Summary The accuracy of the clinical diagnosis of cutaneous melanoma with the unaided eye is only about 60%. Dermoscopy, a non-invasive, in vivo technique for the microscopic examination of pigmented skin lesions, has the potential to improve the diagnostic accuracy. Our objectives were to review previous publications, to compare the accuracy of melanoma diagnosis with and without dermoscopy, and to assess the influence of study characteristics on the diagnostic accuracy. We searched for publications between 1987 and 2000 and identified 27 studies eligible for meta-analysis. The diagnostic accuracy for melanoma was significantly higher with dermoscopy than without this technique (log odds ratio 4·0 [95% CI 3·0 to 5·1] versus 2·7 [1·9 to 3·4]; an improvement of 49%, p=0·001). The diagnostic accuracy of dermoscopy significantly depended on the degree of experience of the examiners. Dermoscopy by untrained or less experienced examiners was no better than clinical inspection without dermoscopy. The diagnostic performance of dermoscopy improved when the diagnosis was made by a group of examiners in consensus and diminished as the prevalence of melanoma increased. A comparison of various diagnostic algorithms for dermoscopy showed no significant differences in their diagnostic performance. A thorough appraisal of the study characteristics showed that most of the studies were potentially influenced by verification bias. In conclusion, dermoscopy improves the diagnostic accuracy for melanoma in comparison with inspection by the unaided eye, but only for experienced examiners.

https://www.thelancet.com/pdfs/journals/lanonc/PIIS1470-2045(02)00679-4.pdf

Diagnostic accuracy of dermoscopy

Objective To compare the reliability of a new 7-point checklist based on simplified epiluminescence microscopy (ELM) pattern analysis with the ABCD rule of dermatoscopy and standard pattern analysis for the diagnosis of clinically doubtful melanocytic skin lesions. Design In a blind study, ELM images of 342 histologically proven melanocytic skin lesions were evaluated for the presence of 7 standard criteria that we called the "ELM 7-point checklist." For each lesion, "overall" and "ABCD scored" diagnoses were recorded. From a training set of 57 melanomas and 139 atypical non-melanomas, odds ratios were calculated to create a simple diagnostic model based on identification of major and minor criteria for the "7-point scored" diagnosis. A test set of 60 melanomas and 86 atypical non-melanomas was used for model validation and was then presented to 2 less experienced ELM observers, who recorded the ABCD and 7-point scored diagnoses. Settings University medical centers. Patients A sample of patients with excised melanocytic lesions. Main Outcome Measures Sensitivity, specificity, and accuracy of the models for diagnosing melanoma. Results From the total combined sets, the 7-point checklist gave a sensitivity of 95% and a specificity of 75% compared with 85% sensitivity and 66% specificity using the ABCD rule and 91% sensitivity and 90% specificity using standard pattern analysis (overall ELM diagnosis). Compared with the ABCD rule, the 7-point method allowed less experienced observers to obtain higher diagnostic accuracy values. Conclusions The ELM 7-point checklist provides a simplification of standard pattern analysis because of the low number of features to identify and the scoring diagnostic system. As with the ABCD rule, it can be easily learned and easily applied and has proven to be reliable in diagnosing melanoma.

/pdf/epiluminescence-microscopy-for-the-diagnosis-of-doubtful-1zt96zc1db.pdf

Epiluminescence Microscopy for the Diagnosis of Doubtful Melanocytic Skin Lesions: Comparison of the ABCD Rule of Dermatoscopy and a New 7-Point Checklist Based on Pattern Analysis

Summary
Background Dermoscopy is a noninvasive technique that enables the clinician to perform direct microscopic examination of diagnostic features, not seen by the naked eye, in pigmented skin lesions. Diagnostic accuracy of dermoscopy has previously been assessed in meta-analyses including studies performed in experimental and clinical settings.

Objectives  To assess the diagnostic accuracy of dermoscopy for the diagnosis of melanoma compared with naked eye examination by performing a meta-analysis exclusively on studies performed in a clinical setting.

Methods  We searched for publications from 1987 to January 2008 and found nine eligible studies. The selected studies compare diagnostic accuracy of dermoscopy with naked eye examination using a valid reference test on consecutive patients with a defined clinical presentation, performed in a clinical setting. Hierarchical summary receiver operator curve analysis was used to estimate the relative diagnostic accuracy for clinical examination with, and without, the use of dermoscopy.

Results  We found the relative diagnostic odds ratio for melanoma, for dermoscopy compared with naked eye examination, to be 15·6 [95% confidence interval (CI) 2·9–83·7, P = 0·016]; removal of two outlier studies changed this to 9·0 (95% CI 1·5–54·6, P = 0·03).

Conclusions  Dermoscopy is more accurate than naked eye examination for the diagnosis of cutaneous melanoma in suspicious skin lesions when performed in the clinical setting.

Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting

A Methodological Approach to the Classification of Dermoscopy Images

Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 versus 0.783), in average precision of 4% (0.649 versus 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state of the art (36.8% specificity compared to 12.5%). Compared to the average of eight expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% versus 70.5%), and specificity (62% versus 59%) evaluated at an equivalent sensitivity (82%).

Deep learning ensembles for melanoma recognition in dermoscopy images

Background and Design: Epiluminescence microscopy (ELM) is a noninvasive technique that, by employing the optical phenomenon of oil immersion, makes subsurface structures of the skin accessible for in vivo examination and thus provides additional criteria for the clinical diagnosis of pigmented skin lesions. At present, almost all studies about the value and clinical importance of ELM are based on data derived from ELM experts (ie, dermatologists specifically trained in this technique). In the present study, we attempt to determine whether the clinical diagnosis of pigmented skin lesions is significantly improved using ELM and whether ELM-trained individuals and dermatologists not trained in this technique profit equally from this technique. Randomly selected histologically proven pigmented skin lesion specimens, photographed with (ELM) and without oil immersion (surface microscopy) were presented by slide projection to six ELM experts and 13 ELM nonexperts (ie, dermatologists not formally trained in ELM) for diagnosis. To evaluate the diagnostic performance of ELM experts and nonexperts with and without the oil immersion technique (ie, ELM vs surface microscopy), the following parameters were obtained: intraobserver and interobserver agreement by κ statistics and sensitivity and specificity of diagnostic performance. Results: Our results show that by usinbetter-ELM technique the ELM experts reach a substantially hetter intraobserver agreement than nonexperts (median κ, 0.56 vs 0.36). The interobserver agreement was markedly increased in the ELM experts group (average gain, 7%) but decreased in the ELM nonexperts group (average loss, 6%). The sensitivity of diagnosis was significantly increased in the ELM experts group (average gain, 10%), but decreased in the nonexperts group (average loss, 10%). Finally, the specificity of diagnosis was excellent in the ELM experts group, both with and without oil immersion (0.91) and was somewhat improved by ELM in the nonexperts group (0.77 vs 0.85). Conclusions: We conclude that the ELM technique increases sensitivity in formally trained dermatologists, but may decrease the diagnostic ability in dermatologists not formally trained in the ELM technique. Consequently, formal broad-based training in ELM should be offered to the dermatologic community. (Arch Dermatol. 1995;131:286-291)

Alexander Winkler

Papers

Epiluminescence Microscopy: A Useful Tool for the Diagnosis of Pigmented Skin Lesions for Formally Trained Dermatologists

A Useful Tool for the Diagnosis of Pigmented Skin Lesions for Formally Trained Dermatologists

Fotemustine plus dacarbazine in advanced stage III malignant melanoma.