Dataset Information

Nomogram to predict survival of patients with advanced and metastatic pancreatic Cancer

ABSTRACT:

Background

Nomograms are rarely employed to estimate the survival of patients with advanced and metastatic pancreatic cancer (PC). Herein, we developed a comprehensive approach to using a nomogram to predict survival probability in patients with advanced and metastatic PC. Methods: A total of 323 patients with advanced and metastatic PC were identified from the Chinese People’s Liberation Army (PLA) General Hospital. A baseline nomogram was constructed using baseline variables of 323 patients. Additionally, 233 patients, whose tumors showed initial responses to first-line chemotherapy, were enrolled in the chemotherapy response-based model. 128 patients and 108 patients with advanced and metastatic PC from January 2019 to April 2021 were selected for external validating baseline model and chemotherapy response-based model. The 1-year and 2-year survival probability was evaluated using multivariate COX regression models. The discrimination and calibration capacity of the nomograms were assessed using C-statistic and calibration plots. The predictive accuracy and net benefit of the nomograms were evaluated using ROC curve and DCA, respectively.

Results

In the baseline model, six variables (gender, KPS, baseline TB, baseline N, baseline WBC and baseline CA19–9) were used in the final model. In the chemotherapy response-based model, nine variables (KPS, gender, ascites, baseline N, baseline CA 19–9, baseline CEA, change in CA 19–9 level at week, change in CEA level at week and initial response to chemotherapy) were included in the final model. The C-statistics of the baseline nomogram and the chemotherapy response-based nomogram were 0.67 (95% CI, 0.62–0.71) and 0.74 (95% CI, 0.69–0.77), respectively.

Conclusion

These nomograms were constructed to predict the survival probability of patients of advanced and metastatic PC. The baseline model and chemotherapy response-based model performed well in survival prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08943-w.

SUBMITTER: Deng G

PROVIDER: S-EPMC8594118 | biostudies-literature |

REPOSITORIES: biostudies-literature

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