AUTHOR=Zhang Wei , Ji Lichen , Zhong Xugang , Zhu Senbo , Zhang Yi , Ge Meng , Kang Yao , Bi Qing 

TITLE=Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.884349

DOI=10.3389/fpubh.2022.884349

ISSN=2296-2565

ABSTRACT=Background: Pancreatic cancer (PC) is one of the most common malignant types of cancer, with the lungs being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of PC patients with lung metastases (PCLM) on diagnosis. As a result, we intend to create two novel nomograms to predict the risk and prognosis of lung metastasis in PCLM.
Methods: PC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess prognosis factors in PCLM patients. Following that, we used receiver operating characteristic (ROC) curves, calibration curves, C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves. 
Results: A total of 805 (4.22%) out of 19067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients’ overall survival (OS). ROC curves, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power. 
Conclusion: We developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.