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基于深度学习的图像分析可以从非小细胞肺癌的F-FDG PET/CT图像中预测PD-L1状态

Chen Liang¹, Meiyu Zheng¹, Han Zou²

¹Department of Nuclear Medicine, Cangzhou Central Hospital, Cangzhou, China.

Frontiers in oncology

|September 20, 2024

概括

这项研究开发了一个深度学习模型,将18F-FDG PET/CT成像和临床数据结合起来,以预测非小细胞肺癌 (NSCLC) 中的编程死亡配体1 (PD-L1) 表达. 结果显示高精度,表明PET/CT作为一种潜在的非侵入性生物标志物用于免疫疗法选择.

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:

缺乏验证的生物标志物,用于选择非小细胞肺癌 (NSCLC) 患者进行编程死亡-1 (PD-1) /编程死亡-1 (PD-L1) 免疫治疗.
目前的PD-L1检测是侵入性的,一些PD-L1阴性患者受益于免疫治疗,需要改进预测方法.

研究的目的:

通过从18F-FDG PET/CT扫描和临床数据中联合建模基于深度学习的成像特征来改善NSCLC中PD-L1表达的预测.
探索18F-FDG PET/CT作为PD-L1表达的非侵入性生物标志物的潜力.

主要方法:

对101名接受18F-FDG PET/CT扫描的NSCLC患者的回顾性分析.
使用深度学习 (3D DenseNet121) 来从PET,CT和合并PET/CT图像中提取特征.
结合提取的成像特征与临床数据 (年龄,性别,吸烟史,病变特征,SUVmax,SUV平均值,TLG) 使用类别嵌入模型共同预测PD-L1阳性 (瘤推进得分≥1%).

主要成果:

结合PET/CT成像和临床特征模型,在预测PD-L1阳性表达时,获得了0.96 ± 0.00905的接受器运行特征曲线 (AUC) 下的高面积和0.950 ± 0.0250的准确性.
单个PET和CT模型的AUC分别为0.814和0.721,单独PET/CT成像的AUC达到0.90.

结论:

18F-FDG PET/CT成像特征和临床数据的联合建模有效地预测了NSCLC中的PD-L1表达.
18F-FDG PET/CT成像显示,作为指导PNSCLC患者PD-L1免疫疗法选择的非侵入性生物标志物具有前途.

关键词:

18 F-FDG PET/CT PET/CT PET/CT PET/CT/CT PET/CT/CT 18 F-FDG PET/CT/CT 在NSCLCLC中,我们可以看到.在PD-L1中.深度学习是一种深度学习.联合建模联合建模

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