Evidence suggests that normal pressure hydrocephalus (NPH) is underdiagnosed in day to day radiologic practice, and differentiating NPH from cerebral atrophy due to other neurodegenerative diseases and normal aging remains a challenge. To better characterize NPH, we test the hypothesis that a prediction model based on automated MRI brain tissue segmentation can help differentiate shunt-responsive NPH patients from cerebral atrophy due to Alzheimer disease (AD) and normal aging. Brain segmentation into gray and white matter (GM, WM), and intracranial cerebrospinal fluid was derived from pre-shunt T1-weighted MRI of 15 shunt-responsive NPH patients (9 men, 72.6 ± 8.0 years-old), 17 AD patients (10 men, 72.1 ± 11.0 years-old) chosen as a representative of cerebral atrophy in this age group; and 18 matched healthy elderly controls (HC, 7 men, 69.7 ± 7.0 years old). A multinomial prediction model was generated based on brain tissue volume distributions. GM decrease of 33% relative to HC characterized AD (P < 0.005). High preoperative ventricular and near normal GM volumes characterized NPH. A multinomial regression model based on gender, GM and ventricular volume had 96.3% accuracy differentiating NPH from AD and HC. In conclusion, automated MRI brain tissue segmentation differentiates shunt-responsive NPH with high accuracy from atrophy due to AD and normal aging. This method may improve diagnosis of NPH and improve our ability to distinguish normal from pathologic aging.
The aim of this study was to assess for an association between radiologists' turnaround time (TAT) and report quality for emergency department (ED) abdominopelvic CT examinations. Reports of 60 consecutive ED abdominopelvic CT studies from five abdominal radiologists (300 total reports) were included. An ED radiologist, abdominal radiologist, and ED physician independently evaluated satisfaction with report content (1-10 scale), satisfaction with report clarity (1-10 scale), and extent to which the report advanced the patient on a previously published clinical spectrum scale (1-5 scale). TAT (time between completion of imaging and completion of the final report) and report quality were compared between radiologists using unpaired t tests; associations between TAT and report quality scores for individual radiologists were assessed using Pearson's correlation coefficients. The five radiologists' mean TAT varied from 35 to 53 min. There were significant differences in report content in half of comparisons between radiologists by observer 1 (p ≤ 0.032) and in a minority of comparisons by observer 2 (p ≤ 0.047), in report clarity in majority of comparisons by observer 1 (p ≤ 0.031) and in a minority of comparisons by observer 2 (p ≤ 0.010), and in impact on patient care in a minority of comparisons for all observers (p ≤ 0.047). There were weak positive correlations between TAT and report content and clarity for three radiologists for observer 1 (r = 0.270-0.362) and no correlation between TAT and any report quality measure for remaining combinations of the five radiologists and three observers (r = -0.197 to +0.181). While both TAT and report quality vary between radiologists, these two factors were not associated for individual radiologists.
Purpose: The aim of this study was to evaluate radiology imaging volumes at distinct time periods throughout the coronavirus disease 2019 (COVID-19) pandemic as a function of regional COVID-19 hospitalizations.Methods: Radiology imaging volumes and statewide COVID-19 hospitalizations were collected, and four 28-day time periods throughout the COVID-19 pandemic of 2020 were analyzed: pre-COVID-19 in January, the "first wave" of COVID-19 hospitalizations in April, the "recovery" time period in the summer of 2020 with a relative nadir of COVID-19 hospitalizations, and the "third wave" of COVID-19 hospitalizations in November. Imaging studies were categorized as inpatient, outpatient, or emergency department on the basis of patient location at the time of acquisition. A Mann-Whitney U test was performed to compare daily imaging volumes during each discrete 28-day time period.Results: Imaging volumes overall during the first wave of COVID-19 infections were 55% (11,098/20,011; P < .001) of pre-COVID-19 imaging volumes. Overall imaging volumes returned during the recovery time period to 99% (19,915/20,011; P ¼ .725), and thirdwave imaging volumes compared with the pre-COVID-19 period were significantly lower in the emergency department at 88.8% (7,951/8,955; P < .001), significantly higher for outpatients at 115.7% (8,818/7,621; P ¼ .008), not significantly different for inpatients at 106% (3,650/3,435; P ¼ .053), and overall unchanged when aggregated together at 102% (20,419/20,011; P ¼ .629).Conclusions: Medical imaging rebounded after the first wave of COVID-19 hospitalizations, with relative stability of utilization over the ensuing phases of the pandemic. As widespread COVID-19 vaccination continues to occur, future surges in COVID-19 hospitalizations will likely have a negligible impact on imaging utilization.
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