Predicting Early-Onset Colorectal Cancer with Large Language Models

2025/06/13 by Wilson Chun Yu Lau, Lau, Wilson, Youngwon Kim +9
Medicine · #Colorectal Cancer Screening and Detection #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2506.11410

Abstract

The incidence rate of early-onset colorectal cancer (EoCRC, age < 45) has increased every year, but this population is younger than the recommended age established by national guidelines for cancer screening. In this paper, we applied 10 different machine learning models to predict EoCRC, and compared their performance with advanced large language models (LLM), using patient conditions, lab results, and observations within 6 months of patient journey prior to the CRC diagnoses. We retrospectively identified 1,953 CRC patients from multiple health systems across the United States. The results demonstrated that the fine-tuned LLM achieved an average of 73% sensitivity and 91% specificity.

Citations

• RareBench: Can LLMs Serve as Rare Diseases Specialists?
• Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data
• Explainability for Large Language Models: A Survey
• Colorectal cancer screening for average‐risk adults: 2018 guideline update from the American Cancer Society

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