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将低剂量计算机断层扫描的深度学习与肺癌风险预测临床数据相结合

Renzo Phellan Aro¹, Stephen Lam², Matthew T Warkentin³

¹Prosserman Centre for Population Health Research. Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.

|March 16, 2026

概括

将深度学习与临床数据相结合,可以改善肺癌风险预测. 新的Sybil-Epi模型显示了增强的准确性,特别是当低剂量计算机断层扫描中没有结节时.

科学领域:

放射学和成像学放射学和成像学
在瘤学瘤学.
人工智能在医学中的应用

背景情况:

低剂量计算机断层扫描 (LDCT) 查对于降低肺癌死亡率至关重要.
像Sybil这样的无细分深度学习 (DL) 模型提供了潜在的选效率改进,但需要进一步验证和增强.

研究的目的:

调查是否将DL与LDCT扫描和临床数据整合在一起可以提高肺癌风险预测.
与现有的DL模型相比,评估一种新型综合模型 (Sybil-Epi) 的性能.

主要方法:

在四个查计划 (2002-2021) 中,对来自22469名参与者的52,482个LDCT扫描进行了回顾性分析.
经过训练并经过外部验证的DL模型 (Sybil和Sybil-Epi) 能够在扫描后6年内预测肺癌风险.
根据结节的存在和大小分层的接收器操作特征曲线 (AUC) 下的计算面积;评估的临床流行病学因素.

主要成果:

西比尔的AUC从0.93 (第1年) 到0.79 (第6年) 之间,缺少或小结节的性能低于最佳 (AUC=0.64,0.61).
综合的Sybil-Epi模型在6年实现了较高的AUC (0.83) 与Sybil (0.80) 相比.
在没有结节的情况下,Sybil-Epi显著改善了AUC (0.76) 与Sybil (0.64) 相比.

结论:

虽然Sybil对短期风险有效,但当没有检测到结节时,它的准确性会下降.
结合DL和临床流行病学数据的Sybil-Epi模型显著提高了肺癌风险预测的准确性,特别是在没有结节的情况下.

关键词:

深度学习是一种深度学习.低剂量计算机断层扫描.肺癌是一种肺癌.风险预测风险预测

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