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Predicting Chemotherapy Response in Patients With Advanced or Metastatic Pancreatic Cancer Using Machine Learning.

Jamin Koo^1,2,3, Gyucheol Choi¹, Jaekyung Cheon⁴

¹ImpriMed Korea, Inc, Seoul, Republic of Korea.

JCO Clinical Cancer Informatics

|December 2, 2025

Summary

This summary is machine-generated.

Machine learning models can predict survival for advanced pancreatic cancer patients receiving FOLFIRINOX or gemcitabine/nab-paclitaxel. This aids in personalizing chemotherapy selection for better outcomes.

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Area of Science:

Oncology
Machine Learning
Clinical Data Analysis

Background:

Selecting first-line chemotherapy for advanced pancreatic cancer (FOLFIRINOX vs. gemcitabine/nab-paclitaxel) is complex due to varied efficacy and toxicity.
Personalized treatment selection is crucial for improving survival outcomes in metastatic pancreatic cancer.

Purpose of the Study:

To develop machine learning (ML) models for predicting survival outcomes in advanced or metastatic pancreatic cancer.
To utilize routinely available clinical data for guiding chemotherapy regimen selection.

Main Methods:

Retrospective analysis of 191 patients with advanced or metastatic pancreatic cancer.
Development of CatBoost-based ML models to predict 12-month overall survival (OS) for FOLFIRINOX and gemcitabine/nab-paclitaxel.
Optimization of variable selection using 5-fold cross-validation and ROC-AUC for risk classification.

Main Results:

ML models achieved high predictive performance (ROC-AUCs of 0.81 for FOLFIRINOX, 0.82 for GnP).
Significant differences in median OS between predicted high- and low-risk groups for both regimens.
Substantial overlap in risk classification between the two chemotherapy regimens, suggesting potential for personalized selection.

Conclusions:

ML models effectively predict early mortality risk in advanced pancreatic cancer using multicenter data.
Personalized chemotherapy selection based on ML predictions may improve clinical outcomes.
Machine learning offers a valuable tool for optimizing treatment strategies in pancreatic cancer.