ObjectiveTo systematically review the adjuvant endocrine therapy adherence among Chinese patients with breast cancer. MethodsThe Cochrane Library, Web of Science, PubMed, EMbase, CINAHL, CNKI, VIP, WanFang Data and CBM were electronically searched to collect studies on adjuvant endocrine therapy adherence among Chinese patients with breast cancer from inception to September 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, then, meta-analysis was performed by using Stata 16.0 software. ResultsA total of 24 studies were included. The results of meta-analysis showed that: the overall adherence rate of adjuvant endocrine therapy in Chinese breast cancer patients was 55.0% (95%CI 0.44 to 0.65), and a 5-year adherence rate was 54.4% (95%CI 0.46 to 0.63). Subgroup analysis showed that patients with good disease awareness, high education level, high monthly household income, living in cities, effective family support, no adverse drug reactions, high convenience of seeking medical treatment, regular review, health education, no comorbidities, and changes in medication type might have higher compliance. ConclusionThe adherence rate of adjuvant endocrine therapy in breast cancer patients in China is low. Adherence varies between sociodemographic characteristics, treatment, and social support for breast cancer patients.
ObjectiveTo conduct a comprehensive analysis of risk prediction models for acute kidney injury (AKI) following Stanford type A aortic dissection surgery through a systematic review. MethodsA systematic search was performed in English and Chinese databases such as PubMed, EMbase, ProQuest, Web of Science, China National Knowledge Infrastructure (CNKI), VIP, Wanfang, and SinoMed to collect relevant literature published up to January 2025. Two researchers completed the literature screening and data extraction. The methodological quality of the prediction models was assessed using bias risk assessment tools, and a meta-analysis was performed using R version 4.3.1, with a focus on evaluating the predictive factors of the models. Results A total of 15 studies were included (13 retrospective cohort studies, 1 prospective cohort study, and 1 case-control study), involving 22 risk prediction models and a cumulative sample size of 4 498 patients. The overall applicability of the included studies was good, but all 15 studies exhibited a high risk of bias. The meta-analysis revealed that the area under the curve (AUC) for the predictive performance of the models was 0.834 [95%CI (0.798, 0.869)]. Further subgroup analysis indicated that the number of predictive factors was a source of heterogeneity. Additionally, hypertension [OR=2.35, 95%CI (1.55, 3.54)], serum creatinine [OR=1.01, 95%CI (1.00, 1.01)], age [OR=1.05, 95%CI (1.02, 1.09)], and white blood cell count [OR=1.14, 95%CI (1.06, 1.22)] were identified as predictors of AKI following type A aortic dissection surgery. Conclusion Currently, the predictive models for AKI after type A aortic dissection surgery demonstrate good performance. However, all included models carry a high risk of bias. It is recommended to strengthen multicenter prospective studies and external validation of the models to enhance their clinical applicability.
Conventional maximum intensity projection (MIP) images tend to ignore some morphological features in the detection of intracranial aneurysms, resulting in missed detection and misdetection. To solve this problem, a new method for intracranial aneurysm detection based on omni-directional MIP image is proposed in this paper. Firstly, the three-dimensional magnetic resonance angiography (MRA) images were projected with the maximum density in all directions to obtain the MIP images. Then, the region of intracranial aneurysm was prepositioned by matching filter. Finally, the Squeeze and Excitation (SE) module was used to improve the CaraNet model. Excitation and the improved model were used to detect the predetermined location in the omni-directional MIP image to determine whether there was intracranial aneurysm. In this paper, 245 cases of images were collected to test the proposed method. The results showed that the accuracy and specificity of the proposed method could reach 93.75% and 93.86%, respectively, significantly improved the detection performance of intracranial aneurysms in MIP images.