Nonmelanoma Skin Cancer of the Head and Neck Clinical Evaluation and Histopathology

WOS: 000311860700004PubMed ID: 23084295Clinical and histopathologic features of nonmelanoma skin cancer, physical examination, and diagnostic methods (biopsy, dermoscopy, confocal microscopy) are summarized. A diagnostic algorithm provides a useful summarization of differential diagnosis of basal cell carcinoma, actinic keratosis, Bowen's disease, and squamous cell carcinoma

White fibrous papulosis of the neck

WOS: 000287249400011PubMed ID: 20954818A 56-year-old white man with multiple, discrete nonfollicular papules on the neck is presented. Clinical and histopathologic features were compatible with the entity of white fibrous papulosis of the neck (WFPN). Pseudoxanthoma elasticum--like papillary dermal elastolysis (PXE-PDE) and WFPN are further clinicopathologic patterns of intrinsic aging. Clinically, WFPN is characterized by isolated, whitish papules, whereas those of PXE-PDE are yellowish and often coalesce to form ""cobblestone"" plaques. Our case showed clearly marginated whitish papules. The major histopathologic feature of WFPN is superficial dermal fibrosis with scant elastolysis; in PXE-PDE, there is papillary dermal elastolysis but no sign of fibrosis. No recurrence was performed in the 3 years'' follow-up in our case. Surgical treatment may be considered in such cases with well-circumscribed lesions.</

Trichilemmal cyst with homogeneous blue pigmentation on dermoscopy

WOS: 000271495600015PubMed ID: 19916979A 61-year-old woman was referred to our dermoscopy unit for a pigmented lesion that had been present on her left arm for 8 years. The patient did not notice any enlargement or change in colour. On dermoscopy, homogeneous blue pigmentation was seen. The lesion was excised with the pre-operative diagnosis of melanoma, blue naevus and dermatofibroma. Histopathological examination showed a trichilemmal cyst in the mid-dermis. Although homogeneous blue pigmentation on dermoscopy is the hallmark of blue naevus, it may be seen in metastatic melanoma and exceptionally in hemosiderotic and cellular types of dermatofibroma. Trichilemmal cyst should be borne in mind also in the dermoscopic differential diagnosis

Androphenotypic features in patients with coronary artery disease

Objective: It has been a debate whether phenotypic features are associated with increased risk of coronary heart disease. Proposed explanations for this relation include biological aging, individual susceptibilities, and androgens which contribute to both the atherosclerotic process and dermatological signs. The results of the studies are inconsistent and most are not based on cardiovascular imaging techniques. Here, association between androphenotypic features and the risk and severity of coronary artery disease (CAD) in men is evaluated. Methods: This case-control study consists of 166 male patients with angiography-proven CAD and 160 age-gender-matched controls. Gensini score of angiograms (for severity of CAD) and phenotypic characteristics including androgenetic alopecia (AGA), thoracic hairiness (TH), hair graying a diagonal earlobe crease (DEC), and hairy ear (HE) were recorded. Men with well-established cardiovascular risk factors were excluded. Results: AGA, DEC, and HE were significantly more frequent in patients with CAD than controls (98.2% and 83.1% [P < 0.001], 61.4% and 23.8% [P < 0.001], 69.3% and 50.6% [P = 0.001], respectively). As the severity of AGA increased, the incidence of heart disease was increasing in patients. The presence of TH and AGA was found to be related to higher Gensini scores. Conclusion: The exact mechanism between these phenotypic features and CAD still remains to be elucidated. However, observation of visible aging signs is easy and inexpensive. AGA, HE, and DEC may be used as early screening tools for CAD

MobileSkin: Classification of Skin Lesion Images Acquired Using Mobile Phone-Attached Hand-Held Dermoscopes

Dermoscopy is the visual examination of the skin under a polarized or non-polarized light source. By using dermoscopic equipment, many lesion patterns that are invisible under visible light can be clearly distinguished. Thus, more accurate decisions can be made regarding the treatment of skin lesions. The use of images collected from a dermoscope has both increased the performance of human examiners and allowed the development of deep learning models. The availability of large-scale dermoscopic datasets has allowed the development of deep learning models that can classify skin lesions with high accuracy. However, most dermoscopic datasets contain images that were collected from digital dermoscopic devices, as these devices are frequently used for clinical examination. However, dermatologists also often use non-digital hand-held (optomechanical) dermoscopes. This study presents a dataset consisting of dermoscopic images taken using a mobile phone-attached hand-held dermoscope. Four deep learning models based on the MobileNetV1, MobileNetV2, NASNetMobile, and Xception architectures have been developed to classify eight different lesion types using this dataset. The number of images in the dataset was increased with different data augmentation methods. The models were initialized with weights that were pre-trained on the ImageNet dataset, and then they were further fine-tuned using the presented dataset. The most successful models on the unseen test data, MobileNetV2 and Xception, had performances of 89.18% and 89.64%. The results were evaluated with the 5-fold cross-validation method and compared. Our method allows for automated examination of dermoscopic images taken with mobile phone-attached hand-held dermoscopes