使用布洛克模型和西比尔模型对持久性肺结节患者的肺癌风险预测
Hui Li1,2, Morteza Salehjahromi2, Myrna C B Godoy3
1Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers
|May 14, 2025
概括
持续存在的肺结节会增加肺癌的风险. 布罗克和西比尔模型都显示了预测癌症风险的局限性,突出了对早期检测优化模型的需求.
科学领域:
- 肺部病理学 肺部病理学
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 持久的肺结节是患肺癌的重要危险因素.
- 对这些结节的准确风险评估对于及时拦截和改善患者结果至关重要.
研究的目的:
- 评估布洛克模型和西比尔深度学习模型在预测持久性肺结节患者肺癌风险方面的性能.
- 探索机器学习模型的潜力,以改进这种患者群体的风险评估.
主要方法:
- 分析了持久性肺结节 (通过CT扫描在3个月内定义) 的患者的追溯和前性队列.
- 评估了人口因素,结节特征和布洛克分数之间的相关性.
- 评估了Brock和Sybil模型的性能;开发了机器学习模型以提高风险预测.
主要成果:
- 在前性队列 (n=301) 中,62名患者被诊断患有肺癌.
- 在患有肺癌的患者中观察到较高的中位数布洛克得分 (18.65%与4.95%,p < 0.001).
- 与肺癌风险相关的因素包括家族病史,结节大小≥10毫米,部分固体类型和. 布洛克模型的AUC:0.679,西比尔的AUC:0.678. 这是一个很大的问题. 后勤回归实现了最高的AUC,为0.729.
结论:
- 布洛克和西比尔模型都在现实世界医院队列中证明了肺癌风险预测的实用性和局限性.
- 优化预测模型对于增强早期肺癌检测和拦截策略在这个人群中至关重要.
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