Published online Jan 15, 2023. doi: 10.4251/wjgo.v15.i1.128
Peer-review started: November 2, 2022
First decision: November 14, 2022
Revised: November 17, 2022
Accepted: December 7, 2022
Article in press: December 7, 2022
Published online: January 15, 2023
Processing time: 69 Days and 4.5 Hours
Pancreatic cancer (PC) with liver metastasis (LM) is a commonly fatal disease with an extremely poor prognosis. Identifying the risk and prognostic factors of PC patients with LM (PCLM) is essential and may aid in providing timely medical interventions to improve the prognosis of these patients.
Few studies have focused on investigating PCLM patients’ risk and prognostic factors, and there are no corresponding diagnostic and prognostic nomograms for these entities.
This study aimed to investigate the risk and prognostic factors of PCLM and establish corresponding diagnostic and prognostic nomograms.
Patients from the Surveillance, Epidemiology, and End Results database with primary PC diagnosed between 2010 and 2015 were reviewed. A multivariate logistic regression analysis was used to identify risk factors to develop a diagnostic model. The least absolute shrinkage and selection operator Cox regression model was used to determine the prognostic factors used to establish a prognostic model. The performance of the two nomogram models was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and risk subgroup classification. Survival analysis was performed using the Kaplan-Meier method with a log-rank test.
A total of 33459 PC patients were included in the study, with 11458 patients (34.2%) having LM at initial diagnosis. Age at diagnosis, primary site, lymph node metastasis, pathological type, tumor size, and pathological grade were identified as independent risk factors for LM in patients with PC. The independent factors associated with poor prognosis for patients with PCLM include age > 70 years, adenocarcinoma, poor or anaplastic differentiation, lung metastasis, no surgery, and no chemotherapy. The C-indices of the diagnostic and prognostic nomograms were 0.731 and 0.753, respectively. Based on the observed analysis results of ROC curves, calibration plots, and DCA curves, the two nomograms could accurately predict the occurrence and prognosis of patients with PCLM. The prognostic nomogram could stratify patients into prognostic groups and perform well in terms of internal validation.
Our study identified the risk and prognostic factors in patients with PCLM and constructed corresponding diagnostic and prognostic nomograms to guide subsequent clinical evaluation and intervention for clinicians.
The nomograms constructed in this study can help clinicians provide better prevention for high-risk subjects and monitor their prognosis. External validation is required to verify these results.