Leveraging Large Language Models and Survival Analysis for Early Prediction of Chemotherapy Outcomes

TL;DR

This paper presents a novel approach combining Large Language Models and survival analysis to predict chemotherapy outcomes early, using real-world data and ontology-based phenotype extraction, improving accuracy and enabling personalized treatment for breast cancer and other cancers.

Contribution

It introduces an LLM-based method for extracting phenotypes and outcomes from clinical notes, addressing data sparsity and enhancing predictive modeling in chemotherapy treatment.

Findings

Achieved a C-index of 73% in survival prediction.

Predicted treatment outcomes with over 70% accuracy and F1 score.

Validated outcome probabilities with calibration curves.

Abstract

Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity. This study addresses these challenges by employing Large Language Models (LLMs) and ontology-based techniques for phenotypes and outcome label extraction from patient notes. We focused on one of the most frequently occurring cancers, breast cancer, due to its high prevalence and significant variability in patient response to treatment, making it a critical area for improving predictive modeling. The dataset included features such as vitals, demographics, staging, biomarkers, and performance scales. Drug regimens and their combinations were extracted from the chemotherapy plans in the EMR data and shortlisted based on NCCN guidelines, verified with NIH standards, and analyzed through survival modeling. The proposed approach significantly reduced phenotypes sparsity and improved predictive accuracy. Random Survival Forest was used to predict time-to-failure, achieving a C-index of 73%, and utilized as a classifier at a specific time point to predict treatment outcomes, with accuracy and F1 scores above 70%. The outcome probabilities were validated for reliability by calibration curves. We extended our approach to four other cancer types. This research highlights the potential of early prediction of treatment outcomes using LLM-based clinical data extraction enabling personalized treatment plans with better patient outcomes.