Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma - Yang, Sheng; Yang, Xun; Wang, Huiwen; Gu, Yuelin; Feng, Jingjing; Qin, Xianfeng; Feng, Chaobo; Li, Yufeng; Liu, Lijun; Fan, Guoxin; Liao, Xiang; He, Shisheng (Frontiers in Medicine, 2022)
Background: The study aimed to investigate the prognostic factors of spinal cord astrocytoma (SCA) and establish a nomogram prognostic model for the management of SCA patients. Methods: Patients diagnosed with SCA between 1975 to 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and testing datasets (7:3). Primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). Cox hazard proportional regression model was used to identify the prognostic factors of SCA patients in training dataset and feature importance were obtained. Based on the independent prognostic factors, nomograms were established for prognostic prediction. Calibration curves, concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the calibration and discrimination of nomogram model, while Kaplan-Meier (KM) survival curves and decision curve analyses (DCA) were used to evaluate the clinical utility. Web-based online calculators were further developed to achieve clinical practicability. Results: A total of 818 patients with SCA were included in this study, with an average age of 30.84±21.97 years and an average follow-up time of 117.57±113.51 months. Cox regression indicated that primary site surgery, age, insurance, histologic type, tumor extension, WHO grade, chemotherapy and postoperation radiotherapy (PRT) were independent prognostic factors for OS. While primary site surgery, insurance, tumor extension, PRT, histologic type, WHO grade and chemotherapy were independent prognostic factors for CSS. For OS prediction, the calibration curves in training and testing dataset illustrated good calibration, with C-indexs of 0.783 and 0.769. The AUCs of 5-year survival prediction were 0.82 and 0.843, while 10-year survival prediction were 0.849 and 0.881, for training and testing dataset, respectively. Moreover, the DCA demonstrated good clinical net benefit. The prediction performance of nomogram were verified to be superior to that of single indicators, and the prediction performance of nomogram for CSS is also excellent. Conclusions: Nomograms for SCA patients prognosis prediction demonstrated good calibration, discrimination and clinical utility. This result might benefit clinical decision-making and patient management for SCA. Before further use, more extensive external validation are required for the established web-based online calculators.