Radiology
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Purpose To determine whether arterial spin-labeling (ASL) magnetic resonance (MR) imaging could be used to identify changes in cerebral blood flow (CBF), collateral blood flow, and anastomosis site patency after revascularization in patients with moyamoya disease. Materials and Methods This retrospective study was conducted in 145 patients with moyamoya disease who underwent middle cerebral artery (MCA)-superficial temporal artery anastomosis. Preoperative, early postoperative, and late postoperative ASL and digital subtraction angiography images were analyzed. ⋯ Results Significant increases in CBFMCA, nCBFMCA, and nCBFCbll were found after revascularization (preoperative and postoperative values of CBFMCA, 35.2 mL/100 g per minute ± 7.8 [mean ± standard deviation] and 51.5 mL/100 g per minute ± 12.0; nCBFMCA, 0.73 mL/100 g per minute ± 0.14 and 1.01 mL/100 g per minute ± 0.18; nCBFCbll, 0.74 mL/100 g per minute ± 0.12 and 1.12 mL/100 g per minute ± 0.16; all P < .001). Agreements for collateral grading and anastomosis patency between ASL MR imaging and digital subtraction angiography were moderate to good, with weighted κ values of 0.77 (95% confidence interval: 0.73, 0.81) and 0.57 (95% confidence interval: 0.37, 0.76), respectively. Conclusion ASL MR imaging can be used to identify perfusion changes in patients with moyamoya disease after revascularization as a noninvasive monitoring tool.
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Randomized Controlled Trial Comparative Study
Digital Mammography versus Digital Mammography Plus Tomosynthesis for Breast Cancer Screening: The Reggio Emilia Tomosynthesis Randomized Trial.
Purpose To compare digital mammography (DM) plus digital breast tomosynthesis (DBT) versus DM alone for breast cancer screening in the Reggio Emilia Tomosynthesis trial, a two-arm test-and-treat randomized controlled trial. Materials and Methods For this trial, eligible women (45-70 years old) who previously participated in the Reggio Emilia screening program were invited for mammography. Consenting women were randomly assigned 1:1 to undergo DBT+DM or DM (both of which involved two projections and double reading). ⋯ PPV of the recall was 13.0% and 24.1%, respectively (P = .0002); 72 of 80 cancers found in the DBT+DM arm and with complete DBT imaging were positive at least at one DBT-alone reading. The greater detection rate for DM+DBT was stronger for ductal carcinoma in situ (+180%, 95% CI: 1, 665); it was notable for small and medium invasive cancers, but not for large ones (+94 [95% CI: 6, 254]; +122 [95% CI: 18, 316]; -12 [95% CI: -68, 141]; for invasive cancers < 10 mm, 10-19 mm, and ≥ 20 mm, respectively). Conclusion DBT+DM depicts 90% more cancers in a population previously screened with DM, with similar recall rates.
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Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.
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Purpose To determine if interstitial features at chest CT enhance the effect of emphysema on clinical disease severity in smokers without clinical pulmonary fibrosis. Materials and Methods In this retrospective cohort study, an objective CT analysis tool was used to measure interstitial features (reticular changes, honeycombing, centrilobular nodules, linear scar, nodular changes, subpleural lines, and ground-glass opacities) and emphysema in 8266 participants in a study of chronic obstructive pulmonary disease (COPD) called COPDGene (recruited between October 2006 and January 2011). Additive differences in patients with emphysema with interstitial features and in those without interstitial features were analyzed by using t tests, multivariable linear regression, and Kaplan-Meier analysis. ⋯ In addition, interstitial features modified the effect of emphysema on percentage predicted DLCO, RVLV volume ratio, 6WMD, SGRQ score, and mortality (P for interaction < .05 for all). Conclusion In smokers, the combined presence of interstitial features and emphysema was associated with worse clinical disease severity and higher mortality than was emphysema alone. In addition, interstitial features enhanced the deleterious effects of emphysema on clinical disease severity and mortality.