Prognostic utility of the chest computed tomography severity score for the requirement of mechanical ventilation and mortality in hospitalized patients with COVID-19.

Purpose: To correlate the chest computed tomography severity score (CT-SS) with the need for mechanical ventilation and mortality in hospitalized patients with COVID-19. Materials and methods: The chest CT images of 224 inpatients with COVID-19, confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR), were retrospectively reviewed from April 1 to 25, 2020, in a tertiary health care center. We calculated the CT-SS (dividing each lung into 20 segments and assigning scores of 0, 1, and 2 due to opacification involving 0%, <50%, and ≥50% of each region for a global range of 0-40 points, including both lungs), and collected clinical data. The receiver operating characteristic curve and Youden Index analysis was performed to calculate the CT-SS threshold and accuracy for classification for risk of mortality or MV requirement. Results: 136 men and 88 women were recruited, with an age range of 23-91 years and a mean of 50.17 years; 79 met the MV criteria, and 53 were nonsurvivors. The optimal threshold was >27.5 points for mortality (area under ROC curve >0.96), with a sensitivity of 93% and specificity of 87%, and >25.5 points for the need for MV (area under ROC curve >0.94), with a sensitivity of 90% and specificity of 89%. The Kaplan-Meier curves show a significant difference in mortality by the CT-SS threshold (Log Rank p < 0.001). Conclusions: In our cohort of hospitalized patients with COVID-19, the CT-SS accurately discriminates the need for MV and mortality risk. In conjunction with clinical status and laboratory data, the CT-SS may be a useful imaging tool that could be included in establishing a prognosis for this population.

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.

Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City

ABSTRACT 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.

Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City.

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.

Liver Transplant Complications Radiologist Can't Miss.

Liver transplantation is considered the ideal and definitive therapy for patients with end-stage liver disease. Since 1963 advances in liver transplant surgical techniques and immunosuppressive therapies have improved outcomes and patients' survival. However, early diagnosis of graft dysfunction, through different imaging modalities, is crucial for graft survival. Imaging plays a fundamental role before, during and after the transplantation process. In this review, we will discuss the importance of imaging in the diagnosis of vascular and biliary post-transplant complications through different imaging modalities such as Doppler ultrasonography, computed tomography (CT) and magnetic resonance imaging (MRI).