Locally Advanced Cervical Cancer
Conditions
Brief summary
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.
Detailed description
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.
Interventions
PROCEDUREsurgical staging
para-aortic lymphadenectomy from the inferior mesenteric artery cranially to the aorta caudally via laparoscopy or laparotomy
Sponsors
Chongqing University Cancer Hospital
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* Patients were excluded if the histopathological type was not squamous cell carcinoma or Adenocarcinoma, and the data of LN status was not available.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predictors of para-aortic lymph node metastasis | 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. |
| The prediction model of para-aortic lymph node metastasis | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| validation of the prediction model | 3 months | We adopted an internal and independent validation to assess the performance of the prediction model.The internal validation of the model was performed with respect to discrimination which was measured by the concordance index and calibration which was assessed by the Hosmer-Lemeshow test and Brier score. The independent validation was performed with respect to sensitivity, specificity and Youden index, which calculated by sensitivity+specificity-1 is a global measure of diagnostic effectiveness. |
Countries
China
Outcome results
None listed