[Dermoscopy in cutaneous melanoma]

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.