Multimodality Imaging Features of Immunoglobulin G4-related Vessel Involvement

Immunoglobulin 4 (IgG4)-related disease is a chronic immune-mediated fibroinflammatory disorder. Involvement of the vascular system, including large- and medium-sized vessels, is increasingly recognized. The varied appearances of vascular involvement reflect the sequela of chronic inflammation and fibrosis and can include aortitis and periaortitis with resultant complications such as aneurysm formation and dissection. A diagnosis of IgG4-related large vessel involvement should be considered when there is known or suspected IgG4-related disease elsewhere. Other organs that are typically affected in IgG4-related disease include the lacrimal and salivary glands, thyroid, pancreas, biliary tree, lungs, kidneys, and meninges. Diagnosis typically requires careful correlation with clinical, imaging, serum, and pathologic findings. Patients may be managed with corticosteroid therapy or the anti-CD20 monoclonal antibody, rituximab, if needed. The varied clinical presentations and imaging features of large vessel involvement are discussed herein. Keywords: Vascular, Inflammation, Aorta, IgG4-related Vessel Involvement © RSNA, 2024.

Neurolymphomatosis in Recrudescent Diffuse Large B-cell Lymphoma

Neurolymphomatosis is an uncommon manifestation of lymphoma, often presenting with painful polyneuropathy or polyradiculopathy and concomitant distal extremity weakness. Differentiation from other etiologies resulting in similar neuropathic symptoms such as compressive or inflammatory pathologies can be difficult and often results in delayed diagnosis. Here we describe a case of neurolymphomatosis affecting a 64-year-old man with a history of diffuse large B-cell lymphoma (DLBCL) in remission presenting with a right-sided foot drop following a gunshot wound. MRI at that time demonstrated thickening and enhancement of the cauda equina nerve roots. Over the course of the subsequent eight months, he developed left lower extremity sensory symptoms, left-sided foot drop and signs of upper motor neuron involvement, including left facial weakness, dysphonia, and dysphagia. ¹⁸F-FDG PET/CT revealed intensely avid left lumbosacral nerve roots, bilateral lower extremity and left upper extremity neurovascular bundles. Left sural nerve biopsies showed infiltration of DLBCL and confirmed neurolymphomatosis. We highlight the role of ¹⁸F-FDG PET/CT, with histological verification, for the diagnosis of an extended course of neurolymphomatosis occurring in the absence of typical painful neuropathy but with cranial and peripheral neuropathies.

Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks

PURPOSE

To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction.

MATERIALS AND METHODS

A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease severity on CXRs (pulmonary x-ray severity (PXS) score), using weakly-supervised pretraining on ∼160,000 anterior-posterior images from CheXpert and transfer learning on 314 frontal CXRs from COVID-19 patients. The algorithm was evaluated on internal and external test sets from different hospitals (154 and 113 CXRs respectively). PXS scores were correlated with radiographic severity scores independently assigned by two thoracic radiologists and one in-training radiologist (Pearson r). For 92 internal test set patients with follow-up CXRs, PXS score change was compared to radiologist assessments of change (Spearman ρ). The association between PXS score and subsequent intubation or death was assessed. Bootstrap 95% confidence intervals (CI) were calculated.

RESULTS

PXS scores correlated with radiographic pulmonary disease severity scores assigned to CXRs in the internal and external test sets (r=0.86 (95%CI 0.80-0.90) and r=0.86 (95%CI 0.79-0.90) respectively). The direction of change in PXS score in follow-up CXRs agreed with radiologist assessment (ρ=0.74 (95%CI 0.63-0.81)). In patients not intubated on the admission CXR, the PXS score predicted subsequent intubation or death within three days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75-0.85)).

CONCLUSION

A Siamese neural network-based severity score automatically measures radiographic COVID-19 pulmonary disease severity, which can be used to track disease change and predict subsequent intubation or death.

Advances in clinical MRI technology

Advances in MRI technologies have the potential to detect, characterize, and monitor a wide variety of diseases.