Focused view CT angiography for selective visualization of stroke related arteries: technical feasibility.

OBJECTIVES: This study investigated the technical feasibility of focused view CTA for the selective visualization of stroke related arteries. METHODS: A total of 141 CTA examinations for acute ischemic stroke evaluation were divided into a set of 100 cases to train a deep learning algorithm (dubbed "focused view CTA") that selectively extracts brain (including intracranial arteries) and extracranial arteries, and a test set of 41 cases. The visibility of anatomic structures at focused view and unmodified CTA was assessed using the following scoring system: 5 = completely visible, diagnostically sufficient; 4 = nearly completely visible, diagnostically sufficient; 3 = incompletely visible, barely diagnostically sufficient; 2 = hardly visible, diagnostically insufficient; 1 = not visible, diagnostically insufficient. RESULTS: At focused view CTA, median scores for the aortic arch, subclavian arteries, common carotid arteries, C1, C6, and C7 segments of the internal carotid arteries, V4 segment of the vertebral arteries, basilar artery, cerebellum including cerebellar arteries, cerebrum including cerebral arteries, and dural venous sinuses, were all 4. Median scores for the C2 to C5 segments of the internal carotid arteries, and V1 to V3 segments of the vertebral arteries ranged between 3 and 2. At unmodified CTA, median score for all above-mentioned anatomic structures was 5, which was significantly higher (p < 0.0001) than that at focused view CTA. CONCLUSION: Focused view CTA shows promise for the selective visualization of stroke-related arteries. Further improvements should focus on more accurately visualizing the smaller and tortuous internal carotid and vertebral artery segments close to bone. CLINICAL RELEVANCE: Focused view CTA may speed up image interpretation time for LVO detection and may potentially be used as a tool to study the clinical relevance of incidental findings in future prospective long-term follow-up studies. KEY POINTS: • A deep learning-based algorithm ("focused view CTA") was developed to selectively visualize relevant structures for acute ischemic stroke evaluation at CTA. • The elimination of unrequested anatomic background information was complete in all cases. • Focused view CTA may be used to study the clinical relevance of incidental findings.

The validation of low-dose CT scans from the [18F]-FDG PET-CT scan to assess skeletal muscle mass in comparison with diagnostic neck CT scans.

PURPOSE: Radiologically defined sarcopenia, or a low skeletal muscle index (SMI), is an emerging biomarker for adverse clinical outcomes in head and neck cancer (HNC) patients. Recently, SMI measurements have been validated at the level of the third cervical vertebra (C3) on diagnostic neck CT scans but are not yet validated on low-dose (LD) neck CT scans from the [18F]-FDG PET-CT. This hampers SMI analysis in HNC patients without a diagnostic neck CT but with a [18F]-FDG PET-CT scan. Therefore, the aim was to study whether (low) SMI based on LD CT scan from [18F]-FDG PET-CT is comparable to those derived from diagnostic neck CT scans. METHODS: HNC patients with both diagnostic CT and [18F]-FDG PET-CT of the neck were prospectively included into the OncoLifeS data-biobank. Skeletal muscle was retrospectively delineated at the level of the third cervical vertebra (C3), and (low) SMI (cm2/m2) was calculated for diagnostic and LD neck CTs. (Low) SMI from the diagnostic neck CT was considered the reference standard. Intra-class correlation coefficient (ICC), Bland-Altman plots, and Cohen's Kappa analysis were performed. RESULTS: The cohort (n = 233) mean age was 66.2 ± 12.8 years, and 74.2% of patients were male. Inter-rater reliability was excellent (ICC > 0.990, 95% confidence interval 0.975-0.996, p < 0.001). The agreement of SMI between both modalities was high according to the Bland-Altman plot (mean ΔSMI = - 0.19 cm2/m2), and there was no substantial bias. Cohen's Kappa analysis showed an almost perfect agreement of low SMI between the two modalities (κ = 0.911, p < 0.001). The position of arms didn't affect the high agreement of (low) SMI. CONCLUSION: Skeletal muscle mass, as measured with (low) SMI, remains constant irrespective of CT acquisition parameters (diagnostic neck CT scans versus LD neck scans of the [18F]-FDG PET-CT scan), positioning of arms, and observers. These findings contribute to the construction of a clinically useful radiological biomarker for SMI and therefore identify patients at risk for adverse clinical outcomes.

Skeletal muscle mass and sarcopenia can be determined with 1.5-T and 3-T neck MRI scans, in the event that no neck CT scan is performed.

OBJECTIVES: Cross-sectional area (CSA) measurements of the neck musculature at the level of third cervical vertebra (C3) on CT scans are used to diagnose radiological sarcopenia, which is related to multiple adverse outcomes in head and neck cancer (HNC) patients. Alternatively, these assessments are performed with neck MRI, which has not been validated so far. For that, the objective was to evaluate whether skeletal muscle mass and sarcopenia can be assessed on neck MRI scans. METHODS: HNC patients were included between November 2014 and November 2018 from a prospective data-biobank. CSAs of the neck musculature at the C3 level were measured on CT (n = 125) and MRI neck scans (n = 92 on 1.5-T, n = 33 on 3-T). Measurements were converted into skeletal muscle index (SMI), and sarcopenia was defined (SMI < 43.2 cm2/m2). Pearson correlation coefficients, Bland-Altman plots, McNemar test, Cohen's kappa coefficients, and interclass correlation coefficients (ICCs) were estimated. RESULTS: CT and MRI correlated highly on CSA and SMI (r = 0.958-0.998, p < 0.001). The Bland-Altman plots showed a nihil mean ΔSMI (- 0.13-0.44 cm2/m2). There was no significant difference between CT and MRI in diagnosing sarcopenia (McNemar, p = 0.5-1.0). Agreement on sarcopenia diagnosis was good with κ = 0.956-0.978 and κ = 0.870-0.933, for 1.5-T and 3-T respectively. Observer ICCs in MRI were excellent. In general, T2-weighted images had the best correlation and agreement with CT. CONCLUSIONS: Skeletal muscle mass and sarcopenia can interchangeably be assessed on CT and 1.5-T and 3-T MRI neck scans. This allows future clinical outcome assessment during treatment irrespective of used modality. KEY POINTS: • Screening for low amount of skeletal muscle mass is usually measured on neck CT scans and is highly clinical relevant as it is related to multiple adverse outcomes in head and neck cancer patients. • We found that skeletal muscle mass and sarcopenia determined on CT and 1.5-T and 3-T MRI neck scans at the C3 level can be used interchangeably. • When CT imaging of the neck is missing for skeletal muscle mass analysis, patients can be assessed with 1.5-T or 3-T neck MRIs.