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.

Background: Artificial intelligence (AI) in radiology has improved diagnostic performance and shortened reading times of coronavirus disease 2019 (COVID-19) patients' studies. Objectives: The objectives pf the study were to analyze the performance of a chest computed tomography (CT) AI quantitative algorithm for determining the risk of mortality/mechanical ventilation (MV) in hospitalized COVID-19 patients and explore a prognostic multivariate model in a tertiary-care center in Mexico City. Methods: Chest CT images of 166 COVID-19 patients hospitalized from April 1 to 20, 2020, were retrospectively analyzed using AI algorithm software. Data were collected from their medical records. We analyzed the diagnostic yield of the relevant CT variables using the area under the ROC curve (area under the curve [AUC]). Optimal thresholds were obtained using the Youden index. We proposed a predictive logistic model for each outcome based on CT AI measures and predetermined laboratory and clinical characteristics. Results: The highest diagnostic yield of the assessed CT variables for mortality was the percentage of total opacity (threshold >51%; AUC = 0.88, sensitivity = 74%, and specificity = 91%). The AUC of the CT severity score (threshold > 12.5) was 0.88 for MV (sensitivity = 65% and specificity = 92%). The proposed prognostic models include the percentage of opacity and lactate dehydrogenase level for mortality and troponin I and CT severity score for MV requirement. Conclusion: The AI-calculated CT severity score and total opacity percentage showed good diagnostic accuracy for mortality and met MV criteria. The proposed prognostic models using biochemical variables and imaging data measured by AI on chest CT showed good risk classification in our population of hospitalized COVID-19 patients. (REV INVEST CLIN. [AHEAD OF PRINT])

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