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DUSP22 gene rearrangements are recurrent in systemic and cutaneous ALK-negative anaplastic large cell lymphomas, rarely encountered in other cutaneous CD30+ lymphoproliferations, and typically absent in other peripheral T-cell lymphomas. We report the case of a 51-year-old woman, with longstanding celiac disease and a rapidly enlarging leg ulcer, due to a DUSP22-rearranged CD30+ T-cell lymphoproliferation. Subsequent history revealed an intestinal enteropathy-associated T-cell lymphoma (EATL). Identical monoclonal TR gene rearrangements and mutations in STAT3 and JAK1 typical of EATL were present in the cutaneous and intestinal lesions. No DUSP22 rearrangement was detected in the patient’s intestinal tumour, nor in 15 additional EATLs tested. These findings indicate that DUSP22 rearrangements are not entirely specific of ALCLs, may rarely occur as a secondary aberration in EATL, and expand the differential diagnosis of DUSP22-rearranged cutaneous CD30+ lymphoproliferative disorders.

The second edition of Skin Cancer: Recognition and Management is a definitive clinical reference which comprehensively examines the wide range of premalignant and malignant cutaneous disorders, including melanoma, Kaposi's sarcoma and other sarcomas, cutaneous lymphoma, cutaneous metastatic disease and cutaneous markers of internal malignancy, with emphasis on the most recent advances in diagnosis and management. Fully revised and expanded, this new edition now includes full colour photographs and illustrations throughout to aid recognition and diagnosis, and covers the latest developments and treatment modalities. New chapters include: Merkel Cell Carcinoma Dermoscopy Skin Cancer: Recognition and Management is a definitive clinical reference for dermatologists, oncologists, residents and any medical practitioner with an interest in skin cancer.

Background Microsecretory adenocarcinoma (MSA) is a newly identified entity in the WHO classification of salivary gland tumors characterized by MEF2C::SS18 fusion. It was previously considered as adenocarcinoma not otherwise specified (NOS). With the discovery of new gene fusions specifying distinct salivary gland tumors and restricting the diagnosis of adenocarcinoma NOS, five cases of MSA were recognized for the first time using targeted RNA sequencing. Afterwards, further authors reported MSA in the salivary glands and more recently in the skin. Methods We reviewed the literature for all cases of MSA reported in English-language articles. We comprehensively discussed clinical, histopathological, immunohistochemical and molecular findings of the retrieved cases. Results Forty cases were identified. Thirty cases occurred in the salivary glands and ten cases occurred in the skin. They were characterized histologically by a well circumscribed mass formed of microcysts containing basophilic secretions and enclosed in a fibromyxoid stroma. The tumor cells were flattened resembling intercalated duct cells with minimal eosinophilic cytoplasm and small oval nuclei. By immunohistochemistry, the tumor cells were positive for SOX10, S100, p63 and negative for p40, calponin and mammaglobin. However, cutaneous cases had a somewhat different immunoprofile. Conclusion MSA is a salivary gland malignancy that also has a cutaneous counterpart. Focusing on emphasising the almost consistent histopathological and immunohistochemical findings help in increasing the awareness of clinicians, surgeons and pathologists about it and at the same time lessening the need for more complicated diagnostic methods that are not readily available in all institutions. Despite the low-grade nature of this tumor, thorough management and rigorous follow up of cases are highly recommended due to occasional aggressive behaviour.

Skin adnexal tumors, those neoplasms deriving from hair follicles and sweat glands, are often a source of confusion amongst even experienced pathologists. Many well-described entities have overlapping features, tumors are often only partially sampled, and many cases do not fit neatly into well-established classification schemes. To simplify categorization of adnexal tumors for the general surgical pathologist and to shed light on many of the diagnostic dilemmas commonly encountered in daily practice. The following review breaks adnexal neoplasms into 3 groups: sebaceous, sweat gland-derived, and follicular. Pathology reference texts and primary literature regarding adnexal tumors. Review of the clinical and histopathologic features of primary cutaneous adnexal tumors, and the diagnostic dilemmas they create, will assist the general surgical pathologist in diagnosing these often challenging lesions.

Cutaneous melanoma is a malignancy arising from melanocytes of the skin. Incidence rates are rising, particularly in White populations. Cutaneous melanoma is typically driven by exposure to ultraviolet radiation from natural sunlight and indoor tanning, although there are several subtypes that are not related to ultraviolet radiation exposure. Primary melanomas are often darkly pigmented, but can be amelanotic, with diagnosis based on a combination of clinical and histopathological findings. Primary melanoma is treated with wide excision, with margins determined by tumour thickness. Further treatment depends on the disease stage (following histopathological examination and, where appropriate, sentinel lymph node biopsy) and can include surgery, checkpoint immunotherapy, targeted therapy, or radiotherapy. Systemic drug therapies are recommended as an adjunct to surgery in patients with resectable locoregional metastases and are the mainstay of treatment in advanced melanoma. Management of advanced melanoma is complex, particularly in those with cerebral metastasis. Multidisciplinary care is essential. Systemic drug therapies, particularly immune checkpoint inhibitors, have substantially increased melanoma survival following a series of landmark approvals from 2011 onward.

An artificial intelligence trained to classify images of skin lesions as benign lesions or malignant skin cancers achieves the accuracy of board-certified dermatologists. Neural network identifies skin cancers Andre Esteva et al . used 129,450 clinical images of skin disease to train a deep convolutional neural network to classify skin lesions. The result is an algorithm that can classify lesions from photographic images similar to those taken with a mobile phone. The accuracy of the system in detecting malignant melanomas and carcinomas matched that of trained dermatologists. The authors suggest that the technique could be used outside the clinic as a visual screen for cancer. Skin cancer, the most common human malignancy 1 , 2 , 3 , is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) 4 , 5 show potential for general and highly variable tasks across many fine-grained object categories 6 , 7 , 8 , 9 , 10 , 11 . Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets 12 —consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13 ) and can therefore potentially provide low-cost universal access to vital diagnostic care.

The incidence of melanoma of the skin is 6 times as high as it was 40 years ago; mortality has stayed low. UV light exposure is the strongest environmental risk factor, but its magnitude gives a relative risk of about 2. The most likely factors influencing the increase are changing thresholds to biopsy pigmented lesions and to label the morphologic change as melanoma.

Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions. A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practice