Role of the screening with coronary computed tomography angiography on lipid management and risk factors control in an asymptomatic Chinese population: a community-based, parallel-group, open-label, randomized clinical trial (RESPECT2).

BACKGROUND: Lipid management based on cardiovascular risk level is the cornerstone of primary prevention of coronary artery disease (CAD), while the accuracy and adherence of traditional cardiovascular risk stratification have been questioned. Prevention strategies based on imaging screening for atherosclerotic plaques are found to be more objective and adherent in recent studies. This trial aims to investigate the role of coronary computed tomography angiography (CCTA) in guiding the primary prevention of CAD in a randomized controlled design. METHODS: Approximately 3400 middle-aged asymptomatic community participants will be recruited and randomized in a 1:1 ratio to a traditional cardiovascular risk score-guided (usual care group) or CCTA-guided (CCTA group) strategy. Participants with cardiovascular disease, prior lipid-lowering therapy, CCTA contraindication, or serious diseases that affect life span will be excluded. The intervention strategy includes blood pressure, blood glucose, and lipid management and lifestyle modifications. Blood pressure and glucose targets and lifestyle modification recommendations keep the same in both strategies, while lipid management is personalized based on traditional risk level or CCTA results, respectively. The primary outcome is the proportion of participants taking lipid-lowering medication regularly at both 6 and 12 months. The secondary outcomes include the proportion of participants achieving low-density lipoprotein cholesterol lowering targets at 12 months, mean changes in lipid levels from baseline to 12 months, barriers to adherence, adverse reactions related to CCTA examination, and cardiovascular events. DISCUSSION: The study is the first randomized clinical trial to examine the effectiveness of a CCTA-guided versus a traditional risk score-guided primary prevention strategy in an asymptomatic community-based population. TRIAL REGISTRATION: ClinicalTrials.gov NCT05725096. Registered on 2 February 2023.

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

BACKGROUND: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer. METHODS: Computed tomography (CT) data sets of 197 prostate cancer patients were collected. Two auto-delineation models were built for radical radiotherapy and postoperative radiotherapy of prostate cancer respectively, and each model included CTVn for pelvic lymph nodes and CTVp for prostate tumors or prostate tumor beds. RESULTS: In the radical radiotherapy model, the volumetric dice (VD) coefficient of CTVn calculated by AI, was higher than that of the one delineated by the junior physicians (0.85 vs. 0.82, p = 0.018); In the postoperative radiotherapy model, the quantitative parameter of CTVn and CTVp, counted by AI, was better than that of the junior physicians. The median delineation time for AI was 0.23 min in the postoperative model and 0.26 min in the radical model, which were significantly shorter than those of the physicians (50.40 and 45.43 min, respectively, p < 0.001). The correction time of the senior physician for AI was much shorter compared with that for the junior physicians in both models (p < 0.001). CONCLUSION: Using deep learning and attention mechanism, a highly consistent and time-saving contouring model was built for CTVs of pelvic lymph nodes and prostate tumors or prostate tumor beds for prostate cancer, which also might be a good approach to train junior radiation oncologists.

Knowledge as a key determinant of public support for autonomous vehicles.

Autonomous vehicles (AVs) have the potential to revolutionize transportation safety and mobility, but many people are still concerned about the safety of AVs and hesitate to use them. Here we survey 4112 individuals to explore the relationship between knowledge and public support for AVs. We find that AV support has a positive relationship with scientific literacy (objective knowledge about science) and perceived understanding of AV (self-assessed knowledge). Respondents who are supportive of AVs tended to have more objective AV knowledge (objective knowledge about AVs). Moreover, the results of further experiments show that increasing people's self-assessed knowledge or gaining additional objective AV knowledge may contribute to increasing their AV support. These findings therefore improve the understanding of the relationship between public knowledge levels and AV support, enabling policy-makers to develop better strategies for raising AV support, specifically, by considering the role of knowledge, which in turn may influence public behavioural intentions and lead to higher levels of AV acceptance.

Incremental value of enhanced plaque length for identifying intracranial atherosclerotic culprit plaques: a high-resolution magnetic resonance imaging study.

OBJECTIVES: Besides plaque enhancement grade, the incremental value of enhancement-related high-resolution MRI features in defining culprit plaques needs further evaluation. This study was focused on assessing whether plaque enhancement features contribute to culprit plaque identification and further risk stratification. METHODS: We retrospectively studied patients who experienced an acute ischaemic stroke and transient ischaemic attack due to intracranial atherosclerosis from 2016 to 2022. The enhancement features included enhancement grade, enhanced length, and enhancement quadrant. Associations between plaque enhancement features and culprit plaques, as well as diagnostic value, were investigated using logistic regression and receiver operating characteristic analyses. RESULTS: Overall, 287 plaques were identified, of which 231 (80.5%) and 56 (19.5%) were classified as culprit and non-culprit plaques, respectively. Comparison of the pre- and post-enhancement images revealed enhanced length longer than the plaque length in 46.32% of the culprit plaques. Multivariate logistic regression showed that enhanced length longer than plaque length (OR 6.77; 95% CI 2.47-18.51) and grade II enhancement (OR 7.00; 95% CI 1.69-28.93) were independently associated with culprit plaques. The area under the curve value for the combination of stenosis and plaque enhancement grade for the diagnosis of culprit plaques was 0.787, which increased significantly to 0.825 on the addition of enhanced length longer than the plaque length (p = 0.026 for DeLong's test). CONCLUSIONS: Enhanced length longer than the plaque length and grade II enhancement were independently associated with culprit plaques. The combination of the enhanced plaque features resulted in better culprit plaque identification.