Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Background: Methotrexate (MTX), a traditional antipsoriatic drug, is very frequently used either as monotherapy or in combination with other systemic drugs. Objectives: To assess the effectiveness and safety of MTX in psoriasis in usual clinical practice. Methods: We conducted a retrospective study. We performed an electronic and manual chart review of patients treated with MTX in the Psoriasis Unit of our Hospital from January 2007 to December 2014. Demographic and clinical data, PASI/DLQI scores and reasons for suspension of all patients treated with MTX in usual clinical practice were recorded. Results: Two hundred and eighteen patients were included. MTX was administered in 67% of cases as the first systemic treatment. The average treatment duration was 17.2 ± 13.6 months. All patients were subjected to clinical and laboratory monitoring. About 33.5% of them achieved a reduction of 75% or more of the initial PASI at week 12, 34.9% at week16, 44.7% at week 24, and 52.8% at week 48. A 3.3% had to discontinue the therapy due to analytical hepatic (2.8%) or renal (0.5%) abnormalities. Only one patient experienced severe interstitial pneumonitis and none required liver biopsy. Conclusions: MTX is an effective and safe option for the treatment of psoriasis in the real-world clinical practice.

Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints.

This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI.

Melanoma diagnosis depends on the experience of doctors. Symmetry is one of the most important factors to measure, since asymmetry shows an uncontrolled growth of cells, leading to melanoma cancer. A system for melanoma detection in diagnosing melanocytic diseases with high sensitivity is proposed here. Two different sets of features are extracted based on the importance of the ABCD rule and symmetry evaluation to develop a new architecture. Support Vector Machines are used to classify the extracted sets by using both an alternative labeling method and a structure divided into two different classifiers which prioritize sensitivity. Although feature extraction is based on former works, the novelty lies in the importance given to symmetry and the proposed architecture, which combines two different feature sets to obtain a high sensitivity, prioritizing the medical aspect of diagnosis. In particular, a database provided by Hospital Universitario de Gran Canaria Doctor Negrín was tested, obtaining a sensitivity of 100% and a specificity of 66.66% using a leave-one-out validation method. These results show that 66.66% of biopsies would be avoided if this system is applied to lesions which are difficult to classify by doctors.

Background: Tildrakizumab, an anti-IL-23p19 monoclonal antibody, has demonstrated efficacy in clinical trials, but real-world evidence remains crucial for confirming its profile in diverse populations. Methods: We have conducted a multicenter, retrospective observational study within the Spanish Psoriasis Group (GPS). This study updates previous findings with a larger sample size (n = 372) and longer follow-up. We assessed absolute Psoriasis Area and Severity Index (PASI), Body Surface Area (BSA), and the Dermatology Life Quality Index (DLQI) improvements, as well as safety, in patients with moderate-to-severe plaque psoriasis. Results: The cohort included a large population of patients with a high prevalence of comorbidities and prior biologic exposure. Effectiveness was high, with a significant proportion of patients achieving PASI < 1. Compared to recent real-world data, our cohort demonstrates superior complete clearance rates (PASI < 1) and includes a comprehensive DLQI assessment. Notably, 79 patients were aged ≥65 years, confirming the drug’s utility in the elderly. Safety was consistent with previous reports, with no new signals detected. Conclusions: Tildrakizumab shows robust effectiveness and safety in a complex, bio-experienced real-world population. The lack of clinical predictors of response suggests a need for future pharmacogenetic exploration.