Development and Validation of a Nomogram for Predicting Postoperative Distant Metastasis in Patients with Cervical Cancer

BACKGROUND: Cervical cancer is the fourth most commonly diagnosed malignant neoplasm among women worldwide. Despite improvements in treatment, the rate of postoperative metastasis remains a problem. Nomograms have been used to predict risk of tumor metastasis. We designed a nomogram to predict postoperative distant metastasis among cervical cancer patients, based on the SEER database, and estimated the performance of the nomogram by internal and external validations. MATERIAL/METHODS: We included 6421 participants and divided them into training (n=4495) and testing (n=1926) sets. Multivariate logistic regression was used to explore predictors. The nomogram’s predictive value was assessed by internal (testing set) and external (561 Chinese patients) validations. The receiver operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) value was calculated to evaluate the nomogram’s discrimination. The nomogram’s calibration was assessed via the Hosmer-Lemeshow test and calibration curve. RESULTS: Histologic type, T stage, treatment, tumor size, and positive lymph node were identified as independent predictors of postoperative distant metastasis in surgical patients (P<0.05). The developed nomogram had an AUC of 0.866 (95% CI: 0.844 to 0.888). The AUC and the chi-square for the Hosmer-Lemeshow test of the nomogram were 0.847 (95% CI: 0.807 to 0.888) and 11.292, respectively, (P>0.05) in the internal validation, and were 0.626 (95% CI: 0.548 to 0.704) and 316.53, respectively, (P<0.05) in the external validation. CONCLUSIONS: Our nomogram showed a good predictive performance for postoperative distant metastasis in cervical cancer patients based on the SEER database. It remains to be determined if it is applicable to other populations.