Melanoma and basal cell carcinoma (BCC) are common cancerous skin lesions, and their incidence is steadily increasing. 1,2 Although both neoplasms require treatment, their associated mortality differs greatly causing a difference in urgency and rapidity of treatment. 2,3 Thus, adequate differentiation between melanoma and BCC is essential.However, differentiating melanoma from pigmented BCC may sometimes be challenging as clinical, and dermoscopic features of both entities may mimic one another. 4 For these equivocal lesions, more sophisticated non-invasive imaging techniques such as reflectance confocal microscopy (RCM) may provide significant help, 4,5 whereas the role of conventional OCT seems to be more marginal in this particular differential diagnosis. 6 Line-field confocal optical coherence tomography (LC-OCT) is a newly introduced non-invasive imaging tool combining the technical advantages of RCM (high isotropic resolution) and conventional OCT (high in-tissue penetration). It provides images in both vertical and horizontal planes (depth 500 µm), which enables tridimensional (3D) F I G U R E 1 Nodular melanoma on the lateral right upper arm of a 62-year-old woman: clinical (A) and dermoscopic (B) presentation.
Basal cell carcinoma (BCC) is one of the most common forms of cancer, 1-3 with a rising incidence worldwide. To date, histopathological examination of a punch biopsy is the gold standard to distinguish BCC from alternative diagnoses and to determine the BCC subtype. 1,4 However, a punch biopsy is an invasive procedure, with risks of pain and bleeding during the procedure and the additional chance of infection and/or scarring. 5 Moreover, awaiting histopathological assessment may be stressful for many patients. Considering the rising incidence of BCC, which causes a major burden on healthcare systems, finding alternatives to an invasive biopsy is desired.The last century has seen many advances in medical imaging from the first X-ray to more sophisticated imaging techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI). 6 This progress, concurrent with advances in computational power and machine learning techniques, often termed artificial intelligence (AI) in the literature, eased the transformation of medical images into quantitative minable data that could be used to build diagnostic, predictive and prognostic clinical decision support systems (cDSS). 7,8 Quantitative imaging analysis techniques that are being extensively investigated to develop cDSS include handcrafted radiomics and deep learning. Handcrafted radiomic features
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