The Spectrum of Cutaneous Reactive Angiomatoses in End Stage Renal Failure.

ABSTRACT: Cutaneous reactive angiomatoses encompass a spectrum of conditions driven by underlying occlusive vasculopathy. We present 2 cases of reactive angioproliferation in the context of end-stage renal failure (ESRF) manifesting as painful cutaneous ulceration. The first case demonstrates histologic features of diffuse dermal angiomatosis. The second case illustrates a nonspecific pattern of vascular proliferation which does not conform to any of the classically described subtypes, therefore reinforcing the concept of a spectrum of histopathologic changes in reactive angioproliferations. Diffuse dermal angiomatosis has been described in ESRF alone and in association with calciphylaxis, a life-threatening condition. Recognizing the patterns of reactive angioproliferation in the context of ESRF therefore has clinical relevance. Earlier identification may facilitate mitigation of risk factors and improve prognosis.

Systematic Review of Patient-Reported Outcome Measures in Graft-versus-Host Disease.

Graft-versus-host disease (GVHD) is a major cause of morbidity and mortality after allogeneic stem cell transplantation. These patients face a unique challenge due to the complexity of GVHD and the toxicity of treatments received. GVHD has significant impact on quality of life (QOL), but this is not routinely evaluated formally. Despite the availability of patient-reported outcome measures (PROMs) to assess QOL, there is currently no consensus regarding the optimal PROMs that should be used to evaluate the impact of GVHD. We undertook a systematic review to determine the current evidence for the use of PROMs in assessment of QOL, symptom burden, and disease severity of patients with GVHD. A comprehensive systematic review based on the COSMIN guidelines was conducted to identify studies using PROMs (including those for QOL and symptom burden) in acute and chronic GVHD (cGVHD) patients. The following databases were searched: OVID Medline, AMED, CINAHL, Embase, PROQOLID, ProQuest, PsychINFO, and Social Sciences Citation Index from inception to May 2018. Hand searches updated the search to December 2018. Articles were screened by 2 independent reviewers, with discrepancies resolved by a third independent reviewer. Included articles were critically appraised using the COSMIN Risk of Bias tool, and relevant data on measurement properties for the included PROMs were extracted from within the target population. A total of 4545 articles were identified, and 64 articles reporting on 27 PROMs were included in this review. PROMs were separated into 5 groups; generic patient-reported measures (n = 7), cancer-specific measures (n = 4), bone marrow transplant-specific measures (n = 2), cGVHD-specific measures (n = 4), and dimension-specific measures (n = 10). Three PROMs (Human Activity Profile, Lee Symptom Scale, National Institutes of Health Eleven Point Scale) had evidence to support strong reliability (including internal consistency), responsiveness, and aspects of validity within the cGVHD population. Only 5 included PROMs were used in patients with acute GVHD. This review summarizes the current evidence regarding the use of 27 included PROMs in the context of GVHD. The choice of the most optimal PROM depends on the clinical or research context of use. Future research should comprehensively validate these tools in the GVHD population, including the testing and possible development of a PROM for use in acute GVHD, which remains a current critical gap in the existing literature.

Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions.

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. Results: The 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 a specificity of 64.8% with iPhone 6s images. Specialists achieved an AUROC of 77.8% (95% CI, 72.5%-81.9%) and a specificity of 69.9%. Conclusions and Relevance: In this study, the algorithm demonstrated an ability to identify melanoma from dermoscopic images of selected lesions with an accuracy similar to that of specialists.

A rare but potentially fatal cause of diarrhoea and weight loss: enteropathy-associated T-cell lymphoma.

Enteropathy-associated T-cell lymphoma (EATL) is a rare but potentially fatal cause of diarrhoea and weight loss. EATL commonly presents with abdominal pain, diarrhoea and weight loss, but can also present with complications such as bowel obstruction and perforation. It is a tumour of intraepithelial lymphocytes that occurs in a relatively young population. It is the most common neoplastic complication of coeliac disease, but can occur with no prior diagnosis of coeliac disease. This case demonstrates the difficulties that can be faced in diagnosing this disorder, particularly when there is no preceding history of coeliac disease. Early diagnosis is of utmost importance in order to start treatment before the effects of malnutrition increase the risk of complications from chemotherapy. Hence awareness of the condition among general medical physicians, to whom it will often present first, is essential. However, even with prompt diagnosis, outcomes for this condition remain poor.