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Background: Few studies have investigated the feasibility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a free-breathing golden-angle radial stack-of-stars volume-interpolated breath-hold examination (FB radial VIBE) sequence in the lung. Purpose: To investigate whether DCE-MRI using the FB radial VIBE sequence can assess morphological and kinetic parameters in patients with pulmonary lesions, with computed tomography (CT) as the reference. Material and Methods: In total, 43 patients (30 men; mean age = 64 years) with one lesion each were prospectively enrolled. Morphological and kinetic features on MRI were calculated. The diagnostic performance of morphological MR features was evaluated using a receiver operating characteristic (ROC) curve. Kinetic features were compared among subgroups based on histopathological subtype, lesion size, and lymph node metastasis. Results: The maximum diameter was not significantly different between CT and MRI (3.66 ± 1.62 cm vs. 3.64 ± 1.72 cm; P = 0.663). Spiculation, lobulation, cavitation or bubble-like areas of low attenuation, and lymph node enlargement had an area under the ROC curve (AUC) >0.9, while pleural indentation yielded an AUC of 0.788. The lung cancer group had significantly lower Ktrans, Ve, and initial AUC values than the other cause inflammation group (0.203, 0.158, and 0.589 vs. 0.597, 0.385, and 1.626; P < 0.05) but significantly higher values than the tuberculosis group (P < 0.05). Conclusion: Morphology features derived from FB radial VIBE have high correlations with CT, and kinetic analyses show significant differences between benign and malignant lesions. DCE-MRI with FB radial VIBE could serve as a complementary quantification tool to CT for radiation-free assessments of lung lesions. [ABSTRACT FROM AUTHOR]

Background: Susceptibility-weighted imaging (SWI) and T1/T2 mapping can be used to detect reperfusion intramyocardial hemorrhage (IMH) in ST-segment elevation myocardial infarction (STEMI) patients. However, the sensitivity and accuracy of the SWI and T1/T2 mapping sequences were not systematically compared. The study aimed to evaluate image quality and diagnostic performance of SWI in patients with IMH, compared with T1/T2 mapping., Methods: A prospective study was conducted on consecutive acute STEMI patients who were recruited from January to July 2022. Within 2-6 days after reperfusion treatment, all patients underwent a 3T cardiac magnetic resonance (CMR) examination, including T2-weighted short-tau inversion recovery (T2W-STIR), T1/T2 mapping, and SWI. A total of 36 patients [age, 56.50±17.25 years; males, 83.33% (30/36)] were enrolled. The relative infarct-remote myocardium signal intensity ratio (SIinfarct-remote) and contrast-to-noise ratio (CNR) were calculated for each patient on T1/T2 mapping and SWI, and the difference between relative signal intensity-to-noise ratio (rSNR) in the IMH (rSNRIMH) was measured for IMH patients on T1/T2 mapping and SWI. SIinfarct-remote, CNR, and rSNRIMH were compared among the three sequences. Receiver operating characteristic (ROC) analyses were used to evaluate the diagnostic performance of three sequences by SIinfarct-remote and visual assessment., Results: A total of 26 (72.22%) patients had IMH. Quantitatively, the SIinfarct-remote of three sequences had excellent diagnostic performance for detecting IMH [SWI area under the curve (AUC) =1.000, 95% confidence interval (CI): 1.000-1.000 vs . T1 mapping AUC =0.954, 95% CI: 0.885-1.000 vs . T2 mapping AUC =0.985, 95% CI: 0.955-1.000; SWI vs . T1 mapping, P=0.300; SWI vs . T2 mapping, P=0.188; T1 mapping vs . T2 mapping, P=0.302). Qualitatively, three sequences had similar performance on detecting IMH (SWI AUC =0.895, 95% CI: 0.784-1.000; T1 mapping AUC =0.835, 95% CI: 0.711-0.958; and T2 mapping AUC =0.855, 95% CI: 0.735-0.974; SWI vs . T1 mapping, P=0.172; SWI vs . T2 mapping, P=0.317; T1 mapping vs . T2 mapping, P=0.710). The rSNRIMH was highest in T1 mapping, followed by T2 mapping and SWI, but SWI had the highest CNR., Conclusions: SWI, as well as T1/T2 mapping, is a feasible and accurate approach for clinical diagnosis of IMH with excellent performance., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-591/coif). X.T. and X.Z. are current employees of Bayer Healthcare and Siemens Healthineers, respectively. The other authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Background: Good feature reproducibility enhances model reliability. The manual segmentation of gastric cancer with liver metastasis (GCLM) can be time-consuming and unstable., Purpose: To assess the value of a semi-automatic segmentation tool in improving the reproducibility of the radiomic features of GCLM., Material and Methods: Patients who underwent dual-source computed tomography were retrospectively reviewed. As an intra-observer analysis, one radiologist segmented metastatic liver lesions manually and semi-automatically twice. Another radiologist re-segmented the lesions once as an inter-observer analysis. A total of 1691 features were extracted. Spearman rank correlation was used for feature reproducibility analysis. The times for manual and semi-automatic segmentation were recorded and analyzed., Results: Seventy-two patients with 168 lesions were included. Most of the GCLM radiomic features became more reliable with the tool than the manual method. For the intra-observer feature reproducibility analysis of manual and semi-automatic segmentation, the rates of features with good reliability were 45.5% and 62.3% ( P  < 0.02), respectively; for the inter-observer analysis, the rates were 29.3% and 46.0% ( P  < 0.05), respectively. For feature types, the semi-automatic method increased reliability in 6/7 types in the intra-observer analysis and 5/7 types in the inter-observer analysis. For image types, the reliability of the square and exponential types was significantly increased. The mean time of semi-automatic segmentation was significantly shorter than that of the manual method ( P  < 0.05)., Conclusion: The application of semi-automated software increased feature reliability in the intra- and inter-observer analyses. The semi-automatic process took less time than the manual process.