Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network.

Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016−01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48−99.38%) and 100.00% (95% CI 96.38−100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904−0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available.

Automated quantification of airway wall thickness on chest CT using retina U-Nets - Performance evaluation and application to a large cohort of chest CTs of COPD patients.

PURPOSE: Airway wall thickening is a consequence of chronic inflammatory processes and usually only qualitatively described in CT radiology reports. The purpose of this study is to automatically quantify airway wall thickness in multiple airway generations and assess the diagnostic potential of this parameter in a large cohort of patients with Chronic Obstructive Pulmonary Disease (COPD). MATERIALS AND METHODS: This retrospective, single-center study included a series of unenhanced chest CTs. Inclusion criteria were the mentioning of an explicit COPD GOLD stage in the written radiology report and time period (01/2019-12/2021). A control group included chest CTs with completely unremarkable lungs according to the report. The DICOM images of all cases (axial orientation; slice-thickness: 1 mm; soft-tissue kernel) were processed by an AI algorithm pipeline consisting of (A) a 3D-U-Net for det detection and tracing of the bronchial tree centerlines (B) extraction of image patches perpendicular to the centerlines of the bronchi, and (C) a 2D U-Net for segmentation of airway walls on those patches. The performance of centerline detection and wall segmentation was assessed. The imaging parameter average wall thickness was calculated for bronchus generations 3-8 (AWT3-8) across the lungs. Mean AWT3-8 was compared between five groups (control, COPD Gold I-IV) using non-parametric statistics. Furthermore, the established emphysema score %LAV-950 was calculated and used to classify scans (normal vs. COPD) alone and in combination with AWT3-8. RESULTS: A total of 575 chest CTs were processed. Algorithm performance was very good (airway centerline detection sensitivity: 86.9%; airway wall segmentation Dice score: 0.86). AWT3-8 was statistically significantly greater in COPD patients compared to controls (2.03 vs. 1.87 mm, p < 0.001) and increased with COPD stage. The classifier that combined %LAV-950 and AWT3-8 was superior to the classifier using only %LAV-950 (AUC = 0.92 vs. 0.79). CONCLUSION: Airway wall thickness increases in patients suffering from COPD and is automatically quantifiable. AWT3-8 could become a CT imaging parameter in COPD complementing the established emphysema biomarker %LAV-950. CLINICAL RELEVANCE STATEMENT: Quantitative measurements considering the complete visible bronchial tree instead of qualitative description could enhance radiology reports, allow for precise monitoring of disease progression and diagnosis of early stages of disease.

Diagnostic Accuracy of Imaging Findings in Pleural Empyema: Systematic Review and Meta-Analysis.

Computed tomography (CT) diagnosis of empyema is challenging because current literature features multiple overlapping pleural findings. We aimed to identify informative findings for structured reporting. The screening according to inclusion criteria (P: Pleural empyema, I: CT C: culture/gram-stain/pathology/pus, O: Diagnostic accuracy measures), data extraction, and risk of bias assessment of studies published between 01-1980 and 10-2021 on Pubmed, Embase, and Web of Science (WOS) were performed independently by two reviewers. CT findings with pooled diagnostic odds ratios (DOR) with 95% confidence intervals, not including 1, were considered as informative. Summary estimates of diagnostic accuracy for CT findings were calculated by using a bivariate random-effects model and heterogeneity sources were evaluated. Ten studies with a total of 252 patients with and 846 without empyema were included. From 119 overlapping descriptors, five informative CT findings were identified: Pleural enhancement, thickening, loculation, fat thickening, and fat stranding with an AUC of 0.80 (hierarchical summary receiver operating characteristic, HSROC). Potential sources of heterogeneity were different thresholds, empyema prevalence, and study year.

Antimicrobial use and misuse at the end of life: a retrospective analysis of a treatment escalation/limitation plan.

BACKGROUND: Antimicrobial treatment is common at end of life. A treatment escalation/limitation plan (TELP) offers the opportunity to avoid non-beneficial treatment in critically ill patients. Our aim was to evaluate antimicrobial prescribing in terminally ill patients, and assess whether it was modified using a TELP. METHODS: Appropriateness of antimicrobial treatment was audited using a priori criteria in 94 consecutive hospital deaths. Prescribing in patients whose death was expected/unexpected, and who had a TELP with/without a 'ceiling' for antimicrobials, were compared. RESULTS: Twenty three of 94 patients (24.5%) were receiving antimicrobials at time of death. This was not influenced by evidence of infection or whether death was expected. The use of a TELP (n = 81) with an antimicrobial 'ceiling' (28 with, 53 without) was associated with a significant reduction in antimicrobials administered (28.6% vs 81.1%; p < 0.0005). CONCLUSIONS: Many complex factors contribute to antimicrobial misuse at end of life. An appropriately constructed TELP reduces inappropriate prescribing.

Zika virus tropism and interactions in myelinating neural cell cultures: CNS cells and myelin are preferentially affected.

The recent global outbreak of Zika virus (ZIKV) infection has been linked to severe neurological disorders affecting the peripheral and central nervous systems (PNS and CNS, respectively). The pathobiology underlying these diverse clinical phenotypes are the subject of intense research; however, even the principal neural cell types vulnerable to productive Zika infection remain poorly characterised. Here we used CNS and PNS myelinating cultures from wild type and Ifnar1 knockout mice to examine neuronal and glial tropism and short-term consequences of direct infection with a Brazilian variant of ZIKV. Cell cultures were infected pre- or post-myelination for various intervals, then stained with cell-type and ZIKV-specific antibodies. In bypassing systemic immunity using ex vivo culture, and the type I interferon response in Ifnar1 deficient cells, we were able to evaluate the intrinsic infectivity of neural cells. Through systematic quantification of ZIKV infected cells in myelinating cultures, we found that ZIKV infection is enhanced in the absence of the type I interferon responses and that CNS cells are considerably more susceptible to infection than PNS cells. In particular, we demonstrate that CNS axons and myelinating oligodendrocytes are especially vulnerable to injury. These results have implications for understanding the pathobiology of neurological symptoms associated with ZIKV infection. Furthermore, we provide a quantifiable ex vivo infection model that can be used for fundamental and therapeutic studies on viral neuroinvasion and its consequences.