Abstract
BACKGROUND: Lung metastasis is commonly observed in patients with cervical carcinoma and is frequently linked to a poor prognosis. Currently, there is a gap in research specifically addressing the diagnostic and prognostic assessment of lung metastasis in cervical carcinoma patients through the use of nomograms. Therefore, developing effective predictive models is crucial for guiding clinical practice and improving patient management. The objective of this study is to develop and validate nomogram-based models for predicting lung metastasis and prognosis in patients with cervical carcinoma. METHODS: We selected patients from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2000 to 2021. In order to find independent risk factors for lung metastasis in cervical carcinoma patients, we used both univariate and multivariate logistic regression analyses. We also performed univariate and multivariate Cox proportional hazards regression analyses to determine independent prognostic factors for cervical cancer patients with lung metastasis. From these analyses, we constructed two innovative nomograms. We evaluated their performance using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: A total of 12,632 cervical carcinoma patients were included in the study, with 379 patients diagnosed with lung metastasis at the time of their initial diagnosis. Age, marital status, histology, grade, primary site, T stage, N stage, surgery, chemotherapy, liver metastasis, and bone metastasis were identified as independent risk factors for lung metastasis in patients with cervical carcinoma. Also, the lack of chemotherapy and radiotherapy, combined with liver metastasis, were recognized as independent prognostic factors affecting the outcomes of patients with cervical carcinoma and lung metastasis. The predictive performance of the two nomograms for lung metastasis and prognosis in cervical carcinoma patients was verified using ROC curves, calibration, DCA curves, and Kaplan-Meier survival curves in both the training and validation groups. CONCLUSIONS: The two nomograms accurately predict lung metastasis in cervical carcinoma patients and forecast outcomes for those with lung metastases. Therefore, they are significant tools for enhancing personalized clinical decision-making in future practices.