A new AI-assisted scoring system for PD-L1 expression in NSCLC. (June 2022)

• Record Type: Journal Article
• Authors: Huang, Ziling; Chen, Lijun; Lv, Lei; Fu, Chi-Cheng; Jin, Yan; Zheng, Qiang; Wang, Boyang; Ye, Qiuyi; Fang, Qu; Li, Yuan
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: Artificial intelligence -- Deep learning -- Digital pathology -- Non-small cell lung cancer -- Programmed cell death ligand-1; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106829
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Predictions of Aitrox were similar to those of experienced pathologists, rs = 0.87. Accuracy of classification was 79.13% in Aitrox, comparable with the pathologists. In the negative TPS subset, Aitrox showed the highest accuracy as 85.29%. In the low TPS subset, Aitrox showed similar accuracy to experienced pathologists. However, in the high TPS subset, AI showed the lowest accuracy (72.73%). Abstract: Background: 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). Methods: 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. 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. Conclusion: The Aitrox AI Model demonstrates potential in assisting routine diagnosis of NSCLC by pathologists through scoring of PD-L1 expression.