Journal of Magnetic Resonance Imaging最新文献

Background: Excessive daytime sleepiness (EDS) substantially impairs health-related quality of life and cognitive function in obstructive sleep apnea (OSA) patients. However, the underlying neuropathological mechanisms remain poorly understood.

Purpose: To investigate the associations between EDS, cognitive deficits, and brain alterations in OSA patients.

Study type: Prospective.

Population: One hundred two OSA patients (16 female, median age (IQR) 33.00 [28.00-41.00]) and 86 healthy controls (HCs) (16 female, median age (IQR) 33.00 [25.00-50.25]) were prospectively recruited.

Field strength/sequence: 3 T, 3D spoiled gradient-echo, diffusion-weighted spin-echo, blood oxygen level-dependent gradient-echo planar.

Assessment: The Epworth Sleepiness Scale, multiparameter MRI, and a series of neuropsychological tests (the number connection tests A and B, digit symbol test, line tracing test, serial dotting test, and Stroop Color/Word test) were performed to evaluate EDS, brain alterations, and cognitive performances.

Statistical tests: Mann-Whitney U test, independent samples t-test, chi-square test, Spearman's correlation, and mediation analyses were used. Significance level: p < 0.05.

Result: OSA patients exhibited statistically poorer cognitive performance (effect size = 0.23-0.37), lower diffusion tensor imaging along the perivascular space (DTI-ALPS) index (effect size = 0.25), and abnormal network topology (effect size = -0.80 to 0.57) compared with HCs. EDS significantly correlated with poorer cognitive performance (r = -0.173 to 0.258), lower DTI-ALPS index (r = -0.199), and abnormal network topology (r = -0.364 to 0.279) in all participants. Furthermore, aberrant degree centrality and nodal efficiency in the bilateral middle frontal gyrus (MFG) and left inferior parietal lobule (IPL) may mediate the relationship between EDS and cognitive deficits (indirect effect = -0.13 to 0.36).

Data conclusion: These results demonstrate that cognition, DTI-ALPS index, and network topology were all compromised in OSA. Furthermore, abnormal network topology in the bilateral MFG and left IPL may mediate the relationship between EDS and cognitive deficits.

Evidence level: Level 2.

Technical efficacy: Stage 2.

Background: Cerebral small vessel disease (CSVD) is an important risk factor for stroke and cognitive impairment, adversely affecting long-term outcomes. Methylglyoxal (MGO), a by-product of glycolysis, is implicated in various vascular complications.

Purpose: To explore the correlation between MGO and CSVD, particularly with white matter hyperintensities (WMH).

Study type: Prospective.

Population: One hundred and fifteen patients with first-ever small artery occlusion (SAO) within 72 h of onset were enrolled, with a mean age of 63.83 ± 10.92 years, of whom 75 (65.2%) were male.

Field strength/sequence: 1.5T or 3T; fast spin-echo for T1- and T2-weighted imaging, fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), and susceptibility-weighted imaging (SWI).

Assessment: Two neurologists independently evaluated the conventional MRI biomarkers of CSVD, including WMH, enlarged perivascular spaces, lacunes (LAC), and cerebral microbleeds; fasting blood samples were collected both within 72 h and between 8 and 14 days after the onset of the disease to measure MGO.

Statistical tests: T-test, Wilcoxon Mann-Whitney test, chi-square test, Spearman correlation analysis, ANOVA or the Kruskal-Wallis test; logistic regression analysis, intraclass correlation coefficient (ICC). p < 0.05 was considered statistically significant.

Results: Elevated MGO levels at admission were independently associated with CSVD (odds ratio [OR] 1.007, 95% confidence interval [CI] 1.002-1.011), and the severity of WMH (OR 1.004, 95% CI 1.001-1.008) and deep white matter hyperintensities (D-WMH) (OR 1.004, 95% CI 1.000-1.007). MGO levels also showed a positive correlation with LAC (correlation coefficient [r] = 0.225) but no significant correlation with periventricular white matter hyperintensities (PV-WMH) (r = 0.172, p = 0.066). Furthermore, there was no significant difference in MGO levels between admission and discharge (t = 1.932, p = 0.056). The reproducibility of MRI parameters was excellent (ICC 0.966-0.976).

Data conclusion: Elevated MGO levels in patients with first-ever SAO were associated with the presence and severity of WMH, particularly D-WMH.

Level of evidence: 1 TECHNICAL EFFICACY: Stage 1.

Background: Although the clear cell likelihood score (ccLS) v2.0 demonstrates high specificity for clear cell renal cell carcinoma (ccRCC), its performance to characterize general malignancy in small renal masses (SRMs) remains limited.

Purpose: To develop and validate a modified clear cell likelihood score (m-ccLS) incorporating the pseudocapsule to improve malignancy detection in SRMs while preserving specificity for diagnosing ccRCC.

Study type: This study was retrospective in type.

Subjects: 352 patients with pathologically proven SRMs were included: development (n = 235), internal validation (n = 60), and external validation (n = 57).

Field strength/sequence: Imaging was performed at 3.0 and 1.5 T using fast spin-echo T2-weighted imaging, single-shot echo planar diffusion-weighted imaging, 3D spoiled gradient echo (GRE) T1-weighted dynamic contrast-enhanced imaging, and in- and opposed-phase using T1-weighted GRE.

Assessment: 14 radiologists blinded to histopathology independently evaluated each SRM using ccLS v2.0 and m-ccLS scores in separate reading sessions; four, five, and five readers interpreted the development, internal, and external cohorts, respectively.

Statistical tests: Random-effects logistic regression, receiver operating characteristic curve, DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and Fleiss Kappa test were used. The statistical significance level was p < 0.05.

