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用放射性轨迹量化早期肺腺癌进展的量化

Zhen-Bin Qiu^1,2,3, Jiaqi Li^4,5, Shihua Dou^6,7

¹Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.

NPJ digital medicine

|November 18, 2025

概括

RadioTrace是一个新的深度学习框架,通过整合成像和病理学数据来量化早期肺腺癌的进展. 它提供比传统方法更准确的预后,改善临床决策.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

对早期肺腺癌 (esLUAD) 进行准确的瘤进展评估至关重要.
目前的组织病理学分类可能无法完全捕捉瘤内异质性.
需要先进的方法来量化esLUAD的进展,超出传统病理学的范围.

研究的目的:

引入RadioTrace,这是一个深度对比学习框架,用于量化esLUAD进展.
整合放射性和病理信息进行全面的评估.
开发一个放射性轨迹来预测瘤进展和患者的结果.

主要方法:

开发了RadioTrace,这是一个深度对比学习框架.
综合CT扫描与病理学数据的放射性特征.
在四个多机构队列中验证了框架.
进行了生存分析,基因组,转录组和纵向CT分析.

主要成果:

RadioTrace准确地预测了瘤表型,例如通过空气空间传播 (STAS) 和淋巴结转移 (LNM).
它作为一个独立的预后因素,显示了显著的生存差异 (p < 0.004).
在相同的病理等级 (p < 0.02) 中显示出显著的生存异质性,突出了当前分级的局限性.

结论:

无线电跟踪提供了ESLUAD进展的定量和可解释的评估.
提供了超越基因病理学的见解,有助于临床决策.
证明了在esLUAD中改善诊断和治疗规划的潜力.

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