Chang Linlin, Zhao Kangkang
Department of 2st Gynecologic Oncology, Jilin Cancer Hospital, Changchun, China.
Department of 4st Radiotherapy, Jilin Cancer Hospital, Changchun, China.
Front Oncol. 2024 May 8;14:1397454. doi: 10.3389/fonc.2024.1397454. eCollection 2024.
To facilitate patient consultation and assist in clinical decision-making, we developed a predictive model to analyze the overall survival (OS) rate of cervical cancer patients with concurrent lung metastasis for 6 months, 1 year, or 2 years.
We extracted data on patients diagnosed with cervical cancer and concurrent lung metastasis between 2010 and 2020 from the Surveillance, Epidemiology, and End Results (SEER) database. Through a random assignment process, these patients were allocated to either a training cohort or a validation cohort, maintaining a 7:3 ratio. Utilizing both univariate and multivariate Cox regression analyses, we determined the independent prognostic factors influencing OS. To enhance predictive accuracy, we developed a nomogram model incorporating these identified independent prognostic variables. Model effectiveness was subsequently assessed using various metrics, including receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
We gathered data on 1330 patients diagnosed with cervical cancer with lung metastases. An OS nomogram was developed, accounting for factors such as histological type, presence of metastases in other organs (brain, liver), surgical interventions, radiation therapy, and chemotherapy. The ROC curves, calibration plots, and DCA curves demonstrated the commendable predictive performance of the nomogram in assessing the prognosis of cervical cancer patients with lung metastases in both the training and validation cohorts.
By utilizing clinical data from the SEER database, we have effectively devised a nomogram capable of predicting the 6-month, 1-year, and 2-year survival rates of cervical cancer patients with lung metastases. The nomogram boasts high accuracy, offering precise prognostic predictions. Its implementation can guide the formulation of individualized follow-up and treatment plans for enhanced patient care.
为便于患者咨询并辅助临床决策，我们开发了一种预测模型，以分析伴有肺转移的宫颈癌患者6个月、1年或2年的总生存率（OS）。
我们从监测、流行病学和最终结果（SEER）数据库中提取了2010年至2020年间被诊断为宫颈癌并伴有肺转移的患者数据。通过随机分配过程，将这些患者按7:3的比例分配到训练队列或验证队列。利用单因素和多因素Cox回归分析，我们确定了影响总生存率的独立预后因素。为提高预测准确性，我们开发了一个纳入这些已确定的独立预后变量的列线图模型。随后使用各种指标评估模型有效性，包括受试者操作特征（ROC）曲线、校准图和决策曲线分析（DCA）。
我们收集了1330例被诊断为宫颈癌伴肺转移患者的数据。开发了一个总生存率列线图，该列线图考虑了组织学类型、其他器官（脑、肝）转移情况、手术干预、放射治疗和化疗等因素。ROC曲线、校准图和DCA曲线表明，该列线图在训练队列和验证队列中评估伴有肺转移的宫颈癌患者预后方面具有值得称赞的预测性能。
通过利用SEER数据库的临床数据，我们有效地设计了一个能够预测伴有肺转移的宫颈癌患者6个月、1年和2年生存率的列线图。该列线图具有很高的准确性，能提供精确的预后预测。其应用可以指导制定个性化的随访和治疗计划，以加强患者护理。
