity were correctly classified by ME, and vice versa. ND is able to detect NMSC without sacrificing melanoma sensitivity.

[1]  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.

[2]  M. Weinstock,et al.  Does skin cancer screening save lives? , 2012, Cancer.

[3]  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.

[4]  Giuseppe Argenziano,et al.  Digital image analysis for diagnosis of skin tumors. , 2008, Seminars in cutaneous medicine and surgery.

[5]  Stein Olav Skrøvseth,et al.  Improved Skin Lesion Diagnostics for General Practice by Computer-Aided Diagnostics , 2015 .

[6]  M. Oliviero,et al.  Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: a feasibility study. , 2001, Journal of the American Academy of Dermatology.

[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]  Dmitrij Frishman,et al.  Pitfalls of supervised feature selection , 2009, Bioinform..

[9]  U. Ringborg,et al.  [Nevus or malignant melanoma? Correct diagnostic competence results in lower costs]. , 2008, Lakartidningen.

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

[11]  H. Kittler,et al.  Diagnostic accuracy of dermoscopy. , 2002, The Lancet. Oncology.

[12]  A. Hauschild,et al.  The Oncologist® Academia–Pharma Intersect: Melanoma , 2022 .

[13]  R Hofmann-Wellenhof,et al.  Value of the clinical history for different users of dermoscopy compared with results of digital image analysis , 2004, Journal of the European Academy of Dermatology and Venereology : JEADV.

[14]  Alejandro Fueyo-Casado,et al.  Evaluation of a Program for the Automatic Dermoscopic Diagnosis of Melanoma in a General Dermatology Setting , 2009, Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.].

[15]  R. Marks,et al.  Who removes pigmented skin lesions? , 1997, Journal of the American Academy of Dermatology.

[16]  B. Møller,et al.  Cancer incidence, mortality, survival and prevalence in Norway , 2011 .

[17]  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.

[18]  S. Feldman,et al.  Frequency of Seborrheic Keratosis Biopsies in the United States: A Benchmark of Skin Lesion Care Quality and Cost Effectiveness , 2003, Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.].

[19]  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.

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

[21]  C. Rosendahl,et al.  The impact of subspecialization and dermatoscopy use on accuracy of melanoma diagnosis among primary care doctors in Australia. , 2012, Journal of the American Academy of Dermatology.

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

[23]  Stephan Dreiseitl,et al.  Do physicians value decision support? A look at the effect of decision support systems on physician opinion , 2005, Artif. Intell. Medicine.

[24]  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 .

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

[26]  M. G. Fleming,et al.  Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. , 2003, Journal of the American Academy of Dermatology.

[27]  Jeffrey E Gershenwald,et al.  Final version of 2009 AJCC melanoma staging and classification. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[28]  M A Weinstock,et al.  Skin cancer screening participation and impact on melanoma incidence in Germany – an observational study on incidence trends in regions with and without population-based screening , 2012, British Journal of Cancer.

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

[30]  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.

[31]  Stein Olav Skrøvseth,et al.  Automatic Segmentation of Dermoscopic Images by Iterative Classification , 2011, Int. J. Biomed. Imaging.

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

[33]  Masaru Tanaka,et al.  Four-Class Classification of Skin Lesions With Task Decomposition Strategy , 2015, IEEE Transactions on Biomedical Engineering.

[34]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[35]  D. McLean,et al.  Real-time Raman Spectroscopy for in Vivo Skin Cancer Diagnosis Raman Spectroscopy of Skin Cancer , 2022 .

[36]  J. Emery,et al.  Effect of adding a diagnostic aid to best practice to manage suspicious pigmented lesions in primary care: randomised controlled trial , 2012, BMJ : British Medical Journal.

[37]  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.

[38]  M Carrara,et al.  Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions , 2007, Physics in medicine and biology.

[39]  J. Hornaday,et al.  Cancer Facts & Figures 2004 , 2004 .

[40]  M. Mihm,et al.  The performance of MelaFind: a prospective multicenter study. , 2011, Archives of dermatology.

[41]  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.

[42]  S. Meehan,et al.  Improved identification of potentially dangerous pigmented skin lesions by computerized image analysis. , 2003, Archives of dermatology.

[43]  Piotr Niezgoda,et al.  Novel Approaches to Treatment of Advanced Melanoma: A Review on Targeted Therapy and Immunotherapy , 2015, BioMed research international.

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