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[在大型语言模型的应用在诊断临床上显著的前列腺癌]
1Department of Urology, Peking University Third Hospital, Beijing 100191, China.
Zhonghua wai ke za zhi [Chinese journal of surgery]
|February 10, 2026
概括
大型语言模型 (LLM) 在诊断临床上显著的前列腺癌 (csPCa) 中表现有前途. DeepSeek R1实现了最高的诊断性能,并微调一个开源的LLM与LoRA导致了相似的结果.
科学领域:
- 人工智能在医学中的应用
- 在瘤学瘤学.
- 医学诊断 医学诊断 医学诊断
背景情况:
- 临床上显著的前列腺癌 (csPCa) 的准确诊断对于有效的患者管理至关重要.
- 大型语言模型 (LLM) 是新兴的工具,在医学诊断中具有潜在的应用.
- 评估LLM性能和微调对诊断准确性的影响是必不可少的.
研究的目的:
- 为了评估四个LLM (GPT4.1,DeepSeek R1,Qwen3-235B-A22B,Qwen3-32B) 对csPCa的诊断性能.
- 评价开源LLM (Qwen3-32B) 在低级调整 (LoRA) 微调后的诊断性能改善.
- 确定LLM在减少不必要的前列腺活检,特别是在PSA灰色区域的实用性.
主要方法:
- 对1077名接受前列腺活检的患者进行了回顾性分析.
- 四名LLM使用临床数据,MRI报告和活检结果评估了csPCa诊断.
- 对Qwen3-32B应用了LoRA微调,创建了PCD-Qwen3模型,与使用ROC曲线和AUC的原始和其他LLM相比,性能较好.
主要成果:
- 在DeepSeek R1中,csPCa诊断的AUC值为0.848%,高于Qwen3-32B和Qwen3-235B-A22B.
- 在PSA灰色区域 (4-10μg/L),DeepSeek R1的AUC达到0.765,可能避免了46.3%的活检.
- 微调的PCD-Qwen3模型在Qwen3-32B上显著提高了性能,达到0.831的AUC,相当于DeepSeek R1.1.
结论:
- 在对csPCa进行评估的LLM中,DeepSeek R1表现出最佳的诊断性能.
- 微调LoRA显著提高了Qwen3-32B模型的诊断能力,达到与DeepSeek R1.1相似的性能水平.
- 在改善cspca诊断和优化活检策略方面,LLM显示出相当大的潜力.
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