Dermatoscopic and clinical features of congenital or congenital-type nail matrix nevi: A multicenter prospective cohort study by the International Dermoscopy Society.

BACKGROUND: Congenital nail matrix nevi (NMN) are difficult to diagnose because they feature clinical characteristics suggestive of adult subungual melanoma. Nail matrix biopsy is difficult to perform, especially in children. OBJECTIVE: To describe the initial clinical and dermatoscopic features of NMN appearing at birth (congenital) or after birth but before the age of 5 years (congenital-type). METHODS: We conducted a prospective, international, and consecutive data collection in 102 hospitals or private medical offices across 30 countries from 2009 to 2019. RESULTS: There were 69 congenital and 161 congenital-type NMNs. Congenital and congenital-type NMN predominantly displayed an irregular pattern of longitudinal microlines (n = 146, 64%), reminiscent of subungual melanoma in adults. The distal fibrillar ("brush-like") pattern, present in 63 patients (27.8%), was more frequently encountered in congenital NMN than in congenital-type NMN (P = .012). Moreover, congenital NMN more frequently displayed a periungual pigmentation (P = .029) and Hutchinson's sign (P = .027) than did congenital-type NMN. LIMITATIONS: Lack of systematic biopsy-proven diagnosis and heterogeneity of clinical and dermatoscopic photographs. CONCLUSION: Congenital and congenital-type NMN showed worrisome clinical and dermatoscopic features similar to those observed in adulthood subungual melanoma. The distal fibrillar ("brush-like") pattern is a suggestive feature of congenital and congenital-type NMN.

Inequalities in the patterns of dermoscopy use and training across Europe: conclusions of the Eurodermoscopy pan-European survey.

BACKGROUND: Dermoscopy is a widely used technique, recommended in clinical practice guidelines worldwide for the early diagnosis of skin cancers. Intra-European disparities are reported for early detection and prognosis of skin cancers, however, no information exists about regional variation in patterns of dermoscopy use across Europe. OBJECTIVE: To evaluate the regional differences in patterns of dermoscopy use and training among European dermatologists. MATERIALS & METHODS: An online survey of European-registered dermatologists regarding dermoscopy training, practice and attitudes was established. Answers from Eastern (EE) versus Western European (WE) countries were compared and their correlation with their respective countries' gross domestic product/capita (GDPc) and total and government health expenditure/capita (THEc and GHEc) was analysed. RESULTS: We received 4,049 responses from 14 WE countries and 3,431 from 18 EE countries. A higher proportion of WE respondents reported dermoscopy use (98% vs. 77%, p<0.001) and training during residency (43% vs. 32%) or anytime (96.5% vs. 87.6%) (p<0.001) compared to EE respondents. The main obstacles in dermoscopy use were poor access to dermoscopy equipment in EE and a lack of confidence in one's skills in WE. GDPc, THEc and GHEc correlated with rate of dermoscopy use and dermoscopy training during residency (Spearman rho: 0.5-0.7, p<0.05), and inversely with availability of dermoscopy equipment. CONCLUSION: The rates and patterns of dermoscopy use vary significantly between Western and Eastern Europe, on a background of economic inequality. Regionally adapted interventions to increase access to dermoscopy equipment and training might enhance the use of this technique towards improving the early detection of skin cancers.

Web-based consultations for parents of children with atopic dermatitis: results of a randomized controlled trial.

AIM: To analyse how web-based consultations for parents of children with atopic dermatitis affect self-management behaviour, health outcome, health resource use and family costs. METHODS: Ninety-eight children with atopic dermatitis were randomly assigned to intervention and control groups. The intervention group received remote dermatology consultations through a secure web-based communication system. The control group was encouraged to seek treatment through traditional means such as general practitioner visits and hospital care. Both groups received an extensive individual educational session prior to the intervention. RESULTS: Thirty-eight percent of the intervention group used web-based consultations 158 times ranging from 1 to 38 consultations per patient. We found no change in self-management behaviour, health outcome or costs. The intervention group tended to have fewer visits to practitioners offering complementary therapies than the control group, and we found a positive correlation between emergency visits at baseline and messages sent. Both groups, however, reduced the mean number of skin care treatments performed per week and had fewer total health care visits after the intervention. CONCLUSION: We found no effect of supplementing traditional treatment for childhood dermatitis with web-based consultations. This study showed that web consultations is feasible, but more research is needed to determine its effect on self-management skills, health outcome and resource use.

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images.

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

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.

Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists.

BACKGROUND: It is often difficult to differentiate early melanomas from benign melanocytic nevi even by expert dermatologists, and the task is even more challenging for primary care physicians untrained in dermatology and dermoscopy. A computer system can provide an objective and quantitative evaluation of skin lesions, reducing subjectivity in the diagnosis. OBJECTIVE: Our objective is to make a low-cost computer aided diagnostic tool applicable in primary care based on a consumer grade camera with attached dermatoscope, and compare its performance to that of experienced dermatologists. METHODS AND MATERIALS: We propose several new image-derived features computed from automatically segmented dermoscopic pictures. These are related to the asymmetry, color, border, geometry, and texture of skin lesions. The diagnostic accuracy of the system is compared with that of three dermatologists. RESULTS: With a data set of 206 skin lesions, 169 benign and 37 melanomas, the classifier was able to provide competitive sensitivity (86%) and specificity (52%) scores compared with the sensitivity (85%) and specificity (48%) of the most accurate dermatologist using only dermoscopic images. CONCLUSION: We show that simple statistical classifiers can be trained to provide a recommendation on whether a pigmented skin lesion requires biopsy to exclude skin cancer with a performance that is comparable to and exceeds that of experienced dermatologists.

Automatic segmentation of dermoscopic images by iterative classification.

Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.