Summary Common variable immunodeficiency (CVID) is the most common severe adult primary immunodeficiency and is characterized by a failure to produce antibodies leading to recurrent predominantly sinopulmonary infections. Improvements in the prevention and treatment of infection with immunoglobulin replacement and antibiotics have resulted in malignancy, autoimmune, inflammatory and lymphoproliferative disorders emerging as major clinical challenges in the management of patients who have CVID. In a proportion of CVID patients, inflammation manifests as granulomas that frequently involve the lungs, lymph nodes, spleen and liver and may affect almost any organ. Granulomatous lymphocytic interstitial lung disease (GLILD) is associated with a worse outcome. Its underlying pathogenic mechanisms are poorly understood and there is limited evidence to inform how best to monitor, treat or select patients to treat. We describe the use of combined 2‐[(18)F]‐fluoro‐2‐deoxy‐d‐glucose positron emission tomography and computed tomography (FDG PET‐CT) scanning for the assessment and monitoring of response to treatment in a patient with GLILD. This enabled a synergistic combination of functional and anatomical imaging in GLILD and demonstrated a widespread and high level of metabolic activity in the lungs and lymph nodes. Following treatment with rituximab and mycophenolate there was almost complete resolution of the previously identified high metabolic activity alongside significant normalization in lymph node size and lung architecture. The results support the view that GLILD represents one facet of a multi‐systemic metabolically highly active lymphoproliferative disorder and suggests potential utility of this imaging modality in this subset of patients with CVID.

This study has demonstrated poor N-staging accuracy in the modern era of radiological staging. Eighty-two percent of LNMs measured <6 mm, making direct identification extremely challenging on medical imaging. Future research should focus on investigating and developing alternative surrogate markers to predict the likelihood of LNMs.

Royal Free London NHS Trust, London, UK Keywords: Hodgkin lymphoma, Hodgkin disease, lymphocyte predominant Hodgkin lymphoma.The guideline group was selected to be representative of UKbased medical experts. Ovid MEDLINE, Ovid EMBASE and DYNAMED were searched systematically for publications in English from 1980 to 2014 using the key words Hodgkin disease, Hodgkin lymphoma and lymphocyte predominant Hodgkin lymphoma. References from relevant publications were also searched. Editorials, studies with <8 cases, letters and conference abstracts were excluded. The writing group produced the draft guideline, which was reviewed by the British Committee for Standards in Haematology (BCSH) Haemato-oncology Task Force. Further comments were invited from a sounding board of the British Society for Haematology (BSH) and patient representatives identified through the Lymphoma Association.

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Purpose To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). Results OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. Conclusion Deep learning–based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.