Dermatoscopy for melanoma and pigmented lesions

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

Dermoscopic evaluation of superficial spreading melanoma

Background

Dermoscopy increases the sensitivity of the diagnosis of melanoma, leading to its early identification and increasing the chances of cure.

Objective

To describe the clinical and dermoscopic characteristics of superficial spreading melanomas, and to detect the differences between in situ and invasive 1-mm thick melanomas.

Methods

This was a cross-sectional study in which dermoscopic images of 58 melanomas, grouped according to their thickness, were evaluated.

Results

24 in situ melanomas were evaluated, 28 invasive melanomas with Breslow ≤ 1 mm (0.50 ± 0.22 mm) and six with Breslow > 1 mm (2.35 ± 2.02 mm). In situ melanomas were smaller than invasive melanomas. The most commonly found dermoscopic criteria were asymmetry (84.5%), three or more colors (81.0%), and atypical network (79.3%). A non-specific pattern was more common in in situ melanomas (p = 0.028) and atypical network in invasive melanomas with Breslow 1 mm presented inverted network (p = 0.018).

Study limitations

The sample was selected by convenience, since it was necessary to have a preoperative photo of the tumor, which may have led to the loss of clinically less significant lesions, as well as those highly suggestive of melanoma.

Conclusions

Melanomas in early stages showed a more frequent nonspecific pattern and atypical network, while invasive melanomas showed a multicomponent pattern, three or more colors, and an inverted network.

The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers

The incidence of skin tumors has steadily increased. Although most are benign and do not affect survival, some of the more malignant skin tumors present a lethal threat if a delay in diagnosis permits them to become advanced. Ideally, an inspection by an expert dermatologist would accurately detect malignant skin tumors in the early stage; however, it is not practical for every single patient to receive intensive screening by dermatologists. To overcome this issue, many studies are ongoing to develop dermatologist-level, computer-aided diagnostics. Whereas, many systems that can classify dermoscopic images at this dermatologist-equivalent level have been reported, a much fewer number of systems that can classify conventional clinical images have been reported thus far. Recently, the introduction of deep-learning technology, a method that automatically extracts a set of representative features for further classification has dramatically improved classification efficacy. This new technology has the potential to improve the computer classification accuracy of conventional clinical images to the level of skilled dermatologists. In this review, this new technology and present development of computer-aided skin tumor classifiers will be summarized.

Patient Attitude towards Videodermatoscopy for the Detection of Skin Cancer: A Cross-Sectional Study

BACKGROUND

Videodermatoscopy (VD) is a useful device for supporting dermatologists in the distinction between benign and malignant lesions. However, only few patients have access to VD in daily practice.

OBJECTIVES

To investigate patient attitudes towards VD.

METHOD

A cross-sectional study was conducted between May and June 2018. Patients were asked to complete a self-administered questionnaire on the popularity of VD. Descriptive analysis was performed including contingency tables and χ2 tests to investigate associations between sociodemographic data and the popularity of VD.

RESULTS

A total of 61.2% (123/201) of the patients had not heard of VD at the time of assessment or were unsure. Of the 38.8% of patients (78/201) who already knew of VD, 64.1% (50/78) reported that they had already been investigated by VD; 57.5% (111/193) were willing to pay an extra fee for VD. A high level of education and private insurance status had a statistically significant association with the popularity of VD (p = 0.036 and p = 0.026, respectively).

CONCLUSIONS

There was a strong information deficit, especially in patients with lower education and statutory health insurance. Nevertheless, the willingness to pay an extra fee for a VD-assisted skin examination was high. Dermatologists should actively offer and inform their patients about VD when performing skin cancer screening.

Developing and validating the Cutaneous WARTS (CWARTS) diagnostic tool: a novel clinical assessment and classification system for cutaneous warts

BACKGROUND

The clinical appearance of cutaneous warts is highly variable and not standardized.

OBJECTIVES

To develop and validate a reproducible clinical tool for the standardized assessment of cutaneous warts to distinguish these lesions accurately.

METHODS

Nine morphological characteristics were defined and validated regarding intra- and interobserver agreement. Based on literature and semistructured interviews, a systematic dichotomous assessment tool, the Cutaneous WARTS (CWARTS) diagnostic tool was developed. The validation consisted of two independent parts performed with photographs from the recent WARTS-2 trial. In part A, the CWARTS diagnostic tool was tested by 28 experienced physicians who assessed photographs of 10 different warts to investigate interobserver concordance. In part B, morphological characteristics were validated by masked and independent scoring of 299 photographs by six different observers. Part B also entailed reassessment of the photographs after at least 1 week. The primary outcome measurement was the intraclass correlation coefficient (ICC).

RESULTS

Presence of black dots (capillary thrombosis) had the greatest ICC (0·85) for interobserver agreement in part A, followed by arrangement (0·65), presence of border erythema (0·64) and sharpness of the border (0·60). In part B, results were similar for interobserver agreement with presence of black dots having the highest ICC (0·68), followed by border erythema (0·64), arrangement (0·58) and colour (0·55). For intraobserver agreement, presence of black dots had the highest agreement (0·70), followed by presence of border erythema (0·694) and colour (0·59).

