5 research outputs found
Flat-panel Detector Perfusion Imaging and Conventional Multidetector Perfusion Imaging in Patients with Acute Ischemic Stroke : A Comparative Study.
PURPOSE
Flat-panel detector computed tomography (FDCT) is increasingly used in (neuro)interventional angiography suites. This study aimed to compare FDCT perfusion (FDCTP) with conventional multidetector computed tomography perfusion (MDCTP) in patients with acute ischemic stroke.
METHODS
In this study, 19 patients with large vessel occlusion in the anterior circulation who had undergone mechanical thrombectomy, baseline MDCTP and pre-interventional FDCTP were included. Hypoperfused tissue volumes were manually segmented on time to maximum (Tmax) and time to peak (TTP) maps based on the maximum visible extent. Absolute and relative thresholds were applied to the maximum visible extent on Tmax and relative cerebral blood flow (rCBF) maps to delineate penumbra volumes and volumes with a high likelihood of irreversible infarcted tissue ("core"). Standard comparative metrics were used to evaluate the performance of FDCTP.
RESULTS
Strong correlations and robust agreement were found between manually segmented volumes on MDCTP and FDCTP Tmax maps (r = 0.85, 95% CI 0.65-0.94, p < 0.001; ICC = 0.85, 95% CI 0.69-0.94) and TTP maps (r = 0.91, 95% CI 0.78-0.97, p < 0.001; ICC = 0.90, 95% CI 0.78-0.96); however, direct quantitative comparisons using thresholding showed lower correlations and weaker agreement (MDCTP versus FDCTP Tmax 6 s: r = 0.35, 95% CI -0.13-0.69, p = 0.15; ICC = 0.32, 95% CI 0.07-0.75). Normalization techniques improved results for Tmax maps (r = 0.78, 95% CI 0.50-0.91, p < 0.001; ICC = 0.77, 95% CI 0.55-0.91). Bland-Altman analyses indicated a slight systematic underestimation of FDCTP Tmax maximum visible extent volumes and slight overestimation of FDCTP TTP maximum visible extent volumes compared to MDCTP.
CONCLUSION
FDCTP and MDCTP provide qualitatively comparable volumetric results on Tmax and TTP maps; however, direct quantitative measurements of infarct core and hypoperfused tissue volumes showed lower correlations and agreement
Clinical features and outcomes of patients with myositis associated-interstitial lung disease
Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model
Introduction: Treatment of interstitial lung diseases (ILDs) other than
idiopathic pulmonary fibrosis (IPF) often includes systemic
corticosteroids. Use of steroid-sparing agents is amenable to avoid
potential side effects.
Methods: Functional indices and high-resolution computed tomography
(HRCT) patterns of patients with non-IPF ILDs receiving mycophenolate
mofetil (MMF) with a minimum follow-up of 1 year were analyzed. Two
independent radiologists and a machine learning software system (Imbio
1.4.2.) evaluated HRCT patterns.
Results: Fifty-five (n = 55) patients were included in the analysis
(male: 30 [55%], median age: 65.0 [95% CI: 59.7-70.0], mean forced
vital capacity %predicted [FVC %pred.] +/- standard deviation
[SD]: 69.4 +/- 18.3, mean diffusing capacity of lung for carbon
monoxide %pred. +/- SD: 40.8 +/- 14.3, hypersensitivity pneumonitis:
26, connective tissue disease-ILDs [CTD-ILDs]: 22, other ILDs: 7).
There was no significant difference in mean FVC %pred. post-6 months
(1.59 +/- 2.04) and 1 year (-0.39 +/- 2.49) of treatment compared to
baseline. Radiographic evaluation showed no significant difference
between baseline and post-1 year %ground glass opacities (20.0 [95%
CI: 14.4-30.0] vs. 20.0 [95% CI: 14.4-25.6]) and %reticulation (5.0
[95% CI: 2.0-15.6] vs. 7.5 [95% CI: 2.0-17.5]). A similar
performance between expert radiologists and Imbio software analysis was
observed in assessing ground glass opacities (intraclass correlation
coefficient [ICC] = 0.73) and reticulation (ICC = 0.88). Fourteen
patients (25.5%) reported at least one side effect and 8 patients
(14.5%) switched to antifibrotics due to disease progression.
Conclusion: Our data suggest that MMF is a safe and effective
steroid-sparing agent leading to disease stabilization in a proportion
of patients with non-IPF ILDs. Machine learning software systems may
exhibit similar performance to specialist radiologists and represent
fruitful diagnostic and prognostic tools