Background : Programmed death ligand 1 (PD-L1) expression is a reliable biomarker of immune-checkpoint inhibitors (ICI) in multiple cancer types including urothelial carcinoma (UC). A 22C3 pharmDx immunohistochemistry was particularly determined by using the combined positive score (CPS) in UC. A challenging issue regarding the manual scoring of CPS by a pathologist is in determining the representative area to read. This requires substantial time and effort and may lead to inter-observer variation. We developed an artificial intelligence (AI)-powered CPS analyzer, to assess CPS in whole-slide images (WSI) and validated its performance by comparing against a consensus of pathologists’ readings.
Methods : An AI-powered CPS analyzer, Lunit SCOPE PD-L1, has been trained and validated based on a total of 3,326,402 tumor cells, lymphocytes, and macrophages annotated by board-certified pathologists for PD-L1 positivity in 1200 WSI stained by 22C3. After excluding the in-house control tissue regions, the WSIs were divided into patches, from which a deep learning-based model was trained to detects the location and PD-L1 positivity of tumor cells, lymphocytes, and macrophages, respectively. Finally, the patch-level cell predictions were aggregated for CPS estimation. The performance of the model was validated on an external validation UC cohort consisting of two institutions: Boramae Medical Center (BMC, n = 93) and Seoul National University Bundang Hospital (SNUBH, n = 100). Three uropathologists independently annotated the CPS of the external validation cohorts, and a consensus of CPS was determined by determination of their mean values.
Results : The AI-model predicts CPS accurately in an internal validation cohort as the area under the curves (AUC) values to predict PD-L1-positive tumor cell, PD-L1-positive lymphocytes or macrophages, PD-L1-negative tumor cell, and PD-L1-negative lymphocytes or macrophages were 0.929, 0.855, 0.885, and 0.872, respectively. There was a significant positive correlation between CPS by AI-model and consensus CPS by 3 pathologists in the external validation cohort (Spearman coefficient = 0.914, P< 0.001). Concordance of AI-model and pathologists' consensus to call CPS ≥ 10 was 88.1%, which was similar to that of either 2 of 3 pathologists (84.5%, 86.5%, and 90.7%). The concordance rate was not significantly different according to data source (BMC: 88.2% versus SNUBH: 88.0%, P = 1.00), but was significantly different according to type of surgery [surgical resection (cystectomy, nephrectomy, and ureterectomy): 92.3% versus transurethral resection: 81.3%, P = 0.0244].
Conclusions : Lunit SCOPE PD-L1, AI-powered CPS analyzer, can detect PD-L1 expression in tumor cells, lymphocytes or macrophages highly accurately compared to uropathologists.
Jeong Hwan Park, Kyu Sang Lee, Euno Choi, Wonkyung Jung, Jaehong Aum, Sergio Pereira, Seonwook Park, Minuk Ma, Seungje Lee, Eunji Baek, Eun-Jihn Roh, Seunghwan Shin, Kyunghyun Paeng, Donggeun Yoo, Chan-Young Ock
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