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Background Objectives Search Strategy Selection Criteria Data Collection and Analysis Main Results Conclusions Bevacizumab serves as an effective treatment in cervical cancer patients with metastatic, recurrent, or advanced disease. However, gastrointestinal (GI)/genitourinary (GU) toxicities have been observed after bevacizumab treatment. Radiotherapy (RT) is the mainstay of treatment of cervical cancer.To investigate the risk of GI/GU toxicities with bevacizumab plus RT compared with RT alone in cervical cancer patients.In this meta‐analysis, PubMed, Embase, Web of Science, and Cochrane databases were searched from inception to September 25, 2022.Cohort studies evaluating the association between bevacizumab and GI/GU fistula or perforation in irradiated metastatic, recurrent, or advanced cervical cancer patients.Results are expressed as odds ratios (OR) with 95% confidence intervals (CI). The inconsistency test (I2) was used to assess heterogeneity. Egger's regression test with a two‐tailed P value was used to evaluate publication bias.Four cohort studies met the inclusion criteria with a total of 597 women included. There was a significant association between GI fistula/perforation and GU fistula/perforation in irradiated cervical cancer patients receiving bevacizumab (OR 4.03 [95% CI: 1.76–9.20] and OR 4.71 [95% CI: 1.51–14.70], respectively).The bevacizumab‐containing regimen was associated with an increased risk of GI or GU toxicities in cervical cancer individuals undergoing pelvic RT. These results suggest the bevacizumab‐associated benefits and risk should be better weighted to reach an optimal treatment strategy. Further investigation on optimal dosage and timing of bevacizumab and RT is vital to minimize the adverse events and maximize the benefits. [ABSTRACT FROM AUTHOR]

Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice. [ABSTRACT FROM AUTHOR]