Objective To improve the vigilance and awareness of malignancy presenting as dermatosis and reduce misdiagnosis. Methods Two cases of gastric cancer presenting as dermatomyositis and erythroderma respectively in the last two years were retrospectively analyzed and the relevant literatures were reviewed. Results The two patients were admitted to hospital due to skin diseases， diagnosis of gastric cancer through endoscopy， and proved to be gastric cancer associated with dermatosis by pathological examination after surgical resection. Conclusions Paraneoplastic dermatoses can be seen as an early manifestation of the internal malignancy. The patients with paraneoplastic dermatoses should be excluded visceral tumors by the means of biomarkers， endoscopy， PET/CT， and so on.
Six kinds of erythemato-squamous diseases have been common skin diseases, but the diagnosis of them has always been a problem. The quantitative data processing method is not suitable for erythemato-squamous data because they are categorical qualitative data. This paper proposed a new method based on group lasso penalized classification for the feature selection and classification for erythemato-squamous data with categorical qualitative data. The first categorical data of 33 dimensions were changed by the virtual code, and then 34th dimension age data were discretized and changed by the virtual code. Then the encoded data were grouped according to class group and variable group. Lastly Group Lasso penalized classification was executed. The classified accuracy of 10-fold cross validation was 98.88%±0.0023%. Compared with those of other method in the literature, this new method is simpler, and better for effect and efficiency, and has stronger interpretability and stronger stability.
Erythemato-squamous diseases are a general designation of six common skin diseases, of which the differential diagnosis is a difficult problem in dermatology. This paper presents a new method based on virtual coding for qualitative variables and multinomial logistic regression penalized via elastic net. Considering the attributes of variables, a virtual coding is applied and contributes to avoid the irrationality of calculating nominal values directly. Multinomial logistic regression model penalized via elastic net is thence used to fit the correlation between the features and classification of diseases. At last, parameter estimations can be attained through coordinate descent. This method reached accuracy rate of 98.34%±0.0027% using 10-fold cross validation in the experiments. Our method attained equivalent accuracy rate compared to the results of other methods, but steps are simpler and stability is higher.
The latest global big data evidence indicated the changes of skin and venereal disease burden was huge. HIV/AIDS disease burden was the heaviest diseases among all skin and venereal diseases, and its skin manifestation was serious. The evidence of skin manifestation was searched and classified by subjects such as clinical symptoms, diagnosis & treatment, nursing, etc. The results showed, that the skin manifestation of HIV/AIDS with high incidence was serious, atypical, difficult to cure which was easy to misdiagnose or miss diagnosis. After analyzing the global HIV/AIDS guidelines, we found that many high quality guidelines with widely-covered subjects were produced by developed countries, while quite a few low quality and ones with narrowly-covered subjects were produced by developing countries. Only one guideline was for treatment of HIV/AIDS skin lesion. Based on the current evidence, we call for that all healthcare professionals to increase their awareness, update knowledge, and joint in cooperative prevention and treatment of HIV/AIDS. We also call for that we should produce high quality primary evidence for clinical diagnosis and treatment of HIV/AIDS skin manifestation, and clinical practice guidelines based on good evidence. For the increasing heavy burden of skin and venereal diseases, we should adjust and expand research directions, enrich and improve new interdisciplinary knowledge. We also should constantly train professionals and spread out knowledge in public on prevention and treatment for skin manifestation, so as to transform the evidence in time, effectively protect medical staff and susceptible population, effectively prevent and treat this disease, and improve the satisfaction of our country, hospitals and patients.
Cutaneous squamous cell carcinoma (cSCC) is the second most common human skin tumor. In recent years, the incidence of cSCC is increasing annually. Although most cSCC is curable after basic treatment, the advanced cSCC progresses rapidly and poses a significant risk for the impact on quality of life and death. In 2017, the latest version of cSCC management guideline was developed by the American Academy of Dermatology (AAD) based on extensive evidence-based medical evidence, including cSCC biopsy techniques, histopathological assessment, clinical staging and grading, surgical and nonsurgical treatment, follow-up, recurrence prevention, and management of the advanced cSCC. The purpose of this article is to briefly introduce and interpretate this guideline.
The skin is the largest organ of the human body, and many visceral diseases will be directly reflected on the skin, so it is of great clinical significance to accurately segment the skin lesion images. To address the characteristics of complex color, blurred boundaries, and uneven scale information, a skin lesion image segmentation method based on dense atrous spatial pyramid pooling (DenseASPP) and attention mechanism is proposed. The method is based on the U-shaped network (U-Net). Firstly, a new encoder is redesigned to replace the ordinary convolutional stacking with a large number of residual connections, which can effectively retain key features even after expanding the network depth. Secondly, channel attention is fused with spatial attention, and residual connections are added so that the network can adaptively learn channel and spatial features of images. Finally, the DenseASPP module is introduced and redesigned to expand the perceptual field size and obtain multi-scale feature information. The algorithm proposed in this paper has obtained satisfactory results in the official public dataset of the International Skin Imaging Collaboration (ISIC 2016). The mean Intersection over Union (mIOU), sensitivity (SE), precision (PC), accuracy (ACC), and Dice coefficient (Dice) are 0.901 8, 0.945 9, 0.948 7, 0.968 1, 0.947 3, respectively. The experimental results demonstrate that the method in this paper can improve the segmentation effect of skin lesion images, and is expected to provide an auxiliary diagnosis for professional dermatologists.
Skin cancer is a significant public health issue, and computer-aided diagnosis technology can effectively alleviate this burden. Accurate identification of skin lesion types is crucial when employing computer-aided diagnosis. This study proposes a multi-level attention cascaded fusion model based on Swin-T and ConvNeXt. It employed hierarchical Swin-T and ConvNeXt to extract global and local features, respectively, and introduced residual channel attention and spatial attention modules for further feature extraction. Multi-level attention mechanisms were utilized to process multi-scale global and local features. To address the problem of shallow features being lost due to their distance from the classifier, a hierarchical inverted residual fusion module was proposed to dynamically adjust the extracted feature information. Balanced sampling strategies and focal loss were employed to tackle the issue of imbalanced categories of skin lesions. Experimental testing on the ISIC2018 and ISIC2019 datasets yielded accuracy, precision, recall, and F1-Score of 96.01%, 93.67%, 92.65%, and 93.11%, respectively, and 92.79%, 91.52%, 88.90%, and 90.15%, respectively. Compared to Swin-T, the proposed method achieved an accuracy improvement of 3.60% and 1.66%, and compared to ConvNeXt, it achieved an accuracy improvement of 2.87% and 3.45%. The experiments demonstrate that the proposed method accurately classifies skin lesion images, providing a new solution for skin cancer diagnosis.