Metastatic Crohn's disease (MCD) indicates the presence of non-caseating granuloma of the skin at sites separated from the gastrointestinal tract by normal tissue and is the least common dermatologic manifestation of CD. In adults, MCD usually appears after the initial diagnosis of CD in 70% of cases, whereas in children, it appears at the same time as CD in almost half of the cases. The most frequent skin lesions in adults are nodules, plaques with or without ulceration on the extremities and ulcers on the genitals. In children, genital swelling with or without erythema is the most frequent presentation of MCD. Simultaneous presence of perianal CD affects more females (60%) and particularly children. Associated gastrointestinal symptoms are present in one third of the cases in adults and in half of the cases in children. Treatment is often unsatisfactory. Randomised controlled trials are lacking. Various chemotherapeutic agents have been used such as oral metronidazole, topical and/or oral steroids, azathioprine, cyclosporine, sulfasalazine, tetracyclines, topical or systemic tacrolimus, infliximab alone or with methotrexate, and surgical treatment with oral zinc sulphate. MCD represents another 'great imitator'. This reviews the most relevant characteristics of this disease, in order to increase awareness and to avoid delay in diagnosis and improve management of the whole CD complex.

IMPORTANCE A high proportion of suspicious pigmented skin lesions referred for investigation are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the patient pathway are needed to reduce the pressure on secondary care and pathology services. OBJECTIVE To determine the accuracy of an artificial intelligence algorithm in identifying melanoma in dermoscopic images of lesions taken with smartphone and digital single-lens reflex (DSLR) cameras. DESIGN, SETTING, AND PARTICIPANTS This prospective, multicenter, single-arm, masked diagnostic trial took place in dermatology and plastic surgery clinics in 7 UK hospitals. Dermoscopic images of suspicious and control skin lesions from 514 patients with at least 1 suspicious pigmented skin lesion scheduled for biopsy were captured on 3 different cameras. Data were collected from January 2017 to July 2018. Clinicians and the Deep Ensemble for Recognition of Malignancy, a deterministic artificial intelligence algorithm trained to identify melanoma in dermoscopic images of pigmented skin lesions using deep learning techniques, assessed the likelihood of melanoma. Initial data analysis was conducted in September 2018; further analysis was conducted from February 2019 to August 2019. INTERVENTIONS Clinician and algorithmic assessment of melanoma.MAIN OUTCOMES AND MEASURES Area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of the algorithmic and specialist assessment, determined using histopathology diagnosis as the criterion standard. RESULTSThe study population of 514 patients included 279 women (55.7%) and 484 white patients (96.8%), with a mean (SD) age of 52.1 (18.6) years. A total of 1550 images of skin lesions were included in the analysis (551 [35.6%] biopsied lesions; 999 [64.4%] control lesions); 286 images (18.6%) were used to train the algorithm, and a further 849 (54.8%) images were missing or unsuitable for analysis. Of the biopsied lesions that were assessed by the algorithm and specialists, 125 (22.7%) were diagnosed as melanoma. Of these, 77 (16.7%) were used for the primary analysis.The algorithm achieved an AUROC of 90.1% (95% CI, 86.3%-94.0%) for biopsied lesions and 95.8% (95% CI, 94.1%-97.6%) for all lesions using iPhone 6s images; an AUROC of 85.8% (95% CI, 81.0%-90.7%) for biopsied lesions and 93.8% (95% CI, 91.4%-96.2%) for all lesions using Galaxy S6 images; and an AUROC of 86.9% (95% CI, 80.8%-93.0%) for biopsied lesions and 91.8% (95% CI, 87.5%-96.1%) for all lesions using DSLR camera images. At 100% sensitivity, the algorithm achieved (continued) Key Points Question How accurate is an artificial intelligence-based melanoma detection algorithm, which analyzes dermoscopic images taken by smartphone and digital single-lens reflex cameras, compared with clinical assessment and histopathological diagnosis? Findings In this diagnostic study, 1550 images of suspicious and benign skin lesions were analyzed by an artificial intelligence algorithm. When compared with histopathological diagnosis, ...

Frontal fibrosing alopecia (FFA) is a recently described inflammatory and scarring type of hair loss affecting almost exclusively women. Despite a dramatic recent increase in incidence the aetiopathogenesis of FFA remains unknown. We undertake genome-wide association studies in females from a UK cohort, comprising 844 cases and 3,760 controls, a Spanish cohort of 172 cases and 385 controls, and perform statistical meta-analysis. We observe genome-wide significant association with FFA at four genomic loci: 2p22.2, 6p21.1, 8q24.22 and 15q2.1. Within the 6p21.1 locus, fine-mapping indicates that the association is driven by the HLA-B*07: 02 allele. At 2p22.1, we implicate a putative causal missense variant in CYP1B1 , encoding the homonymous xenobiotic- and hormone-processing enzyme. Transcriptomic analysis of affected scalp tissue highlights overrepresentation of transcripts encoding components of innate and adaptive immune response pathways. These findings provide insight into disease pathogenesis and characterise FFA as a genetically predisposed immuno-inflammatory disorder driven by HLA-B*07: 02.

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MMS with frozen sections is reliable for treating primary MAC in which PNI is not present on a diagnostic biopsy. Previous surgery and PNI were associated with greater risk of persistence in periocular MAC. In these patients, it may be appropriate to consider MMS with paraffin-embedded sections, possibly as a layer after apparent clearance on frozen sections. Further excision of orbital contents should be considered in periocular MAC that infiltrate the deep orbital fat or are noted to have PNI.

Background: Malignant melanoma can most successfully be cured when diagnosed at an early stage in the natural history. However, there is controversy over screening programs and many advocate screening only for high-risk individuals. Objectives: This study aimed to evaluate the accuracy of an artificial intelligence neural network (Deep Ensemble for Recognition of Melanoma [DERM]) to identify malignant melanoma from dermoscopic images of pigmented skin lesions and to show how this compared to doctors’ performance assessed by meta-analysis. Methods: DERM was trained and tested using 7,102 dermoscopic images of both histologically confirmed melanoma (24%) and benign pigmented lesions (76%). A meta-analysis was conducted of studies examining the accuracy of naked-eye examination, with or without dermoscopy, by specialist and general physicians whose clinical diagnosis was compared to histopathology. The meta-analysis was based on evaluation of 32,226pigmented lesions including 3,277 histopathology-confirmed malignant melanoma cases. The receiver operating characteristic (ROC) curve was used to examine and compare the diagnostic accuracy. Results: DERM achieved a ROC area under the curve (AUC) of 0.93 (95% confidence interval: 0.92-0.94), and sensitivity and specificity of 85.0% and 85.3%, respectively. Avoidance of false-negative results is essential, so different decision thresholds were examined. At 95% sensitivity DERM achieved a specificity of 64.1% and at 95% specificity the sensitivity was 67%. The meta-analysis showed primary care physicians (10 studies) achieve an AUC of 0.83 (95% confidence interval: 0.79-0.86), with sensitivity and specificity of 79.9% and 70.9%; and dermatologists (92 studies) 0.91 (0.88-0.93), 87.5%, and 81.4%, respectively. Conclusions: DERM has the potential to be used as a decision support tool in primary care, by providing dermatologist-grade recommendation on the likelihood of malignant melanoma.