Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population-Based Analysis - Zhang, Wei; Ji, Lichen; Wang, Xijun; Zhu, Senbo; Luo, Junchao; Zhang, Yin; Tong, Yu; Feng, Fabo; Kang, Yao; Bi, Qing (Frontiers in Endocrinology, 2022)
Background: The overall survival (OS) of pancreatic cancer (PC) patients with bone metastasis (BM) is extremely low, and treatment is difficult. However, there are currently no effective nomograms to predict the diagnosis and prognosis of pancreatic cancer with bone metastasis (PCBM). Therefore, it is of great significance to establish an effective predictive model to guide clinical practice. Methods: We screened patients from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2016. The independent risk factors of PCBM were identified from multivariate logistic regression model, and independent prognostic factors affecting the prognosis of PCBM were identified from multivariate Cox regression model. In addition, two nomograms were constructed to predict the risk and prognosis of PCBM. We used the area under curve (AUC), C-index and calibration curve to determine the predictive accuracy and discriminability of nomograms. The decision curve analysis (DCA) and Kaplan-Meier were employed to further confirm the clinical effectiveness of the nomogram. Results: Multivariate logistic regression model revealed that risk factors of PCBM included age, primary site, histological subtype, N stage, radiotherapy, surgery, brain metastasis, lung metastasis, and liver metastasis. Using Cox regression model, we found that independent prognostic factors of PCBM were age, race, grade, histological subtype, surgery, chemotherapy, and lung metastasis. We utilized nomograms to visually express data analysis results. The C-index of training cohort was 0.797 (95%CI: 0.762-0.833), whereas that of internal validation cohort was 0.796 (95%CI: 0.733-0.859), and the external validation cohort was 0.797 (95%CI: 0.756-0.837). Based on AUC of receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance. Conclusion: Nomogram is sufficiently accurate to predict the risk and prognostic factors of PCBM, allowing for individualized clinical decisions for future clinical work.