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Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection
Chongyu Qu1, Allen J Luna2, Thomas Z Li1,2
1Vanderbilt University, Nashville TN 37235, USA.
Summary
Personalized lung cancer risk prediction is now possible. An AI agent dynamically selects the best risk model for each patient using cohort retrieval and Large Language Model (LLM) reasoning.
Area of Science:
- Artificial Intelligence in Medicine
- Medical Imaging and Diagnostics
- Computational Biology and Bioinformatics
Background:
- Lung cancer risk prediction models exhibit significant variability across diverse patient populations and clinical settings.
- No single predictive model demonstrates optimal performance for all patient cohorts.
- Accurate, individualized lung cancer risk assessment is crucial for effective screening and early detection.
Purpose of the Study:
- To develop a personalized lung cancer risk prediction agent that dynamically selects the most appropriate model for individual patients.
- To address the challenge of model generalizability by incorporating cohort-specific knowledge.
- To enhance the accuracy and applicability of lung cancer risk prediction in real-world clinical settings.
Main Methods:
- A two-stage agent pipeline combining retrieval and reasoning techniques.
- Cohort retrieval using FAISS-based similarity search across nine diverse real-world cohorts.
- Large Language Model (LLM) prompted with retrieved cohort data and performance metrics to recommend optimal prediction algorithms from a pool of eight models.
Main Results:
- The proposed agent enables dynamic, cohort-aware lung cancer risk prediction tailored to individual patient profiles.
- The pipeline successfully integrates patient CT scans and structured metadata for personalized risk assessment.
- Facilitates flexible and cohort-driven model selection across diverse clinical populations, improving risk prediction applicability.
Conclusions:
- The developed agent offers a practical approach to individualized lung cancer risk assessment by dynamically selecting appropriate prediction models.
- This personalized strategy addresses the limitations of single-model approaches in heterogeneous patient populations.
- The retrieval and reasoning framework paves the way for more precise and adaptable lung cancer screening tools.
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