Predicting the Probability of Tumor-Specific Survival in Patients Diagnosed With Primary Tumors in the Spinal Cord Using Nomogram Models.

ABSTRACT

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The goal of this study was to develop a useful clinical prediction nomogram to accurately predict the cancer-specific survival (CSS) of patients with primary spinal cord tumor (SCT), thereby formulating scientific prevention and aiding clinical decision-making. METHODS: In this study, patients with SCT diagnoses from the surveillance, epidemiology, and end results (SEER) database (2000-2018) were taken into account. Initially, a nomogram was created using the CSS-associated independent factors that were determined from both univariate and multivariable Cox regression analyses. Furthermore, the nomogram's capacity for calibration, ability to discriminate, and actual clinical effectiveness were assessed through calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA), respectively. Finally, a strategy for categorizing SCT patients' risk was developed. RESULTS: This study included 909 SCT individuals. A novel nomogram was developed to forecast SCT patients' CSS, taking into account age, histological type, tumor grade, tumor stage, and radiotherapy. These factors were identified as independent prognostic indicators for CSS in SCT patients. Elderly SCT patients with distant metastasis, advanced tumor grade, received radiotherapy, and confirmed lymphoma have a poor prognosis. Meanwhile, the risk classification system could differentiate SCT patients and realize targeted management. CONCLUSIONS: The developed nomogram has the ability to accurately forecast the CSS in SCT individuals, aiding in precise decision-making during clinical practice, enhancing health planning, maximizing treatment advantages, and ultimately improving patient prognosis.