Power Doppler imaging to evaluate flow-limiting stenoses

Establishing a carotid artery stenosis disease cohort for comparative effectiveness research using natural language processing

OBJECTIVE

Investigation of asymptomatic carotid stenosis treatment is hindered by the lack of a contemporary population-based disease cohort. We describe the use of natural language processing (NLP) to identify stenosis in patients undergoing carotid imaging.

METHODS

Adult patients with carotid imaging between 2008 and 2012 in a large integrated health care system were identified and followed through 2017. An NLP process was developed to characterize carotid stenosis according to the Society of Radiologists in Ultrasound (for ultrasounds) and North American Symptomatic Carotid Endarterectomy Trial (NASCET) (for axial imaging) guidelines. The resulting algorithm assessed text descriptors to categorize normal/non-hemodynamically significant stenosis, moderate or severe stenosis as well as occlusion in both carotid ultrasound (US) and axial imaging (computed tomography and magnetic resonance angiography [CTA/MRA]). For US reports, internal carotid artery systolic and diastolic velocities and velocity ratios were assessed and matched for laterality to supplement accuracy. To validate the NLP algorithm, positive predictive value (PPV or precision) and sensitivity (recall) were calculated from simple random samples from the population of all imaging studies. Lastly, all non-normal studies were manually reviewed for confirmation for prevalence estimates and disease cohort assembly.

RESULTS

A total of 95,896 qualifying index studies (76,276 US and 19,620 CTA/MRA) were identified among 94,822 patients including 1059 patients who underwent multiple studies on the same day. For studies of normal/non-hemodynamically significant stenosis arteries, the NLP algorithm showed excellent performance with a PPV of 99% for US and 96.5% for CTA/MRA. PPV/sensitivity to identify a non-normal artery with correct laterality in the CTA/MRA and US samples were 76.9% (95% confidence interval [CI], 74.1%-79.5%)/93.1% (95% CI, 91.1%-94.8%) and 74.7% (95% CI, 69.3%-79.5%)/94% (95% CI, 90.2%-96.7%), respectively. Regarding cohort assembly, 15,522 patients were identified with diseased carotid artery, including 2674 exhibiting equal bilateral disease. This resulted in a laterality-specific cohort with 12,828 moderate, 5283 severe, and 1895 occluded arteries and 326 diseased arteries with unknown stenosis. During follow-up, 30.1% of these patients underwent 61,107 additional studies.

CONCLUSIONS

Use of NLP to detect carotid stenosis or occlusion can result in accurate exclusion of normal/non-hemodynamically significant stenosis disease states with more moderate precision with lesion identification, which can substantially reduce the need for manual review. The resulting cohort allows for efficient research and holds promise for similar reporting in other vascular diseases.

Signal losses with real-time three-dimensional power Doppler imaging

Power Doppler imaging (PDI) has been shown to be influenced by the wall filter when assessing arterial stenoses. Real-time 3-D Doppler imaging may likely become a widespread practice in the near future, but how the wall filter could affect PDI during the cardiac cycle has not been investigated. The objective of the study was to demonstrate that the wall filter may produce unexpected major signal losses in real-time 3-D PDI. To test our hypothesis, we first validated binary images obtained from analytical simulations with in vitro PDI acquisitions performed in a tube under pulsatile flow conditions. We then simulated PDI images in the presence of a severe stenosis, considering physiological conditions by finite element modeling. Power Doppler imaging simulations revealed important signal losses within the lumen area at different instants of the flow cycle, and there was a very good concordance between measured and predicted PDI binary images in the tube. Our results show that the wall filter may induce severe PDI signal losses that could negatively influence the assessment of vascular stenosis. Clinicians should therefore be aware of this cause of signal loss to properly interpret power Doppler angiographic images.

Three-Dimensional Assessment of Extracranial Doppler Sonography in Carotid Artery Stenosis Compared With Digital Subtraction Angiography

Background and Purpose— Difficulties in data presentation, data storage, and a high interobserver variability may influence color-coded Duplex sonography assessment of internal carotid artery stenosis (ICAS). The aim of our study was to evaluate the between-method agreement of ICAS using 3D color Doppler sonography (CDS) compared with digital subtraction angiography (DSA).

Methods— Forty-nine patients with 64 ICASs (age 64±9 years) were involved. The patients were investigated with a color-coded duplex system using the power mode. The 3D system consists of an electromagnet that induces a low-intensity magnetic field near the patient’s head. A magnetic position sensor is attached to the probe and transmits the spatial orientation to a personal computer.

Results— A total of 62 ICASs were reconstructed successfully with 3D CDS in 47 of 49 patients. High agreement for 2 independent observers was found in 3D CDS (weighted κ coefficient of 0.88). Three-dimensional CDS slightly underestimated the mean stenotic degree (mean 3D CDS 68.47±10.5 versus DSA 71.3±10.0). The intermethod agreement comparing DSA with 3D CDS was analyzed with the Bland and Altman test, which showed good agreement. Mean sensitivity of 3D CDS was 93%, mean specificity 82.5%, mean positive predictive value 82%, and mean negative predictive value 98%.

Conclusions— The 3D CDS findings demonstrated good agreement compared with the gold standard, DSA, yielding higher accuracy than CDS alone. Compared with angiography or magnetic resonance angiography, 3D CDS can be performed easily on critically ill patients in stroke or intensive care units and may therefore provide a useful tool for patients unable to undergo more invasive imaging techniques.