Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognos...

Full description

Bibliographic Details
Main Authors: Huaqing Shi, Xin Li, Zhou Chen, Wenkai Jiang, Shi Dong, Ru He, Wence Zhou
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Journal of Personalized Medicine
Subjects:
Online Access:https://www.mdpi.com/2075-4426/13/3/409
_version_ 1797610812306620416
author Huaqing Shi
Xin Li
Zhou Chen
Wenkai Jiang
Shi Dong
Ru He
Wence Zhou
author_facet Huaqing Shi
Xin Li
Zhou Chen
Wenkai Jiang
Shi Dong
Ru He
Wence Zhou
author_sort Huaqing Shi
collection DOAJ
description The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.
first_indexed 2024-03-11T06:19:20Z
format Article
id doaj.art-9397aacb6d094c7b8dfc63e84529a97d
institution Directory Open Access Journal
issn 2075-4426
language English
last_indexed 2024-03-11T06:19:20Z
publishDate 2023-02-01
publisher MDPI AG
record_format Article
series Journal of Personalized Medicine
spelling doaj.art-9397aacb6d094c7b8dfc63e84529a97d2023-11-17T12:01:56ZengMDPI AGJournal of Personalized Medicine2075-44262023-02-0113340910.3390/jpm13030409Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based AnalysisHuaqing Shi0Xin Li1Zhou Chen2Wenkai Jiang3Shi Dong4Ru He5Wence Zhou6Second College of Clinical Medicine, Lanzhou University, Lanzhou 730000, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou 730030, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou 730030, ChinaSecond College of Clinical Medicine, Lanzhou University, Lanzhou 730000, ChinaSecond College of Clinical Medicine, Lanzhou University, Lanzhou 730000, ChinaThe First Clinical Medical College, Lanzhou University, Lanzhou 730030, ChinaSecond College of Clinical Medicine, Lanzhou University, Lanzhou 730000, ChinaThe liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.https://www.mdpi.com/2075-4426/13/3/409nomogrampancreatic cancerliver metastasespredictive modelsoverall survivalSEER database
spellingShingle Huaqing Shi
Xin Li
Zhou Chen
Wenkai Jiang
Shi Dong
Ru He
Wence Zhou
Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis
Journal of Personalized Medicine
nomogram
pancreatic cancer
liver metastases
predictive models
overall survival
SEER database
title Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis
title_full Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis
title_fullStr Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis
title_full_unstemmed Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis
title_short Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis
title_sort nomograms for predicting the risk and prognosis of liver metastases in pancreatic cancer a population based analysis
topic nomogram
pancreatic cancer
liver metastases
predictive models
overall survival
SEER database
url https://www.mdpi.com/2075-4426/13/3/409
work_keys_str_mv AT huaqingshi nomogramsforpredictingtheriskandprognosisoflivermetastasesinpancreaticcancerapopulationbasedanalysis
AT xinli nomogramsforpredictingtheriskandprognosisoflivermetastasesinpancreaticcancerapopulationbasedanalysis
AT zhouchen nomogramsforpredictingtheriskandprognosisoflivermetastasesinpancreaticcancerapopulationbasedanalysis
AT wenkaijiang nomogramsforpredictingtheriskandprognosisoflivermetastasesinpancreaticcancerapopulationbasedanalysis
AT shidong nomogramsforpredictingtheriskandprognosisoflivermetastasesinpancreaticcancerapopulationbasedanalysis
AT ruhe nomogramsforpredictingtheriskandprognosisoflivermetastasesinpancreaticcancerapopulationbasedanalysis
AT wencezhou nomogramsforpredictingtheriskandprognosisoflivermetastasesinpancreaticcancerapopulationbasedanalysis