Influencing factors and risk prediction model for cervical cancer recurrence. / å®«é¢ˆç™Œå¤å‘çš„å½±å“å› ç´ åŠé£Žé™©é¢„æµ‹æ¨¡åž‹.

OBJECTIVES: Cervical cancer is the most common malignant tumor in the female reproductive system worldwide. The recurrence rate for the treated cervical cancer patients is high, which seriously threatens women's lives and health. At present, the risk prediction study of cervical cancer has not been reported. Based on the influencing factors of cervical cancer recurrence, we aim to establish a risk prediction model of cervical cancer recurrence to provide a scientific basis for the prevention and treatment of cervical cancer recurrence. METHODS: A total of 4 358 cervical cancer patients admitted to the Hunan Cancer Hospital from January 1992 to December 2005 were selected as research subjects, and the recurrence of cervical cancer patients after treatment was followed up. Univariate analysis was used to analyze the possible influencing factors. Variables that were significant in univariate analysis or those that were not significant in univariate analysis but may be considered significant were included in multivariate Cox regression analysis to establish a cervical cancer recurrence risk prediction model. Line graphs was used to show the model and it was evaluated by using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. RESULTS: Univariate analysis showed that the recurrence rates of cervical cancer patients with different age, age of menarche, parity, miscarriage, clinical stage, and treatment method were significantly different (all P<0.05). Multivariate Cox regression analysis showed that RR=-0.489×(age≥55 years old)+0.481×(age at menarche >15 years old)+0.459×(number of miscarriages≥3)+0.416×(clinical stage II)+0.613×(clinical stage III/IV)+0.366×(the treatment method was surgery + chemotherapy) + 0.015×(the treatment method was chemotherapy alone). The area under the ROC curve (AUC) of the Cox risk prediction model for cervical cancer recurrence constructed was 0.736 (95% CI 0.684 to 0.789), the best prediction threshold was 0.857, the sensitivity was 0.576, and the specificity was 0.810. The accuracy of the Cox risk model constructed by this model was good. From the clinical decision curve, the net benefit value was high and the validity was good. CONCLUSIONS: Patient age, age at menarche, miscarriages, clinical stages, and treatment methods are independent factors affecting cervical cancer recurrence. The Cox proportional hazards prediction model for cervical cancer recurrence constructed in this study can be better used for predicting the risk of cervical cancer recurrence.