用大模型整合影像与病历，个性化评估肺癌筛查风险
Reasoning Language Model for Personalized Lung Cancer Screening
- 构建推理型语言模型，融合影像与长期病历数据
- 在国家肺癌筛查试验数据集上提升预测准确率
- 可拆解风险因素贡献，适合临床决策支持场景
精准的风险评估对肺癌筛查中早期发现癌症和减少不必要的侵入性检查至关重要。肺部CT筛查报告与数据系统（Lung-RADS）虽被广泛用于患者管理和随访，但其仅基于肺结节特征进行风险分层，未纳入多种风险因素，存在敏感性与特异性之间的权衡。本文提出一种推理语言模型（RLM），用于整合放射学发现与纵向医疗记录，实现个体化肺癌风险评估。通过数据集构建、知识蒸馏、监督微调、强化学习及全面评估的系统研究，该模型在国家肺癌筛查试验数据集上显著提升了风险预测性能。值得注意的是，RLM可将风险评估任务分解为子任务，分析不同风险因素的贡献，并通过数据驱动的系统方程合成最终风险评分。该方法通过思维链推理过程同时提升预测准确性和可监控性，推动临床转化应用于肺癌筛查。
原文摘要 · Abstract (English)
Accurate risk assessment in lung cancer screening is critical for enabling early cancer detection and minimizing unnecessary invasive procedures. The Lung CT Screening Reporting and Data System (Lung-RADS) has been widely used as the standard framework for patient management and follow-up. Nevertheless, Lung-RADS faces trade-offs between sensitivity and specificity, as it stratifies risk solely based on lung nodule characteristics without incorporating various risk factors. Here we propose a reasoning language model (RLM) to integrate radiology findings with longitudinal medical records for individualized lung cancer risk assessment. Through a systematic study including dataset construction and distillation, supervised fine-tuning, reinforcement learning, and comprehensive evaluation, our model makes significant improvements in risk prediction performance on datasets in the national lung screening trial. Notably, RLM can decompose the risk evaluation task into sub-components, analyze the contributions of diverse risk factors, and synthesize them into a final risk score computed using our data-driven system equation. Our approach improves both predictive accuracy and monitorability through the chain of thought reasoning process, thereby facilitating clinical translation into lung cancer screening.
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