Distinct features of ANCA-associated hypertrophic pachymeningitis compared to other etiologies: findings from a study of 74 patients.

OBJECTIVES: We aimed to characterize the clinical and radiological features, and outcomes, of a large cohort of hypertrophic pachymeningitis (HP) patients from a single center. METHODS: We conducted a retrospective study at a tertiary referral center, encompassing patients diagnosed with HP between 2003 and 2022. The diagnosis of HP relied on the identification of thickening of the dura mater via magnetic resonance imaging (MRI) of the brain or spine. RESULTS: We included 74 patients with a mean age of 43.6 ± 14.2 years, of whom 37 (50%) were male. Among them, 32 (43.2%) had an immune-mediated origin, including 21 with granulomatosis with polyangiitis (GPA) (predominantly PR3-ANCA positive), four with systemic lupus erythematosus, three with IgG4-related disease, three with idiopathic HP, and one with rheumatoid arthritis. Non-immune-mediated HP accounted for 45 cases (56.8%). Within this category, 21 (28.4%) were infectious cases, with 14 being Mycobacterium tuberculosis infection (TB-HP), and 21 (28.4%) were malignancy-associated HP. Clinical and MRI characteristics exhibited variations among the four etiological groups. Hypoglycorrhachia was primarily observed in infectious and malignancy-associated HP. Immune-mediated HP was associated with a peripheral pattern of contrast enhancement and the Eiffel-by-night sign. MRI features strongly indicative of TB-HP included leptomeningeal involvement, brain parenchymal lesions, and arterial stroke. MPO-ANCA GPA was associated with a higher prevalence of spinal HP. CONCLUSIONS: Within our cohort, GPA and Mycobacterium tuberculosis emerged as the predominant causes of HP. We identified significant disparities in clinical and radiological features among different etiologies, which could have implications for diagnosis.

Posterior reversible encephalopathy syndrome during convalescence from COVID-19.

Background and aim: With an ever-increasing population of patients recovering form severe coronavirus disease 2019 (COVID-19), recognizing long-standing and delayed neurologic manifestations is crucial. Here, we present a patient developing posterior reversible encephalopathy syndrome (PRES) in the convalescence form severe coronavirus disease 2019 (COVID-19).Case presentation: A 61-year-old woman with severe (COVID-19) confirmed by nasopharyngeal real-time reverse transcription-polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) required invasive mechanical ventilation 24-hours after admission. During her intensive care unit stay, she developed transient acute kidney injury and septic shock. She was extubated after 22 days. On day 25, she developed generalized tonic-clonic seizures. Magnetic resonance imaging (MRI) of the brain showed bilateral subcortical lesions on the parietal and occipital lobes and multiple micro-and macro-bleeds, consistent with PRES. At this point, RT-PCR for SARS-CoV-2 in a respiratory specimen and cerebrospinal fluid was negative. She was discharged home 35 days after admission on oral levetiracetam. Control MRI five months after discharge showed bilateral focal gliosis. On follow-up, she remains seizure-free on levetiracetam.Conclusions: PRES has been observed before as a neurological manifestation of acute COVID-19; to our knowledge, this is the first PRES case occurring in a hospitalized patient already recovered from COVID-19. A persistent proinflammatory/prothrombotic state triggered by SARS-CoV-2 infection may lead to long-standing endothelial dysfunction, resulting in delayed PRES in patients recovering from COVID-19. With a rapid and exponential increase in survivors of acute COVID-19, clinicians should be aware of delayed (post-acute) neurological damage, including PRES.

COVID-19-related diffuse posthypoxic leukoencephalopathy and microbleeds masquerades as acute necrotizing encephalopathy.

BACKGROUND: The complications of coronavirus disease 2019 (COVID-19), the clinical entity caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are not limited to the respiratory system. Leukoencephalopathy with microbleeds is increasingly seen in patients with COVID-19. New information is needed to delineate better the clinical implications of this infectious disease. CASE REPORT: A 46-year-old man with confirmed SARS-CoV-2 infection was admitted to the intensive care unit (ICU) with severe COVID-19. After transfer to the general wards, the patient was noted drowsy, disorientated, with slow thinking and speech. A brain MRI showed bilateral symmetrical hyperintense lesions in the deep and subcortical whiter matter, involving the splenium of the corpus callosum, as well as multiple microhemorrhages implicating the splenium and subcortical white matter. No contrast-enhanced lesions were observed in brain CT or MRI. CSF analysis showed no abnormalities, including a negative rtRT-PCR for SARS-CoV-2. An outpatient follow-up visit showed near-complete clinical recovery and resolution of the hyperintense lesions on MRI, without microbleeds change. CONCLUSION: We present the case of a survivor of severe COVID-19 who presented diffuse posthypoxic leukoencephalopathy, and microbleeds masquerading as acute necrotizing encephalopathy. We postulate that this kind of cerebral vasogenic edema with microbleeds could be the consequence of hypoxia, inflammation, the prothrombotic state and medical interventions such as mechanical ventilation and anticoagulation.

From Head to Toe: Granulomatosis with Polyangiitis.

