Seminars in Ultrasound Ct and Mri最新文献

Objective: To investigate the association between ablation range and energy of ultrasound-guided（US-guided) microwave ablation (MWA) in the treatment of colorectal liver metastases (CRLM).

Material and methods: 81 patients with colorectal liver metastases (CRLM) received microwave ablation (MWA), which was fixed by single electrode ablation. Conventional ultrasound and contrasted-enhanced ultrasound were required to confirm tumor size, location, and flow perfusion before ablation, then post-ablation contrast-enhanced ultrasound (CEUS) was employed to ascertain the ablation range. Measurements of ablation-zone volume, longitudinal and orthogonal diameters, as well as their ratios to ablation energy (R_AZV/E, R_ALD/E, R_AOD/E) were conducted. The influence of CRLM characteristics on ablation efficiency was further analyzed utilizing linear regression.

Result: The R_AZV/E is 0.70cm³/KJ, R_ALD/E is 2.47mm/KJ, R_AOD/E is 1.70mm/KJ. Both R_ALD/E and R_AOD/E were inversely associated with ablation energy. Through the single attenuation model, the relationship between ablation longitudinal diameter and ablation energy is finally obtained as Y (mm) =-23.27 * exp (-0.06926 * x) + 42.97. The relationship between orthogonal diameter and ablation energy is Y (mm) =-23.43 * exp (-0.05742 * x) + 34.17. MWA plateau values in longitudinal diameter and orthogonal diameter of ablation are 41.87 mm and 34.17mm respectively. And there is no significant correlation between R_AZV/E and CRLM features (such as CEUS blood supply).

Conclusion: Theoretically, considering the safety margin of 5mm on one side, MWA can completely ablate small colorectal liver metastases whose diameter is less than 24mm through a single fixed ablation device. The characteristics of CRLM (such as blood supply of CEUS) have little effect on ablation efficiency.

This review aims to summarize the application of synthetic magnetic resonance imaging (SyMRI) technology in the research of cognitive impairment disorders.A comprehensive literature search was conducted to identify relevant studies on the use of SyMRI in the assessment and management of cognitive impairment disorders. The search was performed by querying PubMed, Web of Science, MEDLINE, Embase, and the China National Knowledge Infrastructure (CNKI) databases. Cognitive impairment disorders can lead to a decline in activities of daily living, affecting patients' social functioning and quality of life, and even the loss of independent living ability, bringing a serious burden to families and society. SyMRI is a novel imaging technique that can provide both structural information and quantitative data, and has shown promising application results in various diseases. The review of existing literature suggests that SyMRI has the potential to contribute to the diagnosis, assessment, early detection, and management of cognitive impairment disorders.The application of SyMRI technology in cognitive impairment disorders is a promising area of research that may provide valuable insights and tools for the clinical management of these conditions. Further studies are warranted to fully explore the potential of this novel imaging technique in this field.

This study aimed to compare the Breast Imaging Reporting and Data System (BI-RADS) classifications, clinical management recommendations, and diagnostic performance of two artificial intelligence (AI) systems-ChatGPT-4o and DeepSeek-V3-with those of an experienced radiologist in interpreting breast ultrasound reports. The clinical inconsistency and referral safety of the AI systems were assessed. A total of 595 breast ultrasound reports were independently and blindly assessed by a radiologist and two AI systems-ChatGPT-4o and DeepSeek-V3. Observers provided BI-RADS categories and management recommendations based on the findings section. Diagnostic performance was assessed using histopathological diagnosis or ≥ 24-month follow-up as the standard. Clinical inconsistency means discordance between the BI-RADS categories and corresponding recommendations. Referral safety was quantified using a standardized referral safety score. Interobserver agreement was substantial for both BI-RADS classification (κ = 0.65) and management recommendations (κ = 0.79). The radiologist recorded the highest malignancy detection sensitivity (0.989), followed by DeepSeek-V3 (0.968) and ChatGPT (0.927). Specificity (0.735-0.753) and negative predictive values (0.981-0.997) were comparable across the models, while positive predictive values were limited (0.403-0.436). BI-RADS and management recommendations for ChatGPT were consistent, whereas DeepSeek-V3 had a 0.4% rate (p >.05). Referral safety scores were similar (ChatGPT: 0.44; DeepSeek-V3: 0.46). ChatGPT-4o and DeepSeek-V3 demonstrated substantial consistency with expert interpretations and acceptable diagnostic performance in US breast evaluations. Although radiologists demonstrated the most accurate and balanced performance, these findings suggest that ChatGPT-4o and DeepSeek-V3 may aid radiology decision support, pending further validation.

