Prognosis prediction is crucial for determining optimal treatment plans for lung cancer patients. Traditionally, such predictions relied on models developed from retrospective patient data. Recently, large language models (LLMs) have gained attention for their ability to process and generate text based on extensive learned knowledge. In this study, we evaluate the potential of GPT-4o mini and GPT-3.5 in predicting the prognosis of lung cancer patients. We collected two prognosis datasets, i.e., survival and post-operative complication datasets, and designed multiple tasks to assess the models' performance comprehensively. Logistic regression models were also developed as baselines for comparison. The experimental results demonstrate that LLMs can achieve competitive, and in some tasks superior, performance in lung cancer prognosis prediction compared to data-driven logistic regression models despite not using additional patient data. These findings suggest that LLMs can be effective tools for prognosis prediction in lung cancer, particularly when patient data is limited or unavailable.

翻译：预后预测对于确定肺癌患者的最佳治疗方案至关重要。传统上，此类预测依赖于基于回顾性患者数据开发的模型。近年来，大语言模型因其基于广泛习得知识处理和生成文本的能力而受到关注。在本研究中，我们评估了GPT-4o mini和GPT-3.5在预测肺癌患者预后方面的潜力。我们收集了两个预后数据集，即生存数据集和术后并发症数据集，并设计了多项任务以全面评估模型的性能。同时，我们还建立了逻辑回归模型作为比较基准。实验结果表明，尽管未使用额外的患者数据，大语言模型在肺癌预后预测方面能够取得与数据驱动的逻辑回归模型相竞争、甚至在某些任务中更优的性能。这些发现表明，大语言模型可以成为肺癌预后预测的有效工具，尤其是在患者数据有限或无法获取的情况下。