Predictors of Para-aortic Lymph Node Metastasis in Patients With Locally Advanced Cervical Cancer Based on the Pooled Analysis of Surgical Staging Results
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 452
- 试验地点
- 1
- 主要终点
- The prediction model of para-aortic lymph node metastasis
研究概览
简要总结
The goal of this observational study is to identify predictive factors and to develop a risk model predicting para-aortic lymph node metastasis in patients with locally advanced cervical cancer based on the analysis of surgical staging results. The main questions it aims to answer are:
- What are the risk factors to predict para-aortic lymph node metastasis in patients with locally advanced cervical cancer?
- What is the indication for prophylactic extended-field radiation therapy in patients with locally advanced cervical cancer Individual data of patients with locally advanced cervical cancer treated with surgical staging at our institution from 2020 to 2022 were pooled analysed.Multivariate Logistic regression analysis was used to identify the predictive factors and to develop the prediction model.
详细描述
Individual data of 336 patients with locally advanced cervical cancer treated with surgical staging at our institution from January 2020 to August 2022 were pooled analysed. The following factors were collected from each patient to identify variables predicting para-aortic lymph node metastasis: age, T-staging，histopathological type，tumor size, differentiation, pretreatment tumor markers (squamous carcinoma antigen, carcinoembryonic antigen, Carbohydrate antigen 125 and cytokeratin fragment 21-1 , human papilloma virus type, the status of pelvic lymph node on images, common iliac lymph node and the short-axis diameter of the largest positive and the status of para-aortic lymph node on surgical staging results. Multivariate Logistic regression analysis was used to develop the prediction model. A simplified scoring system for each independent predictive factors was developed according to its coefficient. Internal validation was performed to assess the model. An independent validation cohort contained 116 patients with the same criteria from March 2018 to December 2019.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •In 2018, the International Federation of Obstetrics and Gynecology (FIGO) stage was Ib3 IIA2-IVA;
- •It was treated initially without surgical and chemotherapy.
- •Squamous cell carcinoma, adenocarcinoma and adeno-squamous cell carcinoma were confirmed by histopathology.
- •Abdominal pelvic CT, MRI or PET/CT were performed before treatment.
- •Patients with successful surgical staging and the pathological data of para-aortic lymph node were obtained.
排除标准
- •Patients were excluded if the histopathological type was not squamous cell carcinoma or Adenocarcinoma, and the data of LN status was not available.
结局指标
主要结局
The prediction model of para-aortic lymph node metastasis
时间窗: 3 months
The multivariable logistic regression analysis between predictors and para-aortic lymph node metastasis was conducted and evaluated odds ratio. To facilitate practical application, a score chart was developed to present the final prediction model. The risk score of predictive variables were calculated and rounded based on its beta-coefficients from the multivariate logistic regression analysis. The prediction model was then developed by combining all scores, and the sum of scores for each predictor represented the risk score for every patient.
Predictors of para-aortic lymph node metastasis
时间窗: 3 months
We evaluate the institutional database for medical records to identify patients who underwent surgical staging, then comprised the primary and the independent validation cohort, respectively. The variables were collected from each patient. We assess the bivariate relationship between each variable and para-aortic lymph node metastasis via logistic regression analysis. The potential predictive variables of a P-value\<0.05 on univariate analysis were considered as risk factors.
次要结局
- validation of the prediction model(3 months)
研究者
Dongling Zou
Associated Director
Chongqing University Cancer Hospital