Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Shanghai Aitrox Technology Corporation Limited, Shanghai, China.
Comput Methods Programs Biomed. 2022 Jun;221:106829. doi: 10.1016/j.cmpb.2022.106829. Epub 2022 Apr 23.
Artificial intelligence (AI) analysis may serve as a scoring tool for programmed cell death ligand-1 (PD-L1) expression. In this study, a new AI-assisted scoring system for pathologists was tested for PD-L1 expression assessment in non-small cell lung cancer (NSCLC).
PD-L1 expression was evaluated using the tumor proportion score (TPS) categorized into three levels: negative (TPS < 1%), low expression (TPS 1-49%), and high expression (TPS ≥ 50%). In order to train, validate, and test the Aitrox AI segmentation model at the whole slide image (WSI) level, 54, 53, and 115 cases were used as training, validation, and test datasets, respectively. TPS reading results from five experienced pathologists, six inexperienced and the Aitrox AI model were analyzed on 115 PD-L1 stained WSIs. The Gold Standard for TPS was derived from the review of three expert pathologists. Spearman's correlation coefficient was calculated and compared between the results.
Aitrox AI Model correlated strongly with the TPS Gold Standard and was comparable with the results of three of the five experienced pathologists. In contrast, the results of four of the six inexperienced pathologists correlated only moderately with the TPS Gold Standard. Aitrox AI Model performed better than the inexperienced pathologists and was comparable to experienced pathologists in both negative and low TPS groups. Despite the fact that the low TPS group showed 5.09% of cases with large fluctuations, the Aitrox AI Model still showed a higher correlation than the inexperienced pathologists. However, the AI model showed unsatisfactory performance in the high TPS groups, especially lower values than the Gold Standard in images with large regions of false-positive cells.
The Aitrox AI Model demonstrates potential in assisting routine diagnosis of NSCLC by pathologists through scoring of PD-L1 expression.
人工智能 (AI) 分析可作为程序性死亡配体-1 (PD-L1) 表达的评分工具。本研究旨在测试一种新的 AI 辅助病理学家评分系统，用于评估非小细胞肺癌 (NSCLC) 中的 PD-L1 表达。
采用肿瘤比例评分 (TPS) 评估 PD-L1 表达，分为三个水平：阴性 (TPS<1%)、低表达 (TPS 1-49%) 和高表达 (TPS≥50%)。为了在全切片图像 (WSI) 水平上训练、验证和测试 Aitrox AI 分割模型，分别使用 54、53 和 115 例作为训练、验证和测试数据集。对 115 例 PD-L1 染色 WSI 进行了 5 名经验丰富的病理学家、6 名无经验的病理学家和 Aitrox AI 模型的 TPS 读数分析。TPS 的金标准来自三位专家病理学家的审查。计算并比较了结果之间的 Spearman 相关系数。
Aitrox AI 模型与 TPS 金标准高度相关，与五名经验丰富病理学家中的三名结果相当。相比之下，六名无经验病理学家中的四名结果与 TPS 金标准仅中度相关。Aitrox AI 模型在阴性和低 TPS 组中的表现优于无经验病理学家，与经验丰富的病理学家相当。尽管低 TPS 组有 5.09%的病例波动较大，但 AI 模型的相关性仍高于无经验的病理学家。然而，该 AI 模型在高 TPS 组中的表现并不理想，尤其是在假阳性细胞区域较大的图像中，其值低于金标准。
Aitrox AI 模型通过对 PD-L1 表达进行评分，有望辅助病理学家对 NSCLC 进行常规诊断。
