Wang Qiushi, Deng Xixiang, Huang Pan, Ma Qiang, Zhao Lianhua, Feng Yangyang, Wang Yiying, Zhao Yuan, Chen Yan, Zhong Peng, He Peng, Ma Mingrui, Feng Peng, Xiao Hualiang
Department of Pathology, Daping Hospital, Army Medical University, Chongqing, China.
The Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing, China.
Front Artif Intell. 2024 Dec 20;7:1452563. doi: 10.3389/frai.2024.1452563. eCollection 2024.
Detecting programmed death ligand 1 (PD-L1) expression based on immunohistochemical (IHC) staining is an important guide for the treatment of lung cancer with immune checkpoint inhibitors. However, this method has problems such as high staining costs, tumor heterogeneity, and subjective differences among pathologists. Therefore, the application of deep learning models to segment and quantitatively predict PD-L1 expression in digital sections of Hematoxylin and eosin (H&E) stained lung squamous cell carcinoma is of great significance.
We constructed a dataset comprising H&E-stained digital sections of lung squamous cell carcinoma and used a Transformer Unet (TransUnet) deep learning network with an encoder-decoder design to segment PD-L1 negative and positive regions and quantitatively predict the tumor cell positive score (TPS).
The results showed that the dice similarity coefficient (DSC) and intersection overunion (IoU) of deep learning for PD-L1 expression segmentation of H&E-stained digital slides of lung squamous cell carcinoma were 80 and 72%, respectively, which were better than the other seven cutting-edge segmentation models. The root mean square error (RMSE) of quantitative prediction TPS was 26.8, and the intra-group correlation coefficients with the gold standard was 0.92 (95% CI: 0.90-0.93), which was better than the consistency between the results of five pathologists and the gold standard.
The deep learning model is capable of segmenting and quantitatively predicting PD-L1 expression in H&E-stained digital sections of lung squamous cell carcinoma, which has significant implications for the application and guidance of immune checkpoint inhibitor treatments. And the link to the code is https://github.com/Baron-Huang/PD-L1-prediction-via-HE-image.
基于免疫组织化学（IHC）染色检测程序性死亡配体1（PD-L1）表达是免疫检查点抑制剂治疗肺癌的重要指导依据。然而，该方法存在染色成本高、肿瘤异质性以及病理学家之间主观差异等问题。因此，应用深度学习模型对苏木精和伊红（H&E）染色的肺鳞状细胞癌数字切片中的PD-L1表达进行分割和定量预测具有重要意义。
我们构建了一个包含H&E染色的肺鳞状细胞癌数字切片的数据集，并使用具有编码器-解码器设计的Transformer Unet（TransUnet）深度学习网络来分割PD-L1阴性和阳性区域，并定量预测肿瘤细胞阳性评分（TPS）。
结果显示，深度学习对H&E染色的肺鳞状细胞癌数字切片进行PD-L1表达分割的骰子相似系数（DSC）和交并比（IoU）分别为80%和72%，优于其他七个前沿分割模型。定量预测TPS的均方根误差（RMSE）为26.8，与金标准的组内相关系数为0.92（95%CI：0.90 - 0.93），优于五位病理学家的结果与金标准之间的一致性。
深度学习模型能够对H&E染色的肺鳞状细胞癌数字切片中的PD-L1表达进行分割和定量预测，这对免疫检查点抑制剂治疗的应用和指导具有重要意义。代码链接为https://github.com/Baron-Huang/PD-L1-prediction-via-HE-image。