CONCLUSIONS

Wart phenotype can be reliably assessed using the CWARTS diagnostic tool.

Pigmented nodular melanoma: the predictive value of dermoscopic features using multivariate analysis

BACKGROUND

Nodular melanoma (NM), representing 10-30% of all melanomas, plays a major role in global mortality related to melanoma. Nonetheless, the literature on dermoscopy of NM is scanty.

OBJECTIVES

To assess odds ratios (ORs) to quantify dermoscopic features of pigmented NM vs. pigmented superficial spreading melanoma (SSM), and pigmented nodular nonmelanocytic and benign melanocytic lesions.

METHODS

To assess the presence or absence of global patterns and dermoscopic criteria, digitized images of 457 pigmented skin lesions from patients with a histopathological diagnosis of NM (n = 75), SSM (n = 93), and nodular nonmelanocytic and benign melanocytic lesions (n = 289; namely, 39 basal cell carcinomas, 85 seborrhoeic keratoses, 81 blue naevi, and 84 compound/dermal naevi) were retrospectively collected and blindly evaluated by three observers.

RESULTS

Multivariate analysis showed that ulceration (OR 4.07), homogeneous disorganized pattern (OR 10.76), and homogeneous blue pigmented structureless areas (OR 2.37) were significantly independent prognostic factors for NM vs. SSM. Multivariate analysis of dermoscopic features of NM vs. nonmelanocytic and benign melanocytic lesions showed that the positive correlating features leading to a significantly increased risk of NM were asymmetric pigmentation (OR 6.70), blue-black pigmented areas (OR 7.15), homogeneous disorganized pattern (OR 9.62), a combination of polymorphous vessels and milky-red globules/areas (OR 23.65), and polymorphous vessels combined with homogeneous red areas (OR 33.88).

CONCLUSIONS

Dermoscopy may be helpful in improving the recognition of pigmented NM by revealing asymmetric pigmentation, blue-black pigmented areas, homogeneous disorganized pattern and abnormal vascular structures, including polymorphous vessels, milky-red globules/areas and homogeneous red areas.

Dermoscopic and clinical features of trunk melanomas

INTRODUCTION

Malignant melanomas account for 5% of all skin cancers and usually have a fatal clinical course. Additionally, the incidence of melanoma increases more rapidly than in any other cancer, and this has been attributed to the development of highly sensitive diagnostic techniques, mainly dermoscopy, which allows for early diagnosis. The phenotypic manifestations of gene/environment interactions, environmental factor and genetic factors may determine subtypes and anatomic localization of melanoma. Histopathologic subtypes, risk factors, and thickness of the skin are different in trunk melanomas.

AIM

To determine the frequency of dermatoscopic features in trunk melanomas. This study also investigates dermoscopic features according to the diameter of lesions.

MATERIAL AND METHODS

Seventy-one trunk melanomas were included. Their dermoscopic and clinical images, histopathological and clinical data were assessed. The relations between the diameter, Breslow thickness and dermoscopic characteristics were evaluated.

RESULTS

The most common dermoscopic findings of trunk melanomas were the multicomponent pattern (55 patients, 77.5%), asymmetry (62 patients; 87.3%), blue-gray veil (59 patients, 83.1%), and color variety (56 patients, 78.8%). When dermoscopic findings were compared, a multicomponent pattern (p = 0.03), milky-red areas (p = 0.001), blue-gray veils (p = 0.023), and regression structures (p = 0.037) were more common in large melanomas than in small melanomas.

CONCLUSIONS

The most common dermoscopic findings of trunk melanomas were the multicomponent pattern, asymmetry and blue-gray veil, color variety. The multicomponent pattern, milky-red areas, blue-gray veils, regression structures were statistically significant dermoscopic features in a group of large-diameter melanomas, compared to small melanomas.

Dysplastic nevi, cutaneous melanoma, and other skin neoplasms in patients with myotonic dystrophy type 1: a cross-sectional study

BACKGROUND

Myotonic dystrophy type 1 (MD1) is reported to be associated with internal malignancies. The association of myotonic dystrophy with cutaneous tumors is not fully understood.

OBJECTIVE

We sought to explore the total nevi count and the presence of atypical nevi, cutaneous melanoma, and other skin neoplasms in a representative cohort of patients with MD1 and to compare the findings with age- and sex-matched control subjects.

METHODS

In all, 90 patients with MD1 and 103 age- and sex-matched control subjects were assessed for cutaneous neoplasms by clinical skin and epiluminescence examination (dermoscopy). Where indicated, subsequent excisions were performed. In patients with MD1, leukocyte n(CTG) expansion was measured.

RESULTS

Patients with MD1 showed significantly higher numbers of nevi, dysplastic nevi, and melanomas despite a significantly greater proportion of the control subjects reporting sunburns. In addition, we found a significantly greater number of pilomatrixoma in patients with MD1.

LIMITATIONS

Our study is limited by the fact that there is no agreed-upon standardized technique to assess for prior sun exposure. Further research in the association of cutaneous neoplasms and MD1 including vitamin D and molecular biological techniques are also recommended.

CONCLUSION

MD1 itself may predispose to development of skin tumors.