Comparison of lung lesion assessment using free-breathing dynamic contrast-enhanced 1.5-T MRI with a golden-angle radial stack-of-stars VIBE sequence and CT.

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.

Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software.

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.