European Radiology最新文献

Objectives: Individuals with normal coronary arteries may develop coronary artery disease (CAD). Coronary computed tomography (CT) angiography (CCTA) offers a non-invasive method to assess the development of CAD.

Materials and methods: In a post-hoc observational study of the Scottish Computed Tomography of the HEART (SCOT-HEART) trial, we identified patients with normal coronary arteries on initial CCTA who subsequently underwent clinically indicated CT. Images were visually assessed for the presence, severity, and type of CAD.

Results: Normal coronary arteries on baseline CCTA were present in 524 patients (mean age 53 ± 10 years, 38% male). After a median of 9.3 (Interquartile range, IQR: 9.3-10.8) years, 31 (6%) underwent repeat CCTA and 162 (31%) underwent chest CT. There were no differences in baseline clinical characteristics amongst those who did or did not have repeat CCTA, but those with subsequent chest CT were older and had higher cardiovascular risk scores. CAD was identified on 48% (n = 15) of CCTA and 25% (n = 41) of chest CT. Median time to CT scan on which CAD was identified was 8.1 (IQR: 6.9-9.7) years. There was no difference in all-cause mortality or combined CAD death or non-fatal myocardial infarction in patients who had CAD identified on subsequent CT. However, they were more likely to undergo invasive coronary angiography (adjusted hazard ratio [aHR] 4.94, 95% confidence interval [CI]: 1.95, 12.51; p < 0.001) and revascularization (aHR 19.99, 95% CI: 1.69, 237.1; p = 0.018), adjusted for age and sex.

Conclusion: One third of patients with previously normal CCTA will develop CAD on clinically indicated CT imaging over a 10-year period.

Key points: Question In patients with normal coronary arteries on coronary computed tomography angiography (CCTA), the risk of developing CAD in the future is uncertain. Findings Among 524 patients with normal coronaries, CAD was identified on 48% of CCTA and 25% of chest CT during 10 years of follow-up. Clinical relevance A substantial proportion of patients with initially normal coronary arteries on CCTA later develop CAD, highlighting the need for clinicians to be alert for the development of new CAD in patients with initially normal coronary arteries.

Objective: To identify a quantitative surrogate parameter for the embolic endpoint in genicular artery embolization (GAE).

Materials and methods: Digital subtraction angiography (DSA) images were fused and converted into color maps. Using segmentation software, blush size was measured before and after embolization, and blush reduction ratio (BRR) was calculated. Osteoarthritis severity was graded on radiographs, and clinical outcome was evaluated using the Knee Injury and Osteoarthritis Outcome Score (KOOS) at 6 weeks, 3 months, and 6 months. Embolized vessels and embolic volume were recorded. Blush size and BRR were compared between osteoarthritis grades and across embolized vessels.

Results: GAE using 100-300 µm permanent microspheres was performed in 90 patients with mild to severe osteoarthritis and 23 patients with pain after total knee replacement (post-TKR) (404 vessels). The median number of vessels embolized per session was 4 (range: 1-6) with a median total embolic volume of 3.5 mL (1.1-8.0 mL). Pre-embolization blush size (+ 1116 mm²/osteoarthritis grade; p < 0.0001) and embolic volume (+ 1.1 mL/OA grade; p < 0.0001) increased with higher osteoarthritis grade and post-TKR. Blush size significantly decreased after embolization (p < 0.0001) with a median BRR of 0.81 (0.62-0.94). No significant differences in BRR were observed between osteoarthritis grades and different vessels. All KOOS subscales improved significantly at each follow-up (p < 0.0001).

Conclusion: Segmentation of blush size enables quantitative assessment of embolic endpoints across all genicular arteries and osteoarthritis grades, including post-TKR cases. "Pruning" corresponds to a blush size reduction of 80%. Higher osteoarthritis grades are associated with larger blush areas, requiring higher embolic volumes to achieve comparable embolic endpoints.

Key points: Question Standardized, quantitative assessment of embolic endpoints in GAE is lacking, as the angiographic endpoint "pruning" has so far been defined only subjectively. Findings Segmentation of angiographic blush using color-coded maps enables objective quantification of embolic endpoints. With increasing osteoarthritis grade, baseline blush size and embolic volume increase, while an 80% blush reduction defines the endpoint "pruning." Clinical relevance Objective blush quantification improves the reproducibility of embolic endpoint assessment in GAE and supports individualized embolization strategies across disease severity and vascular territories.

Objectives: To investigate MRI-based habitat analysis for its value in predicting pathologic response following neoadjuvant chemoradiotherapy (nCRT) in rectal cancer (RC) patients.

Materials and methods: 1021 RC patients in three hospitals were divided into the training and test sets (n = 319), the internal validation set (n = 317), and external validation sets 1 (n = 158) and 2 (n = 227). Deep learning was performed to automatically segment the entire lesion on high-resolution MRI. Simple linear iterative clustering was used to divide each tumor into subregions, from which radiomics features were extracted. The optimal number of clusters reflecting the diversity of the tumor ecosystem was determined. Finally, four models were developed: clinical, intratumoral heterogeneity (ITH)-based, radiomics, and fusion models. The performance of these models was evaluated. The impact of nCRT on disease-free survival (DFS) was further analyzed.

Results: The Delong test revealed the fusion model (AUCs of 0.867, 0.851, 0.852, and 0.818 in the four cohorts, respectively), the radiomics model (0.831, 0.694, 0.753, and 0.705, respectively), and the ITH model (0.790, 0.786, 0.759, and 0.722, respectively) were all superior to the clinical model (0.790, 0.605, 0.735, and 0.704, respectively). However, no significant differences were detected between the fusion and ITH models. Patients stratified using the fusion model showed significant differences in DFS between the good and poor response groups (all p < 0.05 in the four sets).

Conclusion: The fusion model combining clinical factors, radiomics features, and ITH features may help predict pathologic response in RC cases receiving nCRT.

Key points: Question Identifying rectal cancer (RC) patients likely to benefit from neoadjuvant chemoradiotherapy (nCRT) before treatment is crucial. Findings The fusion model shows the best performance in predicting response after neoadjuvant chemoradiotherapy. Clinical relevance The fusion model integrates clinical characteristics, radiomics features, and intratumoral heterogeneity (ITH)features, which can be applied for the prediction of response to nCRT in RC patients, offering potential benefits in terms of personalized treatment strategies.