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

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 STATEMENT<b>What is already known on this subject?</b> Cervical cancer has a variety of pathological types. The biological behaviour of each type is different, and the prognosis is quite different.<b>What do the results of this study add?</b> We analysed and explored the relevant factors affecting the prognosis of cervical mucinous adenocarcinoma.<b>What are the implications of these findings for clinical practice and/or further research?</b> 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. <b>What is already known on this subject?</b> Cervical cancer has a variety of pathological types. The biological behaviour of each type is different, and the prognosis is quite different. <b>What do the results of this study add?</b> We analysed and explored the relevant factors affecting the prognosis of cervical mucinous adenocarcinoma. <b>What are the implications of these findings for clinical practice and/or further research?</b> 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.

目前尚无针对宫颈黏液腺癌预后因素的相关研究。因此，本研究围绕宫颈黏液腺癌的预后因素展开探索，并基于SEER数据库（Surveillance, Epidemiology, and End Results Program）构建并验证了预后预测模型。本研究通过单因素与多因素分析筛选独立影响因素，并采用LASSO回归分析识别潜在风险因素。结合LASSO回归与多因素分析结果，本研究构建的列线图（nomogram）纳入了与总生存期（overall survival, OS）相关的三项变量：年龄、肿瘤大小及AJCC分期（American Joint Committee on Cancer, AJCC）。该预测模型在训练队列与验证队列中的C指数（c-index）分别为0.794与0.831，提示其在训练队列中具备良好的区分能力。此外，校准曲线显示，该模型在训练队列与验证队列中均表现出极佳的一致性。本研究构建了首个用于预测宫颈黏液腺癌总生存期的列线图，其预测性能优于AJCC分期与国际妇产科联盟（International Federation of Gynecology and Obstetrics, FIGO）分期系统。宫颈黏液腺癌患者可通过应用该模型制定个体化治疗方案，从而获益。

研究亮点声明
<b>当前已知的研究现状？</b> 宫颈癌存在多种病理亚型，各亚型的生物学行为存在差异，预后亦大相径庭。
<b>本研究新增的研究成果？</b> 本研究分析并探索了影响宫颈黏液腺癌预后的相关因素。
<b>本研究结果对临床实践及后续研究的启示？</b> 通过对SEER数据库的分析，本研究明确了影响宫颈黏液腺癌预后的相关因素，并构建了首个用于预测患者预后的模型，可帮助临床医师制定个体化治疗方案与随访计划。
<b>当前已知的研究现状？</b> 宫颈癌存在多种病理亚型，各亚型的生物学行为存在差异，预后亦大相径庭。
<b>本研究新增的研究成果？</b> 本研究分析并探索了影响宫颈黏液腺癌预后的相关因素。
<b>本研究结果对临床实践及后续研究的启示？</b> 通过对SEER数据库的分析，本研究明确了影响宫颈黏液腺癌预后的相关因素，并构建了首个用于预测患者预后的模型，可帮助临床医师制定个体化治疗方案与随访计划。