Prediction of 12-month recurrence of pancreatic cancer using machine learning and prognostic factors.

Abstract

Background and aim: Pancreatic cancer is lethal and prevalent among other cancer types. The recurrence of this tumor is high, especially in patients who did not receive adjuvant therapies. Early prediction of PC recurrence has a significant role in enhancing patients' prognosis and survival. So far, machine learning techniques have given us insight into favorable performance efficiency in various medical domains. So, this study aims to establish a prediction model based on machine learning to achieve better prediction on this topic.

Materials and methods: In this retrospective research, we used data from 585 PC patient cases from January 2019 to November 2023 from three clinical centers in Tehran City. Ten chosen ensemble and non-ensemble algorithms were used to establish prediction models on this topic.

Results: Random forest and support vector machine with an AU-ROC of approximately 0.9 obtained more performance efficiency regarding PC recurrence. Lymph node metastasis, tumor size, tumor grade, radiotherapy, and chemotherapy were the best factors influencing PC recurrence.

Conclusion: Random forest and support vector machine algorithms demonstrated high-performance ability and clinical usability to improve doctors' decisions in achieving different therapeutic and diagnostic measures.