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

Wei Zhang; Lichen Ji; Xugang Zhong; Senbo Zhu; Yi Zhang; Meng Ge; Yao Kang; Qing Bi

doi:10.3389/fpubh.2022.884349

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

Front Public Health. 2022 May 31:10:884349. doi: 10.3389/fpubh.2022.884349. eCollection 2022.

Authors

Wei Zhang^{1

2}, Lichen Ji^{2

3}, Xugang Zhong^{1

2}, Senbo Zhu^{2

3}, Yi Zhang^{1

4}, Meng Ge^{2

5}, Yao Kang^{2

6}, Qing Bi^{2

6}

Affiliations

¹ Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China.
² Department of Orthopedics, Zhejiang Provincial People's Hospital, Hangzhou, China.
³ Department of Orthopedics, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
⁴ Department of Hepatobiliary and Pancreatic Surgery and Minimally Invasive Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.
⁵ Graduate Department, Bengbu Medical College, Bengbu, China.
⁶ Department of Orthopedics, Hangzhou Medical College People's Hospital, Hangzhou, China.

Abstract

Background: Pancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of 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 PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent 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 803 (4.22%) out of 19,067 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' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, 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.

Keywords: SEER database; lung metastasis; nomogram; overall survival; pancreatic cancer; predictive factors.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bone Neoplasms* / diagnosis
Bone Neoplasms* / epidemiology
Humans
Liver Neoplasms*
Lung Neoplasms*
Neoplasm Staging
Nomograms
Pancreatic Neoplasms* / diagnosis
Prognosis
Risk Factors
SEER Program