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使用CT放射学进行第一阶段肺腺癌中第三级淋巴状结构的术前评估:多中心回顾性队列研究

Xiaojiang Zhao¹, Yuhang Wang², Mengli Xue^3,4

¹Chest hospital, Tianjin University, Tianjin, China.

Cancer imaging : the official publication of the International Cancer Imaging Society

|December 19, 2024

概括

一种名为RAITS的新模型,使用CT扫描,可以在早期肺癌中识别瘤微环境. 该工具有助于预测患者的结果,并为I期肺腺癌个性化治疗策略.

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:

瘤透淋巴细胞 (TLS) 在肺腺癌 (LUAD) 预后中至关重要.
在手术前准确识别TLS是具有挑战性的.
针对个性化治疗策略需要新的预测模型.

研究的目的:

开发一个多式预测模型,Radiomics集成TLS系统 (RAITS),用于在I阶段的LUAD中识别TLS.
评估RAITS在预后分层和指导个性化治疗方面的潜力.

主要方法:

从I阶段LUAD患者的手术前CT扫描和病理学数据的回顾性收集.
使用3D U-Net卷积神经网络进行自动瘤细分.
从瘤和周区域提取放射性和深度学习特征;构建和验证RAITS模型.

主要成果:

与单模模型相比,RAITS在培训和验证队列中表现出优异的性能 (AUC,灵敏度,特异性).
在验证队列中,RAITS获得了0.78的AUC,并且在风险分层方面表现出显著的歧视能力.
通过RAITS识别的TLS阳性患者的无复发存活率 (RFS) 显著提高.

结论:

基于手术前CT放射性特征的RAITS模型,在I阶段的LUAD中有效地识别了TLS.
RAITS在临床决策中显示出潜在的价值,用于预后分层和个性化治疗.

关键词:

深度学习是一种深度学习.肺部腺癌瘤是肺部腺癌.无线电学 (Radiomics) 是一种辐射学.三级淋巴状结构的第三级淋巴状结构.

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