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Leveraging Large Language Models to Integrate Clinical Knowledge and Machine Learning Predictions for Lymph Node
Hongying Yu1, Bing Liu2, Xian Zeng1
1Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Linjiang Building, No.219, Ningliu Road, Nanjing, 210044, China, 1 13291879390.
Background:
Lymph node metastasis (LNM) is a critical clinical indicator for determining the initial treatment strategy for patients with lung cancer. However, accurately diagnosing LNM preoperatively remains a significant challenge. Data-driven predictive modeling has become a mainstream approach to address this issue, yet it often overlooks existing clinical knowledge. Large language models (LLMs) have demonstrated the potential to predict clinical risks in a zero-shot manner based on the extensive clinical knowledge learned from large-scale corpora.
Objective:
LLMs have demonstrated the potential to predict clinical risks in a zero-shot manner based on the extensive clinical knowledge learned from large-scale corpora. This study aims to investigate the integration of LLM-derived knowledge with data-driven patterns to enhance the accuracy of LNM prediction.
Methods:
We propose a novel ensemble framework that combines the strengths of LLMs and machine learning (ML) models for LNM prediction in lung cancer. Specifically, 3 ML models were trained using clinical data, and their predicted probabilities, along with the original clinical features, were incorporated into prompts for LLMs. Three LLMs-GPT-5.4, GPT-5.4-nano, and DeepSeek-V3.2-were used to independently predict LNM risk 5 times, and 4 ensemble strategies were applied to aggregate their predictions into a final outcome.
Results:
The proposed approach was evaluated on clinical data from 767 patients with lung cancer at Peking University Cancer Hospital. Experimental results show that our proposed framework significantly outperforms base ML models, achieving an area under the curve of 0.781 and an average precision of 0.420. Compared with the no reasoning English setting, both the reasoning English setting and nonreasoning Chinese setting showed a lower area under the curve but higher average precision.
Conclusions:
This study presents a novel knowledge-augmented strategy for integrating the clinical knowledge embedded in LLMs with the statistical patterns captured by ML models to improve the LNM prediction of lung cancer, offering a new paradigm for integrating medical knowledge and patient data in clinical predictions.
