Background/Aim. Growing incidence of skin tumors require their accurate diagnosis. Dermoscopy, especially in-vivo, enhances diagnosis of basal cell carcinoma (BCC). Total body skin examination (TBSE), a visual inspection of the patient?s total body surface, is considered basic step in dermatological exam, especially in skin cancer screening. However, TBSE is still a matter of debate of its expediency in real clinical setting. Aim of this study is to analyze diagnostic accuracy of BCC detected and treated by referred dermatologists in Skin Cancer Unit of a Dermatology and Venereology Clinic. Methods. Retrospective analysis of the BCC detection during total body skin examination with visual inspection and dermoscopy. We calculated sensitivity and specificity and positive predictive value for BCC using histopathological results as correct diagnosis. Results. Out of 3346 biopsied skin tumors 49.58% were malignant and 50.42% benign. The most common malignant tumor was BCC, accounting for 84.09%. Localization of BCCs was mainly on the trunk 38.92% and on H-zone of the face 37.63%. Other localizations were face (non-H-zone) 6.67%, neck 3.01%, scalp 3.37%, arms 6.88% and limbs 3.51%. Of all BCCs, 0.83% were recurrent BCC. The sensitivity for diagnosis BCC was 97.71%, and positive predictive value 95.08%. Conclusion. In dermatology setting, total body skin examination (TBSE) and visual inspection with in-vivo dermoscopy results with very good diagnostic performance of BCC.

Basal cell carcinoma is a slow-growing, malignant epidermal tumor predominantly affecting sun exposed areas in Caucasians, accounting for up to 80% of all diagnosed skin cancers, with a rising incidence. Chronic UV radiation, in association with constitutional factors, plays the main role in its etiology. Inappropriate activation of the hedgehog signaling pathway seems to be a key pathogenesis mechanism. Basal cell carcinoma metastases are extremely rare, but it is a locally invasive tumor that can cause significant destruction of the surrounding tissues, with their functional and esthetic impairment. There are four main clinical types of basal cell carcinoma, although clinical classification is of poor prognostic significance. Preselection of suspicious lesions and treatment planning include noninvasive diagnostic techniques: dermoscopy, confocal microscopy and ultrasoud imaging, yet histopathology remains the “gold standard” of basal cell carcinoma diagnosis. In terms of the histological growth pattern, which is essential for the prognosis, basal cell carcinoma may be divided into circumscribed or diffuse types. Surgical excision is considered to be a first line treatment option, but there are numerous less invasive treatment modalities for low-risk basal cell carcinoma. Prevention strategies are focused on behavioral modifications, regular follow up and use of chemopreventive agents in high-risk patients.

The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches.

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