Personalized prognostic prediction tool for high-grade neuroendocrine cervical cancer: a SEER database analysis and single-center validation

Purpose Cervical high-grade neuroendocrine carcinoma (CHGNEC) is a rare but highly aggressive cancer. The purpose of this study is to develop a prognostic nomogram that can accurately predict the outcomes for CHGNEC patients.Methods We analyzed clinical data from the Surveillance, Epidemiology, and End Results (SEER) database of CHGNEC patients, including small-cell neuroendocrine carcinoma (SCNEC) and large-cell neuroendocrine carcinoma (LCNEC). We investigated patient characteristics and prognosis, and developed a prognostic nomogram model for cancer-specific survival in CHGNEC patients. External validation was conducted using real clinical cases from our hospital.Results Our study included 306 patients from SEER database, with a mean age of 49.9 +/- 15.5 years. Most of the patients had SCNEC (86.9%). Among them, 170 died from the disease, while 136 either survived or died from other causes. Our final predictive model identified age at diagnosis, stage 1 status, stage 4 status, T1, N0, and surgery of the primary site as independent prognostic factors for CHGNEC. We validated our model using a group of 16 CHGNEC patients who underwent surgery at our center. The external validation showed that the prognostic nomogram had excellent discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI 0.49-1.00) for the prediction of 3-year cancer-specific survival (CSS) and an AUC of 0.85 (95% CI 0.62-1.00) for the prediction of 5-years CSS. The random survival forest model achieved an AUC of 0.80 (95% CI 0.56-1.00) for 3-years CSS and 0.91 (95% CI 0.72-1.00) for 5-years CSS, indicating its adequacy in predicting outcomes for CHGNEC patients.Conclusion Our study provides an excellent nomogram for predicting the prognosis of CHGNEC patients. The prognostic nomogram can be a useful tool for clinicians in identifying high-risk patients and making personalized treatment decisions.