Pathologic validation of artificial intelligence-powered prediction of combined positive score of PD-L1 immunohistochemistry in urothelial carcinoma.
Authors: 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
Journal: Journal of Clinical Oncology (2021)

e16518 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 &lt; 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.