Selective Use of CT Fractional Flow at a Large Academic Medical Center: Insights from Clinical Implementation after 1 Year of Practice.

Purpose This special report outlines a retrospective observational study of CT fractional flow reserve (CT-FFR) analysis using dual-source coronary CT angiography (CTA) scans performed without heart rate control and its impact on clinical outcomes. Materials and Methods All patients who underwent clinically indicated coronary CTA between August 2020 and August 2021 were included in this retrospective observational study. Scans were performed in the late systolic to early diastolic period without heart rate control and analyzed at the interpreting physician's discretion. Demographics, coronary CTA features, and rates of invasive coronary angiography (ICA), percutaneous coronary intervention (PCI), myocardial infarction, and all-cause death at 3 months were assessed by chart review. Results During the study period, 3098 patients underwent coronary CTA, of whom 113 with coronary bypass grafting were excluded. Of the remaining 2985 patients, 292 (9.7%) were referred for CT-FFR analysis. Two studies (0.7%) were rejected from CT-FFR analysis, and six (2.1%) analyses did not evaluate the lesion of concern. A total of 160 patients (56.3%) had CT-FFR greater than 0.80. Among patients with significant stenosis at coronary CTA, patients who underwent CT-FFR analysis presented with lower rates of ICA (74.5% vs 25.5%, P = .04) and PCI (78.9% vs 21.1%, P = .05). Conclusion CT-FFR was implemented in patients not requiring heart rate control by using dual-source coronary CTA acquisition and showed the potential to decrease rates of ICA and PCI without compromising safety in patients with significant stenosis and an average heart rate of 65 beats per minute. Keywords: Angiography, CT, CT-Angiography, Fractional Flow Reserve, Cardiac, Heart, Arteriosclerosis Supplemental material is available for this article. © RSNA, 2024.

Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography.

PURPOSE: Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data. METHODS: We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers). RESULTS: Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively. CONCLUSION: Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available.[Media: see text].

Coronary Artery Disease Reporting and Data System (CAD-RADS) Adoption: Analysis of Local Trends in a Large Academic Medical Center.

PURPOSE: To perform a retrospective review of Coronary Artery Disease Reporting and Data System (CAD-RADS) adoption at a high-volume cardiac CT service. MATERIALS AND METHODS: In this retrospective study, the adoption of CAD-RADS in 6562 coronary CT angiography (CTA) reports from January 1, 2017, to February 13, 2020, was evaluated. Reports without CAD-RADS were classified as opt-outs or exceptions to CAD-RADS. CAD-RADS classifications were retrospectively assigned to the opt-outs and the clinical indications for coronary CTA. RESULTS: CAD-RADS scores were reported in 95% (6264 of 6562) of cases. Among the 5% (n = 298) of reports not reported according to CAD-RADS, 58% (n = 172) were considered opt-outs and 42% (n = 126) were exceptions. Cases with higher degree of stenosis, stents, and coronary artery bypass grafts (CABGs) occurred more often in opt-outs versus reports with CAD-RADS (odds ratio [OR], 8.3 [95% CI: 1.6, 42.1]; P < .001). The quarterly opt-out rate decreased over consecutive quarters in the 1st year (OR, 0.77 [95% CI: 0.61, 0.96]; P = .01), then stabilized. Quarterly opt-out rate for patients with stents decreased over time (OR, 0.82 [95% CI: 0.73, 0.92]; P = .008), as did the opt-out rates in patients with CABG (OR, 0.83 [95% CI: 0.76, 0.91]; P < .001). Exceptions (n = 126) included coronary dissections (44%), anomalous coronary arteries (41%), coronary artery aneurysms or pseudoaneurysms (10%), vasculitis (2%), stent complications (2%), and extrinsic compression of grafts (2%). CONCLUSION: CAD-RADS was adopted rapidly and widely. Readers opted out of its use most often in complex cases of CAD, and the most common exceptions were coronary dissections and anomalous coronary artery.Keywords: Coronary Arteries, CT Angiography© RSNA, 2021.

Long-Term Training with a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients.

Brain-machine interfaces (BMIs) provide a new assistive strategy aimed at restoring mobility in severely paralyzed patients. Yet, no study in animals or in human subjects has indicated that long-term BMI training could induce any type of clinical recovery. Eight chronic (3-13 years) spinal cord injury (SCI) paraplegics were subjected to long-term training (12 months) with a multi-stage BMI-based gait neurorehabilitation paradigm aimed at restoring locomotion. This paradigm combined intense immersive virtual reality training, enriched visual-tactile feedback, and walking with two EEG-controlled robotic actuators, including a custom-designed lower limb exoskeleton capable of delivering tactile feedback to subjects. Following 12 months of training with this paradigm, all eight patients experienced neurological improvements in somatic sensation (pain localization, fine/crude touch, and proprioceptive sensing) in multiple dermatomes. Patients also regained voluntary motor control in key muscles below the SCI level, as measured by EMGs, resulting in marked improvement in their walking index. As a result, 50% of these patients were upgraded to an incomplete paraplegia classification. Neurological recovery was paralleled by the reemergence of lower limb motor imagery at cortical level. We hypothesize that this unprecedented neurological recovery results from both cortical and spinal cord plasticity triggered by long-term BMI usage.