Results: For malignancy detection, m-ccLS showed a significantly higher area under the curve (AUC) than ccLS v2.0 across the development (0.850 vs. 0.772), internal validation (0.856 vs. 0.779), and external validation (0.803 vs. 0.720) cohorts with improved classification (NRI = 0.270, 0.045, and 0.028) and discrimination (IDI = 0.132, 0.206, and 0.120). For diagnosing ccRCC, m-ccLS and ccLS v2.0 showed similar results (0.908 vs. 0.894, p = 0.250; 0.912 vs. 0.898, p = 0.134; 0.865 vs. 0.838, p = 0.065) in development, internal, and external validation cohorts, respectively. m-ccLS category 3 contained fewer ccRCCs (33.3% vs. 72.5%; 15.8% vs. 47.2%; 7.6% vs. 26.9%) and malignancies (79.2% vs. 88.7%; 71.6% vs. 73.0%; 55.4% vs. 63.9%) than ccLS v2.0 category 3.

Data conclusion: m-ccLS improves malignancy detection in SRMs compared with ccLS v2.0 without impairing diagnostic performance for ccRCC.

Evidence level: 4.

Technical efficacy: Stage 2.

Background: The pulsatile expansion of pulmonary vessels carries dynamic cardiopulmonary information that may reveal disease earlier than structural changes alone.

Purpose: To test (i) intra- and inter-scan repeatability of dynamic vessel metrics in healthy subjects, and (ii) whether chronic obstructive pulmonary disease (COPD) and postcapillary pulmonary hypertension (PH) exhibit disease-specific differences.

Study type: Retrospective, multi-cohort feasibility study with repeatability sub-study.

Subjects: Healthy: 29 (11 female), COPD: 52 (15 female), PH: 25 (15 female) with isolated postcapillary PH.

Fieldstrength/sequence: 1.5T, free-breathing spoiled gradient-echo sequence, TE = 0.82 ms, TR = 3 ms, flip angle = 5°.

Assessment: 2D U-Nets segmented pulmonary vessels throughout each series of 250 images. Dynamic metrics included the coefficient of variation (CV) of vessel area and CV of vessel signal. Delay between vessel signal and vessel expansion as %-of-RR-interval (SE-delay) together with the heart rate and lung area were calculated.

Statistical tests: Repeatability: Friedman; intraclass correlation coefficient (ICC); standard error of measurement (SEM) and the minimal detectable change (MDC). Group comparisons: Kruskal-Wallis and multiple linear regression. p < 0.05 was considered significant.

Results: Only the heart rate and the SE-delay presented significant changes across repetitive scans. Differences in Bland-Altman plots were evenly distributed across the range of measurements and symmetrically scattered around zero within 95% confidence intervals (mean bias 0.08%-5.12%). Across cohorts, CV vessel area and CV lung area differed significantly, showing lowest variability in COPD (2.67%; 7.03%) and highest in PH (4.58%; 16.74%). SE-delay was significantly prolonged in COPD (81.98%) compared to PH (67.20%) and healthy participants (62.97%). Cohort status (Healthy/COPD/PH) remained the strongest significant predictor for all parameters after adjustment (F-values 4.62-34.04), while age and gender had no significant influence (p > 0.391; p > 0.069).

Data conclusion: Free-breathing lung MRI with automated vessel segmentation reveals distinct hemodynamic characteristics in COPD and PH. This method shows potential as a sensitive non-invasive tool for detection, phenotyping, and treatment monitoring in pulmonary pathology.

Evidence level: 3.

Technical efficacy: 2.

Background: Tertiary lymphoid structures (TLS) are associated with enhanced antitumor immune activity in hepatocellular carcinoma (HCC). Dynamic contrast-enhanced (DCE)-MRI-based habitat imaging may enable noninvasive TLS identification.

Purpose: To develop and validate a DCE-MRI-based habitat model to noninvasively identify TLS status in HCC.

Study type: Retrospective.

Subjects: Three hundred and thirty-four patients (mean age, 53.59 years ±11.38; male = 296; training set:test set = 200:134) with pathologically confirmed HCC.

Field strength/sequence: 1.5-T/3.0-T, contrast-enhanced three-dimensional gradient-recalled-echo T1-weighted sequence.

Assessment: Tumor habitats were identified from DCE-MRI via k-means clustering of voxel-wise enhancement patterns. Habitat-derived features and radiomics features were extracted. Five identification models were developed: clinical model, radiomics model, habitat model, clinical-radiomics model, and clinical-radiomics-habitat (hybrid) model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).

Statistical tests: Receiver operating characteristic (ROC) curve analysis, area under the precision-recall curve (AUPRC), DeLong test, decision curve analysis, Spiegelhalter's Z test. Significance level: p < 0.05 (two-sided).

Results: Four habitats with distinct dynamic enhancement patterns were identified. The hybrid model demonstrated the highest discrimination in the training and test sets (AUC = 0.83 [95% CI: 0.78, 0.89] and 0.84 [95% CI: 0.78, 0.91]). It significantly outperformed the clinical-radiomics model (AUC = 0.78 [95% CI: 0.72, 0.84] and 0.77 [95% CI: 0.69, 0.85]), radiomics model (AUC = 0.76 [95% CI: 0.70, 0.83] and 0.75 [95% CI: 0.67, 0.83]), and clinical model (AUC = 0.70 [95% CI: 0.63, 0.78] and 0.68 [95% CI: 0.58, 0.77]) in both sets. Compared to the habitat model (AUC = 0.78 and 0.80), the hybrid model showed significantly better training performance but comparable test performance (p = 0.09).

Conclusion: A hybrid model combining clinical, radiomics, and habitat-derived features was developed for identifying TLS status.

Level of evidence: 3:

Technical efficacy: Stage 2.