Manifestation of Urinary Tract Injury during Cervical Cancer Surgery Based on CT Urography Secretion Phase Images

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Abstract

Object. CT imaging can be processed by computer, and the absorption coefficient of each voxel to X-ray can be obtained by calculation, which can effectively improve the efficiency of surgery. Traditional treatment is based on the patient's age, fertility requirements, and general conditions, often using a comprehensive treatment plan with surgery and radiotherapy as the mainstay, supplemented by chemotherapy, which has great limitations and side effects, in order to alleviate the loss of various body functions, especially the urinary tract during cervical cancer surgery. Methods. We grouped the patients who had undergone cervical cancer surgery in a hospital in this article and compared the nanodrug carrier system under CT imaging with traditional laparoscopy. The postoperative physical parameters of surgical patients are collected from cervical cancer patients of different degrees, and the parameters and prognostic health of patients after different operations are compared. Results. The results of the study show that the postoperative patient's body parameters of the nanodrug delivery system under the CT imaging technology used in this article are better than those of the traditional surgery group, and the average intraoperative blood loss is about 20% less than that of the traditional surgery. Postoperative complications occur. The situation is even lower, more than 30% lower than traditional surgery. Conclusion. This shows that the operation of the nanodrug delivery system based on CT imaging technology has broken through some of the limitations of the development of laparoscopic technology and has played an important role in the surgical treatment of cervical cancer.

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Lin, S., Li, X., Zhang, Y., Mao, X., Liang, X., Cheng, S., & Zhang, L. (2022). Manifestation of Urinary Tract Injury during Cervical Cancer Surgery Based on CT Urography Secretion Phase Images. Contrast Media and Molecular Imaging, 2022. https://doi.org/10.1155/2022/2572681

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