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基于可切除IIIA (N2) 阶段非小细胞肺癌的整合模型,预测新辅助免疫疗法的治疗反应

Long Xu¹, Haojie Si¹, Fenghui Zhuang¹

¹Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.

The Journal of thoracic and cardiovascular surgery

|May 19, 2024

概括

结合深度学习,血液瘤突变负担 (bTMB) 和临床因素的综合模型显示,预测非小细胞肺癌对新辅助化疗免疫疗法的反应具有前途. 序列ctDNA动态也可靠地监测治疗反应.

科学领域:

在瘤学瘤学.
翻译研究是翻译研究.
基因组学就是基因组学.

背景情况:

在接受新辅助化疗免疫治疗的可切除非小细胞肺癌 (NSCLC) 中预测治疗反应至关重要.
目前评估治疗疗效的方法需要改进,以实现个性化的患者管理.

研究的目的:

评估基于血液的瘤突变负担 (bTMB) 和深度学习 (DL) 模型在预测NSCLC患者的主要病理反应 (MPR) 和生存方面的有效性.
开发和验证结合DL,bTMB和临床因素的综合模型,以提高预测准确度.

主要方法:

从45名IIIA (N2) 期NSCLC接受新辅助化疗免疫治疗的患者收集血液样本.
开发一个综合预测模型,包括基于CT的DL得分,bTMB和临床数据.
对bTMB和循环瘤DNA (ctDNA) 动态的分析,与MPR和生存结果相关.

主要成果:

较高的基线bTMB (≥11突变/兆基) 与显著改善的MPR率和无病生存率相关.
综合模型实现了0.820的AUC,仅仅超过DL模型 (AUC0.703).
手术前ctDNA清除与更高的MPR率和改善的无病生存率密切相关.

结论:

综合模型显示,在NSCLC中预测瘤对新辅助化疗免疫疗法的反应具有显著的潜力.
对ctDNA动态的连续监测提供了一种可靠的方法,用于评估在新辅助疗法期间的治疗反应.

关键词:

基于血液的瘤突变负担在循环中的瘤DNA.深度学习是一种深度学习.新辅助化学免疫治疗新辅助化学免疫治疗不是小细胞肺癌的肺癌

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Tumor Immunotherapy

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