Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study

Abstract

Background and aim

Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study.

Materials and methods

In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm.

Results

The XG-Boost with AU-ROC of 0.933 (95% CI= [0.906–0.958]) and AU-ROC of 0.836 (95% CI= [0.789–0.865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction.

Conclusion

The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients.

Highlights

We developed machine learning models to predict the mortality risk of pancreatic cancer.

XG-Boost demonstrated more competency in predicting mortality risk.

Prognostic factors are essential for predicting the mortality risk of PC.

Based on the external validation results, the clinical applicability of the XG-Boost is almost efficient in other clinical environments.

Some lifestyle factors, such as smoking, have a significant role in predicting the mortality risk on this topic.