Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet.

By Giuseppe Argenziano, H. Soyer, S. Chimenti, R. Talamini, R. Corona, F. Sera, Michael Binder, L. Cerroni, G. de Rosa, G. Ferrara and 47 more. Journal of American Academy of Dermatology, 2003

1,204 citations recorded by the source corpus.

BACKGROUND There is a need for better standardization of the dermoscopic terminology in assessing pigmented skin lesions. OBJECTIVE The virtual Consensus Net Meeting on Dermoscopy was organized to investigate reproducibility and validity of the various features and diagnostic algorithms. METHODS Dermoscopic images of 108 lesions were evaluated via the Internet by 40 experienced dermoscopists using a 2-step diagnostic procedure. The first-step algorithm distinguished melanocytic versus nonmelanocytic lesions. The second step in the diagnostic procedure used 4 algorithms (pattern analysis, ABCD rule, Menzies method, and 7-point checklist) to distinguish melanoma versus benign melanocytic lesions. kappa Values, log odds ratios, sensitivity, specificity, and positive likelihood ratios were estimated for all diagnostic algorithms and dermoscopic features. RESULTS Interobserver agreement was fair to good for all diagnostic methods, but it was poor for the majority of dermoscopic criteria. Intraobserver agreement was good to excellent for all algorithms and features considered. Pattern analysis allowed the best diagnostic performance (positive likelihood ratio: 5.1), whereas alternative algorithms revealed comparable sensitivity but less specificity. Interobserver agreement on management decisions made by dermoscopy was fairly good (mean kappa value: 0.53). CONCLUSION The virtual Consensus Net Meeting on Dermoscopy represents a valid tool for better standardization of the dermoscopic terminology and, moreover, opens up a new territory for diagnosing and managing pigmented skin lesions.

Subfields: Epidemiology, Oncology, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/52cc773368177ba687dc45b4b686aa05e1496d20

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Citations in the corpus

Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) (2016, methodology): “In an attempt to improve the scalability of dermoscopic expertise, procedural algorithms, such as “3-point checklist,” “ABCD rule,” “Menzies method,” and “7-point checklist,” were developed [6, 8].”
Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets (2019, methodology): “Further, although the 7-point checklist and pattern analysis diagnostic procedures are different, the 7-point checklist criteria are based on the criteria used in the process of pattern analysis [10].”
A patient-centric dataset of images and metadata for identifying melanomas using clinical context (2020, background): “implex, solar lentigo, lichenoid keratosis, and seborrheic keratosis were included [7, 8, 9]. Lesions satisfying the described criteria were represented in the dataset with a single dermoscopic image [8, 10, 11]. These include images captured with or without polarized light using a contact or noncon”
PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones (2020, background): “Nonetheless, accurate diagnosis is challenging and requires proper training and experience in dermoscopy 4–6 , a noninvasive diagnostic technique that uses optic magniﬁcation to permit the visualization of morphologic features that are not visible to the naked eye 7 .”
Overview of Advanced Computer Vision Systems for Skin Lesions Characterization (2009, methodology): “In the conventional procedure, the following diagnosis methods are mainly used [46]: 1) ABCD rule of dermoscopy; 2) pattern analysis; 3) Menzies method; 4) seven-point checklist;”