Academic Radiology最新文献

Abdominal aortic aneurysm (AAA) is a potentially fatal condition that is often asymptomatic in its early stages, with treatment strategies that remain controversial due to limited predictive accuracy for rupture risk. Current clinical approaches primarily rely on aneurysm size and growth rates for risk assessment, which are insufficient for identifying high-risk individuals. This review focuses on preclinical models and the development of molecular imaging technologies, which offer high-spatial-resolution visualization of pathological processes at the molecular level. These advancements provide a promising opportunity to characterize AAA beyond anatomical dimensions and address existing gaps in early diagnosis and targeted therapy. We will discuss the progression of pathophysiological alterations in AAA, the principles underlying contrast agents and molecular probes, and recent advancements in vascular wall molecular imaging within preclinical models.

Rationale and objectives: Cognitive decline is common in End-Stage Renal Disease (ESRD) patients, yet its neural mechanisms are poorly understood. This study investigates structural and functional brain network reconfiguration in ESRD patients transitioning to Mild Cognitive Impairment (MCI) and evaluates its potential for predicting MCI risk.

Methods: We enrolled 90 ESRD patients with 2-year follow-up, categorized as MCI converters (MCI_C, n=48) and non-converters (MCI_NC, n=42). Brain networks were constructed using baseline rs-fMRI and high angular resolution diffusion imaging, focusing on regional structural-functional coupling (SFC). A Support Vector Machine (SVM) model was used to identify brain regions associated with cognitive decline. Mediation analysis was conducted to explore the relationship between kidney function, brain network reconfiguration, and cognition.

Results: MCI_C patients showed decreased network efficiency in the structural network and compensatory changes in the functional network. Machine learning models using multimodal network features predicted MCI with high accuracy (AUC=0.928 for training set, AUC=0.903 for test set). SHAP analysis indicated that reduced hippocampal SFC was the most significant predictor of MCI_C. Mediation analysis revealed that altered brain network topology, particularly hippocampal SFC, mediated the relationship between kidney dysfunction and cognitive decline.

Conclusion: This study provides new insights into the link between kidney function and cognition, offering potential clinical applications for structural and functional MRI biomarkers.

Rationale and objectives: This study aimed to compare the diagnostic accuracy of the abbreviated MRI protocol (AP) with the full protocol (FP) in preoperative staging of locally advanced rectal cancer (LARC).

Materials and methods: This prospective single-center study included 131 cases of LARC. All patients underwent the FP rectal MRI, including T2-weighted imaging (T2WI) and contrast-enhanced T1WI, as well as the AP MRI, which included only T2WI. Two independent readers with 10 and 15years of experience in gastrointestinal imaging evaluated all MRI images for both protocols. The interpretation time for each protocol was compared using the Wilcoxon Signed-Rank test. Diagnostic accuracy in predicting tumor stage, mesorectal fascia (MRF) involvement, and extramural venous invasion (EMVI) was assessed using histopathology as the reference standard. The inter-test agreement was evaluated using Cohen's Kappa test.

Results: The AP protocol showed a sensitivity of 82.1%, specificity of 95.3%, and accuracy of 94.4%. In comparison, the FP protocol demonstrated a sensitivity of 91%, specificity of 100%, and accuracy of 97.6% for the local staging of LARC. There was strong agreement between both protocols in T staging, MRF involvement, and EMVI detection, with Cohen's kappa (K) values of 0.862, 0.710, and 0.863, respectively. The median interpretation time for the AP and FP protocols was 12 and 22 minutes, respectively. Moreover, the AP had a significantly shorter interpretation time than the FP (P<.001).

Conclusion: The AP demonstrated high diagnostic performance with significantly reduced interpretation time, suggesting its potential as an alternative in certain clinical settings.

Rationale and objectives: Tuberous sclerosis complex (TSC) is a multisystem genetic disorder. Focusing on central nervous system manifestations, this study developed an imaging-clinical model combining advanced diffusion MRI parameters with neurological clinical features to distinguish TSC1 vs. TSC2 genotypes.

Materials and methods: Eighty-eight patients newly diagnosed with TSC were enrolled. All underwent a stratified genetic testing strategy comprising whole-exome sequencing, whole-genome sequencing, and tissue-specific deep sequencing. Diffusion spectrum imaging provided parameters from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAP-MRI). A combined prediction model was constructed using logistic regression and validated via bootstrap resampling.

Results: A younger age of onset, autism, neuropsychiatric disorders, intracellular volume fraction, and q-space inverse variance were independently associated with TSC2 mutations. The combined model achieved an AUC of 0.879 (95% CI: 0.841-0.917) in the training set and 0.864 (95% CI: 0.803-0.926) in the validation set. By DeLong's test, it significantly outperformed the clinical model (AUC: 0.637, 95% CI: 0.552-0.723; p < 0.001), while the difference from the imaging model (AUC: 0.833, 95% CI: 0.763-0.903) was not statistically significant (p = 0.068). However, net reclassification (NRI = 0.702, p < 0.001) and integrated discrimination improvement (IDI = 0.097, p < 0.001) both supported the combined model's superior classification ability.

Conclusion: Integrating advanced diffusion MRI parameters with clinical data significantly improves prediction of TSC1 vs. TSC2 genotypes. This combined approach offers valuable support for early diagnosis and personalized treatment in TSC.