The purpose of this prospective study was to investigate the potential correlations among three perfusion methods-multidelay pseudocontinuous arterial spin labelling (MD-PCASL), conventional single-delay pseudocontinuous arterial spin labelling (SD-PCASL), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-in patients with locoregionally advanced nasopharyngeal carcinoma (ANPC). Forty-seven patients with locoregional ANPC were prospectively recruited. Two observers independently calculated the MD-PCASL parameters, including the ATT-corrected tumour blood flow (c-TBF), arterial transit time (ATT), and arterial tumour blood volume (aTBV); the SD-PCASL parameters, including the noncorrected tumour blood flow (nc-TBF); and the DCE-MRI quantitative parameters, including K^trans, K_ep, V_e, and V_p. The intra- and interobserver agreement was evaluated via the intraclass correlation coefficient (ICC) and BlandAltman analysis. Relationships between the MD-PCASL, SD-PCASL and DCE-MRI parameters were assessed via Spearman's rank correlation. The intra- and interobserver reproducibility were excellent, with ICCs ranging from 0.81 to 0.997 and a narrow width of 95% limits of agreement. A good positive correlation was observed between MD-PCASL-related perfusion parameters (c-TBF, aTBV) and DCE-MRI parameters (K^trans, K_ep) (Spearman's rank correlation coefficients: 0.61 to 0.71, p < 0.001). The SD-PCASL (nc-TBF) values were fairly correlated with K^trans (p<0.001, r = 0.53) and K_ep (p<0.001, r=0.51). In addition, there was a good correlation between the MD-PCASL-related perfusion parameters (c-TBF) and the SD-PCASL parameters (nc-TBF) (Spearman's rank correlation coefficients: 0.70, p < 0.001). No correlation was detected between MD-PCASL-related perfusion parameters (c-TBF and aTBV), SD-PCASL (nc-TBF) and DCE-MRI parameters (V_e and V_p) (Spearman's rank correlation coefficients: 0.07 to 0.145, p > 0.05). MD-PCASL multiparametric perfusion, SD-PCASL single-parameter perfusion and quantitative DCE-MRI parameters were correlated and reproducible in the assessment of ANPC. Although SD-PCASL demonstrated a fair correlation with DCE-MRI, the correlation was inferior to that of MD-PCASL with multiple parameters. MD-PCASL, as a noninvasive perfusion imaging tool, may become an alternative for assessing ANPC perfusion levels in the future.

To explore the clinical application value of double contrast-enhanced ultrasound (DCEUS), which combines oral contrast-enhanced ultrasound (OCEUS) with intravenous contrast-enhanced ultrasound (ICEUS), for the differential diagnosis of gastric submucosal tumors (SMTs) and assessment of their malignant risk, with a focus on distinguishing gastrointestinal stromal tumors (GISTs) from benign SMTs. We retrospectively analysed 276 patients with gastric SMTs admitted to our hospital from January 2019 to January 2024, all of whom completed gastric DCEUS before surgery or endoscopic biopsy. Pathological findings served as the gold standard, with all malignant/potentially malignant lesions confirmed as GISTs. Patients were divided into two groups: benign SMTs and GISTs. We analyzed clinical and imaging features, and performed binary logistic regression to identify independent risk factors and construct a prediction model. The model's performance was evaluated using receiver operating characteristic (ROC) curves, and internal validation was performed. Time-intensity curves (TIC) were generated using VueBox. Of 276 patients, 183 (66.3%) had GISTs, and 93 (33.7%) had benign SMTs. Multivariate logistic analysis showed that age, gender, location, layer of origin, enhancement degree, and homogeneity were independent predictors for distinguishing GISTs from benign SMTs. The prediction model had an area under the curve (AUC) of 0.84, accuracy of 80%, sensitivity of 73%, specificity of 84%, positive predictive value (PPV) of 70%, and negative predictive value (NPV) of 86%. Internal validation confirmed stability. TIC analysis showed higher PE and shorter RT in GISTs than benign lesions. DCEUS enhances the ability to distinguish GISTs from benign SMTs by integrating tumor morphology, hemodynamics, and clinical features. Heterogeneous high enhancement may serve as a potential imaging marker for intermediate-high-risk GIST stratification. The Nomogram model provides a quantitative tool for individualized risk assessment.