Contrast-Induced Encephalopathy: A Case Series Analysis.

BACKGROUND: Contrast-induced encephalopathy (CIE) is a rare adverse event linked to intravascular use of iodine-containing contrast media. The prevalence of CIE could increase in the future due to growing numbers of endovascular procedures. We provide insights from a case series of 7 patients. METHODS: Cases from 3 centers were collected based on existing academic collaborations, and key factors were extracted to illustrate development and management of CIE. RESULTS: In our retrospective case-series analysis of 7 cases from 3 countries, affected patients had an equal distribution of sex (4 women, 3 men) and a median age of 75 (IQR 63-77). Common risk factors included hypertension (5/7), hyperlipidemia (5/7), previous stroke (3/7), and type 2 diabetes (3/7). CIE developed in 3 cases after endovascular thrombectomy (EVT) for stroke, in 2 cases after aneurysm treatment, in 1 case after cardiac catheterization, and in 1 case after diagnostic computed tomography (CT) angiography without an endovascular procedure. The median procedure time was 48 min (IQR 40-81). All patients received non-ionic, low-osmolar contrast agents with volumes ranging from 100-300 ml. Symptom onset was close to contrast administration, with stroke-like neurological deficits being most common (4/7). Prednisolone was the most frequently used medication to treat the symptoms (4/7). Symptom resolution occurred in 4 out of 7 patients within two to several days, and 1 patient died, but without clear connection to CIE. CONCLUSION: CIE is a rare and possibly underrecognized condition, but fortunately, with a favorable outcome in most cases.

MRI-based Neuropathy Score Reporting And Data System (NS-RADS): multi-institutional wider-experience usability study of peripheral neuropathy conditions among 32 radiology readers.

OBJECTIVE: To determine the inter-reader reliability and diagnostic performance of classification and severity scales of Neuropathy Score Reporting And Data System (NS-RADS) among readers of differing experience levels after limited teaching of the scoring system. METHODS: This is a multi-institutional, cross-sectional, retrospective study of MRI cases of proven peripheral neuropathy (PN) conditions. Thirty-two radiology readers with varying experience levels were recruited from different institutions. Each reader attended and received a structured presentation that described the NS-RADS classification system containing examples and reviewed published articles on this subject. The readers were then asked to perform NS-RADS scoring with recording of category, subcategory, and most likely diagnosis. Inter-reader agreements were evaluated by Conger's kappa and diagnostic accuracy was calculated for each reader as percent correct diagnosis. A linear mixed model was used to estimate and compare accuracy between trainees and attendings. RESULTS: Across all readers, agreement was good for NS-RADS category and moderate for subcategory. Inter-reader agreement of trainees was comparable to attendings (0.65 vs 0.65). Reader accuracy for attendings was 75% (95% CI 73%, 77%), slightly higher than for trainees (71% (69%, 72%), p = 0.0006) for nerves and comparable for muscles (attendings, 87.5% (95% CI 86.1-88.8%) and trainees, 86.6% (95% CI 85.2-87.9%), p = 0.4). NS-RADS accuracy was also higher than average accuracy for the most plausible diagnosis for attending radiologists at 67% (95% CI 63%, 71%) and for trainees at 65% (95% CI 60%, 69%) (p = 0.036). CONCLUSION: Non-expert radiologists interpreted PN conditions with good accuracy and moderate-to-good inter-reader reliability using the NS-RADS scoring system. CLINICAL RELEVANCE STATEMENT: The Neuropathy Score Reporting And Data System (NS-RADS) is an accurate and reliable MRI-based image scoring system for practical use for the diagnosis and grading of severity of peripheral neuromuscular disorders by both experienced and general radiologists. KEY POINTS: • The Neuropathy Score Reporting And Data System (NS-RADS) can be used effectively by non-expert radiologists to categorize peripheral neuropathy. • Across 32 different experience-level readers, the agreement was good for NS-RADS category and moderate for NS-RADS subcategory. • NS-RADS accuracy was higher than the average accuracy for the most plausible diagnosis for both attending radiologists and trainees (at 75%, 71% and 65%, 65%, respectively).

[Artificial intelligence-based algorithms : Decision-making support for computed tomography of the chest]. / Algorithmen mit künstlicher Intelligenz : Entscheidungsunterstützung für Computertomographien des Thorax.

Artificial intelligence (AI) algorithms are increasingly used in radiology. The main areas of application are, for example, the detection of lung lesions and the diagnosis of chronic obstructive and interstitial lung diseases. The aim of our study was to train and evaluate a package of algorithms that analyze data from computed tomographic (CT) images of the chest and provide quantitative measurements to the radiologist. The following algorithms were trained: lung lesion detection and measurement, lung lobe segmentation, vessel segmentation and measurement, coronary calcium scoring, measurement and density analysis of vertebral bodies. AI-supported algorithms will become part of daily routine of the radiologist in the future. Tasks that do not require medical expertise can be performed by AI. However, our results show that, based on the current accuracy, verification by an experienced radiologist is necessary.