Granulomatosis with polyangiitis (GPA) is an antineutrophil cytoplasmic antibody-associated vasculitis. It is an uncommon multisystem disease involving predominantly small vessels and is characterized by granulomatous inflammation, pauci-immune necrotizing glomerulonephritis, and vasculitis. GPA can involve virtually any organ. Clinical manifestations are heterogeneous and can be classified as granulomatous (eg, ear, nose, and throat disease; lung nodules or masses; retro-orbital tumors; pachymeningitis) or vasculitic (eg, glomerulonephritis, alveolar hemorrhage, mononeuritis multiplex, scleritis). The diagnosis of GPA relies on a combination of clinical findings, imaging study results, laboratory test results, serologic markers, and histopathologic results. Radiology has a crucial role in the diagnosis and follow-up of patients with GPA. CT and MRI are the primary imaging modalities used to evaluate GPA manifestations, allowing the differentiation of GPA from other diseases that could simulate GPA. The authors review the main clinical, histopathologic, and imaging features of GPA to address the differential diagnosis in the affected organs and provide a panoramic picture of the protean manifestations of this infrequent disease. The heterogeneous manifestations of GPA pose a significant challenge in the diagnosis of this rare condition. By recognizing the common and unusual imaging findings, radiologists play an important role in the diagnosis and follow-up of patients with GPA and aid clinicians in the differentiation of disease activity versus disease-induced damage, which ultimately affects therapeutic decisions. Online supplemental material is available for this article. ©RSNA, 2021.

Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.

OBJECTIVES: This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction. METHODS: Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck. RESULTS: Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy. CONCLUSIONS: Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone. KEY POINTS: • Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.

Styloid Process Osteoradionecrosis: Report of 3 Cases.

We report the imaging and clinical findings in 3 patients with osteoradionecrosis of the styloid process, an uncommon consequence of radiation therapy for head and neck malignancy.

MR Angiography of Extracranial Carotid Disease.

Magnetic resonance angiography sequences, such as time-of-flight and contrast-enhanced angiography, provide clear depiction of vessel lumen, traditionally used to evaluate carotid pathologic conditions such as stenosis, dissection, and occlusion; however, atherosclerotic plaques with a similar degree of stenosis may vary tremendously from a histopathological standpoint. MR vessel wall imaging is a promising noninvasive method to evaluate the content of the vessel wall at high spatial resolution. This is particularly interesting in the case of atherosclerosis as vessel wall imaging can identify higher risk, vulnerable plaques as well as has potential applications in the evaluation of other carotid pathologic conditions.

Advanced cross-sectional imaging of cerebral aneurysms.

While the rupture rate of cerebral aneurysms is only 1% per year, ruptured aneurysms are associated with significant morbidity and mortality, while aneurysm treatments have their own associated risk of morbidity and mortality. Conventional markers for aneurysm rupture include patient-specific and aneurysm-specific characteristics, with the development of scoring systems to better assess rupture risk. These scores, however, rely heavily on aneurysm size, and their accuracy in assessing risk in smaller aneurysms is limited. While the individual risk of rupture of small aneurysms is low, due to their sheer number, the largest proportion of ruptured aneurysms are small aneurysms. Conventional imaging techniques are valuable in characterizing aneurysm morphology; however, advanced imaging techniques assessing the presence of inflammatory changes within the aneurysm wall, hemodynamic characteristics of blood flow within aneurysm sacs, and imaging visualization of irregular aneurysm wall motion have been used to further determine aneurysm instability that otherwise cannot be characterized by conventional imaging techniques. The current manuscript reviews conventional imaging techniques and their value and limitations in cerebral aneurysm characterization, and evaluates the applications, value and limitations of advanced aneurysm imaging and post-processing techniques including intracranial vessel wall MRA, 4D-flow, 4D-CTA, and computational fluid dynamic simulations.

Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models.

Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (p-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.

Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy.

PURPOSE: To determine whether machine learning assisted-texture analysis of multi-energy virtual monochromatic image (VMI) datasets from dual-energy CT (DECT) can be used to differentiate metastatic head and neck squamous cell carcinoma (HNSCC) lymph nodes from lymphoma, inflammatory, or normal lymph nodes. MATERIALS AND METHODS: A retrospective evaluation of 412 cervical nodes from 5 different patient groups (50 patients in total) having undergone DECT of the neck between 2013 and 2015 was performed: (1) HNSCC with pathology proven metastatic adenopathy, (2) HNSCC with pathology proven benign nodes (controls for (1)), (3) lymphoma, (4) inflammatory, and (5) normal nodes (controls for (3) and (4)). Texture analysis was performed with TexRAD® software using two independent sets of contours to assess the impact of inter-rater variation. Two machine learning algorithms (Random Forests (RF) and Gradient Boosting Machine (GBM)) were used with independent training and testing sets and determination of accuracy, sensitivity, specificity, PPV, NPV, and AUC. RESULTS: In the independent testing (prediction) sets, the accuracy for distinguishing different groups of pathologic nodes or normal nodes ranged between 80 and 95%. The models generated using texture data extracted from the independent contour sets had substantial to almost perfect agreement. The accuracy, sensitivity, specificity, PPV, and NPV for correctly classifying a lymph node as malignant (i.e. metastatic HNSCC or lymphoma) versus benign were 92%, 91%, 93%, 95%, 87%, respectively. CONCLUSION: Machine learning assisted-DECT texture analysis can help distinguish different nodal pathology and normal nodes with a high accuracy.