RESUMO
Tick-borne encephalitis (TBE) virus (TBEV) is transmitted to humans via tick bites. Infection is benign in >90% of the cases but can cause mild (<5%), moderate (<4%), or severe (<1%) encephalitis. We show here that â¼10% of patients hospitalized for severe TBE in cohorts from Austria, Czech Republic, and France carry auto-Abs neutralizing IFN-α2, -ß, and/or -ω at the onset of disease, contrasting with only â¼1% of patients with moderate and mild TBE. These auto-Abs were found in two of eight patients who died and none of 13 with silent infection. The odds ratios (OR) for severe TBE in individuals with these auto-Abs relative to those without them in the general population were 4.9 (95% CI: 1.5-15.9, P < 0.0001) for the neutralization of only 100 pg/ml IFN-α2 and/or -ω, and 20.8 (95% CI: 4.5-97.4, P < 0.0001) for the neutralization of 10 ng/ml IFN-α2 and -ω. Auto-Abs neutralizing type I IFNs accounted for â¼10% of severe TBE cases in these three European cohorts.
Assuntos
Anticorpos Neutralizantes , Autoanticorpos , Encefalite Transmitida por Carrapatos , Interferon Tipo I , Humanos , Encefalite Transmitida por Carrapatos/imunologia , Interferon Tipo I/imunologia , Autoanticorpos/imunologia , Feminino , Masculino , Anticorpos Neutralizantes/imunologia , Pessoa de Meia-Idade , Adulto , Vírus da Encefalite Transmitidos por Carrapatos/imunologia , Idoso , Áustria/epidemiologia , República TchecaRESUMO
RATIONALE AND OBJECTIVES: Finding comparison to relevant prior studies is a requisite component of the radiology workflow. The purpose of this study was to evaluate the impact of a deep learning tool simplifying this time-consuming task by automatically identifying and displaying the finding in relevant prior studies. MATERIALS AND METHODS: The algorithm pipeline used in this retrospective study, TimeLens (TL), is based on natural language processing and descriptor-based image-matching algorithms. The dataset used for testing comprised 3872 series of 246 radiology examinations from 75 patients (189 CTs, 95 MRIs). To ensure a comprehensive testing, five finding types frequently encountered in radiology practice were included: aortic aneurysm, intracranial aneurysm, kidney lesion, meningioma, and pulmonary nodule. After a standardized training session, nine radiologists from three university hospitals performed two reading sessions on a cloud-based evaluation platform resembling a standard RIS/PACS. The task was to measure the diameter of the finding-of-interest on two or more exams (a most recent and at least one prior exam): first without use of TL, and a second session at an interval of at least 21 days with the use of TL. All user actions were logged for each round, including time needed to measure the finding at all timepoints, number of mouse clicks, and mouse distance traveled. The effect of TL was evaluated in total, per finding type, per reader, per experience (resident vs. board-certified radiologist), and per modality. Mouse movement patterns were analyzed with heatmaps. To assess the effect of habituation to the cases, a third round of readings was performed without TL. RESULTS: Across scenarios, TL reduced the average time needed to assess a finding at all timepoints by 40.1% (107 vs. 65 seconds; p < 0.001). Largest accelerations were demonstrated for assessment of pulmonary nodules (-47.0%; p < 0.001). Less mouse clicks (-17.2%) were needed for finding evaluation with TL, and mouse distance traveled was reduced by 38.0%. Time needed to assess the findings increased from round 2 to round 3 (+27.6%; p < 0.001). Readers were able to measure a given finding in 94.4% of cases on the series initially proposed by TL as most relevant series for comparison. The heatmaps showed consistently simplified mouse movement patterns with TL. CONCLUSION: A deep learning tool significantly reduced both the amount of user interactions with the radiology image viewer and the time needed to assess findings of interest on cross-sectional imaging with relevant prior exams.