Comparison of short-term outcomes between patients with extracranial carotid and/or intracranial atherosclerotic disease

OBJECTIVE

To directly compare the 90-day outcomes of patients with symptomatic intracranial atherosclerotic disease (ICAD), extracranial carotid atherosclerotic disease (ECAD), and ICAD with concomitant ECAD.

METHODS

From 2017-2021, patients who had (1) a transient ischemic attack or ischemic stroke within 30 days of admission as evaluated by a stroke neurologist and (2) ipsilateral ICAD and/or ECAD were prospectively enrolled. The cohort was divided into three groups: ICAD, ECAD, and ICAD with concomitant ECAD. The primary outcome assessed was 90-day ischemic stroke recurrence. Secondary outcomes included 90-day myocardial infarction (MI), all-cause mortality, and major adverse cardiovascular events (MACE, including cardiovascular death, nonfatal MI, and/or nonfatal ischemic stroke).

RESULTS

Of 371 patients included in the analysis, 240 (64.7%) patients had ICAD only, 93 (25.0%) patients had ECAD only, and 38 (10.3%) patients had ICAD with concomitant ECAD. On multivariate time-to-event analysis adjusting for potential confounders and with ICAD as the reference comparator, the risk of 90-day clinical outcomes was highest among patients with ICAD and concomitant ECAD, with adjusted hazard ratios of 4.54 (95% CI=1.45, 14.2; p = 0.006), 9.32 (95% CI=1.58, 54.8; p = 0.014), and 8.52 (95% CI=3.54, 20.5; p < 0.001) for 90-day ischemic stroke, MI, and MACE, respectively.

CONCLUSIONS

Patients with ICAD and concomitant ECAD have a poorer prognosis and are at significantly higher risk for 90-day ischemic stroke, MI, and MACE. Further research should focus on the evaluation of coronary atherosclerotic disease and more intensive medical therapy in this population.

Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT

Introduction

Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment.

Methods

Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated.

Results

Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001).

Conclusion

Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis.

Comparison of visibility of ulnar sided triangular fibrocartilage complex (TFCC) ligaments between isotropic three-dimensional and two-dimensional high-resolution FSE MR images

OBJECTIVES

Assessment of the ulnar attachment of the triangular fibrocartilage complex (TFCC) in a neutral forearm position remains challenging. Our study aims to evaluate the visibility of ulnar sided TFCC on 3 T MRI and compare isotropic 3D FSE sequences utilizing multiplanar reformation (MPR) with standard high-resolution 2D FSE sequences.

METHODS

Ninety-nine MRI wrist studies in patients with wrist pain were retrospectively analyzed. Patients were scanned with a neutral forearm position and reviewed with isotropic 3D coronal FSE proton density-weighted images (PDWI) and 2D coronal FSE PDWI. MPR was used for 3D assessment. Visibility of the dorsal radioulnar ligament (DRUL), triangular ligament (TL), and volar radioulnar ligament (VRUL) was assessed by three raters utilizing a five-point grading scale. Grades were compared between 2D and 3D sequences. Intrarater and interrater reliability for the delineation of anatomic structures was measured by Spearman's rank correlation coefficient, Cohen's kappa, and percentage of exact agreement/agreement within a range of ±1 score point.

RESULTS

Visibility grades in 3D were statistically significantly higher than those in 2D in all ligaments by all raters (p < 0.01). In Spearman's rank correlation coefficient and Cohen's kappa analysis, interrater correlations and agreements are variable but tended to be higher on 3D than on 2D. Both 2D and 3D sequences showed high intrarater exact agreement in all ligaments (80-91 % on 2D and 88-95 % on 3D). All exact interrater agreements on 3D were acceptable for TL (83-93 %) and acceptable to close to acceptable for VRUL (72-96 %).

CONCLUSION

The utilization of isotopic 3D imaging combined with MPR function significantly improves visibility of ulnar attachment of the TFCC.

Clival chordoma with drop metastases

Chordoma is a rare midline malignant tumor arising from embryonic remnants of the primitive notochord. The base of the skull is the second most common site of disease after the sacrococcygeal region. Intracranial chordoma constitutes about 30-35% of chordoma cases. Metastasis from chordoma is uncommon but if occurs, it tends to spread to the lungs. Cerebrospinal fluid seeding or drop metastasis is very rare. Here we describe a case of a clival chordoma with drop metastases.