An improved transformer network for skin cancer classification.

BACKGROUND: Use of artificial intelligence to identify dermoscopic images has brought major breakthroughs in recent years to the early diagnosis and early treatment of skin cancer, the incidence of which is increasing year by year worldwide and poses a great threat to human health. Achievements have been made in the research of skin cancer image classification by using the deep backbone of the convolutional neural network (CNN). This approach, however, only extracts the features of small objects in the image, and cannot locate the important parts. OBJECTIVES: As a result, researchers of the paper turn to vision transformers (VIT) which has demonstrated powerful performance in traditional classification tasks. The self-attention is to improve the value of important features and suppress the features that cause noise. Specifically, an improved transformer network named SkinTrans is proposed. INNOVATIONS: To verify its efficiency, a three step procedure is followed. Firstly, a VIT network is established to verify the effectiveness of SkinTrans in skin cancer classification. Then multi-scale and overlapping sliding windows are used to serialize the image and multi-scale patch embedding is carried out which pay more attention to multi-scale features. Finally, contrastive learning is used which makes the similar data of skin cancer encode similarly so that the encoding results of different data are as different as possible. MAIN RESULTS: The experiment is carried out based on two datasets, namely (1) HAM10000: a large dataset of multi-source dermatoscopic images of common skin cancers; (2)A clinical dataset of skin cancer collected by dermoscopy. The model proposed has achieved 94.3% accuracy on HAM10000 and 94.1% accuracy on our datasets, which verifies the efficiency of SkinTrans. CONCLUSIONS: The transformer network has not only achieved good results in natural language but also achieved ideal results in the field of vision, which also lays a good foundation for skin cancer classification based on multimodal data. This paper is convinced that it will be of interest to dermatologists, clinical researchers, computer scientists and researchers in other related fields, and provide greater convenience for patients.

Value of a Signature of Immune-Related Genes in Predicting the Prognosis of Melanoma and Its Responses to Immune Checkpoint Blocker Therapies.

Melanoma is becoming increasingly common worldwide, with high rates of transformation into malignancy compared to other skin lesions. The prognosis of patients with melanoma at an advanced stage is highly unsatisfying despite the development of immunotherapy, target therapy, or combinative therapy. The major barrier to exploiting immune checkpoint therapies and achieving the best benefits clinically is resistance that can easily develop if regimens are not selected appropriately. In this study, we investigated the possibility of using immune-related genes to predict patient survival and their responses to immune checkpoint blocker therapies with the expression profiles available at The Cancer Genome Atlas (TCGA) Program plus expression data from the Gene Expression Omnibus (GEO) for validation. A five gene signature that is highly correlated with the local infiltration of cytotoxic lymphocytes in the tumor microenvironment was identified, and a scoring model was developed with stepwise regression after multivariate Cox analyses. The score calculated strongly correlates with Breslow depth, and this model effectively predicts the prognosis of patients with melanoma, whether primary or metastasized. It also depicts the heterogenous immune-related nature of melanoma by revealing different predicted responses to immune checkpoint blocker therapies through its correlation to tumor immune dysfunction and exclusion (TIDE) score.

The risk of non-melanoma skin cancer in HIV-infected patients: new data and meta-analysis.

The role of HIV/AIDS in non-melanoma skin cancer (NMSC) is not well defined. We sought to update the evidence of the association between HIV/AIDS and risk of NMSC by gender and antiretroviral therapy (ART). We searched MEDLINE and EMBASE on 29 February 2014. Standardised incidence ratios with corresponding 95% confidence intervals were extracted and combined using generic inverse variance methods assuming a random effects model. Six studies including 78,794 patients with HIV/AIDS fulfilled the inclusion criteria. Analysis of all studies showed that HIV/AIDS was associated with an increased risk of NMSC (standardised incidence ratio 2.76; 95% confidence interval 2.55-2.98). The standardised incidence ratios of NMSC were 3.63 (1.08-12.22) for men and 2.18 (1.24-3.83) for women with HIV/AIDS, respectively. In analysis stratified by ART, we found that individuals receiving ART had lower risk of developing NMSC than individuals who had not received ART (standardised incidence ratio, 95% confidence interval; 1.95 [1.10-3.47] versus 2.11 [1.44-3.12]). HIV/AIDS is associated with an increased risk of NMSC in both male and female patients. The use of ART appears to be beneficial in protecting against the development of NMSC.

Kissing nevus of the penis. Report of two cases and review of the literature.

Kissing nevus is a curious type of nevus that was first described on the eyelids and rarely described on the penis. We report two cases of kissing nevus of the penis and review previously reported cases. The lesions of the kissing nevus of the penis showed characteristic mirror-image symmetry relative to the coronal sulcus. On histopathology, the lesion showed a compound nevus.

Kissing nevus of the penis. Report of two cases and review of the literature

Kissing nevus is a curious type of nevus that was first described on the eyelids and rarely described on the penis. We report two cases of kissing nevus of the penis and review previously reported cases. The lesions of the kissing nevus of the penis showed characteristic mirror-image symmetry relative to the coronal sulcus. On histopathology, the lesion showed a compound nevus.