Programmed death ligand 1 (PD-L1) expression level is a prognostic marker in predicting response to immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (NSCLC). However, manual evaluation of the PD-L1 tumor proportion score (TPS) often leads to inter-observer variation. Whether artificial intelligence (AI) assistance could improve the accurate TPS reading and hence lead to better prediction of the ICI response have not been studied yet.
An AI-powered analyzer, LUNIT SCOPE PD-L1 TPS was developed with a total of 393,565 tumor cells from 802 wholeslide images (WSIs) of NSCLC, annotated by board-certified pathologists. Three independent pathologists scored PD-L1 TPS in an external cohort of 479 patients. They then had chances to revise the initial TPS groups (<1%, 1%–49%, and ≥50%) with AI assistance. They independently reviewed cases that the TPS group had been changed after AI model assistance despite the initial concordance among them (human misinterpretation) and cases that the group had not been changed despite the AI suggestion of other TPS groups (AI misinterpretation). The effect on clinical outcome of AI-assisted revised TPS group was also analyzed in 430 patients treated with second or later ICIs.
Initially, the three pathologists concordantly scored TPS in 81.4% of cases. Following the revision of their score by adopting AI’s suggestion of TPS group other than their baseline value (N = 91, 93, and 107 for each pathologist), the overall concordance rate was increased to 90.2% (P < 0.001). The potential human misinterpretation cases include simple under- /overestimation (74.2%), difficulty due to artifacts (12.9%), and difficulty due to cellular morphology (12.9%) (Figure 1). The potential AI misinterpretation cases include false-negative readings of PD-L1-negative normal epithelium (66.7%), false-negative readings of PD-L1-positive tumor cell (19.0%), and false-positive readings of PD-L1-positive macrophage (14.3%). The revised TPS group with AI assistance predicted objective response rate, progression-free survival, and overall survival better than the baseline group without AI assistance.
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Seokhwi Kim1, Sangjoon Choi2, Soo Ick Cho3, Hyojin Kim4, Minuk Ma5, Sergio Pereira6, Seonwook Park6, Brian Aum6, Seunghwan Shin6, Kyunghyun Paeng6, Donggeun Yoo6, Wonkyung Jung5, Chan-Young Ock6, Se-Hoon Lee7, Jin-Haeng Chung8, Yuna Choi9
1Ajou University School of Medicine, Suwon, South Korea,
2Samsung Medical Center, Seoul, South Korea,
3Lunit Inc., Gang nam gu, South Korea,
4Seoul National University Bundang Hospital, Seongnam, South Korea,
5Lunit Inc., Gangnamgu, South Korea,
6Lunit Inc., Seoul, South Korea,
7Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea,
8Seoul National University Bundang Hospital, Seoul, South Korea,
9Samsung Medical Center, South Korea
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