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The growing incidence of skin cancer makes computer-aided diagnosis tools for this group of diseases increasingly important. The use of ultrasound has the potential to complement information from optical dermoscopy. The current work presents a fully automatic classification framework utilizing fully-automated (FA) segmentation and compares it with classification using two semi-automated (SA) segmentation methods. Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained on 62 features, with ten-fold cross-validation. Six classification tasks were considered, namely all the possible permutations of one class versus one or two remaining classes. The receiver operating characteristic (ROC) area under the curve (AUC) as well as the accuracy (ACC) were measured. The best classification was obtained for the classification of nevi from cancerous lesions (melanoma, basal cell carcinoma), with AUCs of over 90% and ACCs of over 85% obtained with all segmentation methods. Previous works have either not implemented FA ultrasound-based skin cancer classification (making diagnosis more lengthy and operator-dependent), or are unclear in their classification results. Furthermore, the current work is the first to assess the effect of implementing FA instead of SA classification, with FA classification never degrading performance (in terms of AUC or ACC) by more than 5%.

Background: Psoriasis is frequently accompanied by cardiovascular diseases based on the shared immunopathogenic pathway. Authors determined the effect of interleukin (IL)-17 inhibitor therapy on arterial intima-media thickness (IMT) among severe psoriatic patients. Methods: Thirty-one severe psoriatic patients were enrolled. Twenty received secukinumab and 11 received ixekizumab. Before treatment initiation and after 6 months, the carotid-brachial-femoral IMT, the Psoriasis Area Severity Index (PASI), the Dermatology Life Quality of Index (DLQI) and the EuroQol Visual Analogue Scale (EQ VAS) were evaluated. Results: After 6 months, significant ameliorations were observed in PASI (p < 0.001) from 18 to 0, in DLQI (p < 0.001) from 17 to 0, in EQ VAS (p < 0.001) from 60 to 90, in right carotid IMT (p < 0.001) from 1.1 mm to 0.8 mm, in left carotid IMT (p < 0.001) from 1.1 mm to 0.7 mm, in right brachial IMT (p < 0.001) from 0.75 mm to 0.6 mm, in left brachial IMT (p < 0.001) from 0.8 mm to 0.5 mm, in right femoral IMT (p < 0.001) from 0.9 mm to 0.7 mm and in left femoral IMT (p < 0.001) from 0.8 mm to 0.7 mm. Conclusions: By reducing the inflammation of the vascular wall, anti-IL-17 therapy may have a beneficial long-term effect on cardiovascular complications of systemic inflammation.

Abstract-Ultrasound (US) imaging of skin lesions provides information supplementary to dermoscopy and helps in improving diagnostic accuracy. The aim of the current work is to explore the feasibility of using ultrasound image features derived from radiological experience to distinguish between common skin lesions. 5-18 MHz B-mode ultrasound images were acquired of incoming patients. Images containing lesions 1-2 mm thick were selected (N=248), with histology used to diagnose suspicious lesions. 73 melanomas, 130 BCC, and 45 nevi were studied. Following semi-automatic segmentation, a number of relevant features expressing the geometry of the lesion boundary and boundary layer, as well as the image characteristics of the lesion, lesion boundary layer, and post-lesion region were considered. With the exception of lesion echogenicity, all features had an area under the curve (AUC) value of above 0.70. The AdaBoost and Support Vector Machine (SVM) classifiers were then trained and tested using cross-validation of 50 random equal populations of melanomas, BCC, and nevi; each population was then 2-fold (holdout) cross-validated 50 times. When detecting one group against two other groups, the detection of cancerous lesions fared best, with an AUC of at least 0.84 and a specificity of at least 19% at 100% sensitivity for both classifiers. The results demonstrate the potential of clinically useful ultrasound-based automatic differential diagnosis of skin lesions, which could perhaps be attained by better segmentation, having more training data, using several images of the same lesion when performing classification, as well as refinements in the definition of image features.

A compact handheld skin ultrasound imaging device has been developed that uses co-registered optical and ultrasound imaging to provide diagnostic information about the full skin depth. The aim of the current work is to present the preliminary clinical results of this device. Using additional photographic, dermoscopic and ultrasonic images as reference, the images from the device were assessed in terms of the detectability of the main skin layer boundaries and characteristic image features. Combined optical-ultrasonic recordings of various types of skin lesions (melanoma, basal cell carcinoma, seborrheic keratosis, dermatofibroma, naevus, dermatitis and psoriasis) were taken with the device (N = 53) and compared with images captured with a reference portable skin ultrasound imager. The investigator and two additional independent experts performed the evaluation. The detectability of skin structures was over 90% for the epidermis, the dermis and the lesions. The morphological and echogenicity information observed for the different skin lesions were found consistent with those of the reference ultrasound device and relevant ultrasound images in the literature. The presented device was able to obtain simultaneous in-vivo optical and ultrasound images of various skin lesions. This has the potential for further investigations, including the preoperative planning of skin cancer treatment.

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