Analysis of prognostic factors for cervical mucinous adenocarcinoma and establishment and validation a nomogram: a SEER-based study

医学 列线图 肿瘤科 腺癌 宫颈癌 多元分析 单变量 一致性 内科学 多元统计 队列 癌症 统计 数学
作者
Yiping Hao,Qingqing Liu,Ruowen Li,Zhonghao Mao,Nan Jiang,Bingyu Wang,Wenjing Zhang,Baoxia Cui
出处
期刊:Journal of Obstetrics and Gynaecology [Informa]
卷期号:43 (1)
标识
DOI:10.1080/01443615.2022.2153027
摘要

Up to now, there are no relevant studies on prognostic factors of cervical mucinous adenocarcinoma. Therefore, we explored the prognostic factors for cervical mucinous adenocarcinoma, and established and validated the prognostic model using the SEER database. We selected the independent factors through univariate and multivariate analyses. LASSO regression analysis was conducted to identify potential risk factors. In conjunction with LASSO and multivariate analysis, the nomogram incorporated three variables, including age, tumour size, and AJCC stage for OS. The c-index was 0.794 and 0.831 in development and validated cohorts, indicating that this prediction model showed adequate discriminative ability in the development cohort. Besides, calibration curves showed good concordance for the development cohort, as well as the validation cohort. We constructed a first-of-its-kind nomogram to predict cervical mucinous adenocarcinomas OS and it showed better performance than AJCC and FIGO stages. Patients with cervical mucinous adenocarcinoma might benefit from using this model to develop tailored treatments.IMPACT STATEMENTWhat is already known on this subject? Cervical cancer has a variety of pathological types. The biological behaviour of each type is different, and the prognosis is quite different.What do the results of this study add? We analysed and explored the relevant factors affecting the prognosis of cervical mucinous adenocarcinoma.What are the implications of these findings for clinical practice and/or further research? Through the analysis of the SEER dataset, the prognostic factors affecting cervical mucinous adenocarcinoma were identified, and the first predictive model was created to predict the prognosis to help doctors develop individualised treatment plans and follow-up plans.
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