Preoperative Prediction of Adherent Perirenal Fat Based on CT Radiomics Combined With Deep Learning: a Prospective, Multicenter Study.
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- Radiomics features
研究概览
简要总结
In addition to kidney tumor specific factors, adherent perirenal fat is one of the most important causes of technical complications in kidney surgery, and currently, there is a lack of widely used non-invasive predictive models in clinical practice. In this study, a deep learning algorithm based on CT imaging and nomogram was proposed to identify and predict the presence of adherent perirenal fat. This study includes the construction of a prediction model based on CT imaging and the verification of the prediction model.
详细描述
Importance:
For patients with kidney tumors requiring surgical treatment, adhesive perirenal fat is a frustrating variable that surgeons encounter during surgery, but the current image-dependent kidney morphometric scoring system used to predict the potential difficulty of surgery ignores this factor. Accurate preoperative prediction of perirenal fat status remains an urgent need.
Purpose:
To determine whether radiomics features of perirenal fat derived from computed tomography images can provide valuable information for judging perirenal fat status, develop a prediction model based on CT radiomics combined with deep learning, and validate the performance of the model in an independent cohort.
Design, setup and participants:
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 90 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •（1）Renal tumors, patients requiring surgical treatment. (2) Patients with complete preoperative CT image data.
排除标准
- •(1) Preoperative complications such as acute urinary tract infection, hydronephrosis, pulmonary infection, autoimmune disease, and blood system disease.
- •(2) Severe respiratory movement artifacts in CT images. (3) Pregnant or breastfeeding women. (4) Patients who have received immunotherapy or chemoradiotherapy.
结局指标
主要结局
Radiomics features
时间窗: From January 2020 to December 2023.
Radiomics features related to the prediction of adherent perirenal fat.
次要结局
未报告次要终点