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基于PET/CT的深度学习放射学模型预测了非小细胞肺癌中PD-L1表达的情况

Bo Li¹, Jie Su¹, Kai Liu¹

¹Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou Medical University, Xuzhou, China.

European journal of radiology open

|February 2, 2024

概括

一种基于PET/CT的新型深度学习和放射学模型准确地预测了非小细胞肺癌 (NSCLC) 中的编程细胞死亡蛋白-1联体 (PD-L1) 表达. 这种非侵入性工具有助于临床医生选择免疫治疗的患者.

科学领域:

放射学放射学是指放射学
在瘤学瘤学.
人工智能的人工智能

背景情况:

编程细胞死亡蛋白-1配体 (PD-L1) 是非小细胞肺癌 (NSCLC) 免疫治疗的关键预后指标.
准确预测PD-L1表达对于有效选择患者接受免疫治疗至关重要.

研究的目的:

开发和评估一种非侵入性深度学习和放射学模型,使用正子发射断层扫描和计算机断层扫描 (PET/CT) 来预测NSCLC中的PD-L1表达.
为了比较开发的核聚变模型与独立的放射学和深度学习模型的性能.

主要方法:

分析了136名NSCLC患者的PET/CT图像,数据分为培训 (70%) 和验证 (30%) 组.
提取了放射学和深度学习特征,并使用统计方法选择了重要的特征 (曼-惠特尼U测试,LASSO,斯皮尔曼相关性).
开发了一种融合模型,将放射学和深度学习功能结合起来,并使用曲线下的面积 (AUC),灵敏度,特异性和精度来评估其性能.

主要成果:

融合模型在训练 (AUC:0.954) 和验证 (AUC:0.910) 数据集中表现出优异的性能,与个人放射学 (AUC:0.829,0.785) 和深度学习模型 (AUC:0.935,0.867) 相比.
融合模型在预测NSCLC患者的PD-L1表达方面取得了很高的准确性.

结论:

开发的基于PET/CT的深度学习放射学融合模型为预测NSCLC中PD-L1表达提供了准确且非侵入性的方法.
这种模型可以帮助临床医生识别适合免疫治疗的候选人,从而有可能改善治疗结果.

关键词:

深度学习是一种深度学习.机器学习机器学习非小细胞肺癌是非小细胞肺癌.在PD-L1中.聚乙烯/聚乙烯/聚乙烯无线电学 (Radiomics) 是一种辐射学.

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