Abstract P4-05-07: Assistance with an artificial intelligence-powered tumor infiltrating lymphocytes (TIL) analyzer reduces interobserver variation in pathologic scoring of TIL in breast cancer
Authors: Soo Ick Cho, Wonkyung Jung, Sangjoon Choi, Seokhwi Kim, Sanghoon Song, Gahee Park, Minuk Ma, Seonwook Park, Sergio Pereira, Sangheon Ahn, Brian Jaehong Aum, Seunghwan Shin, Kyunghyun Paeng, Donggeun Yoo, Chan-Young Ock
Journal: Cancer Research (2022)

Abstract Background Tumor infiltrating lymphocytes (TIL) is a promising prognostic marker in breast cancer. However, TIL is manually scored by pathologists, thus laborious work is required and interobserver heterogeneity exists in the results. In this study, we aimed to evaluate the clinical utility of an artificial intelligence (AI)-powered TIL analyzer in terms of reducing the interobserver variation. Methods Lunit SCOPE IO, AI-powered TIL analyzer was trained and validated with a 2.8 x 109 micrometer2 area and 5.9 x 106 TIL from 3,166 H&amp;E Whole-Slide Images (WSI) of multiple cancer types including breast cancer, annotated by 52 board-certified pathologists. Three independent board-certified pathologists scored TIL% of H&amp;E slides of breast cancer from an external cohort (N = 199). TIL% was calculated referenced on the guideline of Immuno-Oncology Biomarker Working Group on Breast Cancer. For the cases of TIL score difference between each pathologist and AI model more than 15%, the pathologists were asked to revise TIL% in assistance with AI model which displays both stromal area and TIL. Finally, we compared the interobserver variation based on intraclass correlation coefficients (ICC) before and after AI assistance. Results The distribution of TIL score by 3 pathologists was 7% (5-20%), 15% (5-50%), and 20% (10-40%), respectively [median (25%-75% quantile)]. The ICC value of the initial TIL score evaluation was 0.716 (95% confidence interval, 0.560-0.811). Afterward, pathologists revised their initial scoring with assistance of AI model for the cases of difference more than 15% (n = 19, 72, and 73, respectively for each pathologist). After rescoring, number of slides with 15% or more difference of TIL% between raters significantly decreased from 109 slides (54.8%) to 75 slides (37.7%, p &amp;lt; 0.001). The ICC value after re-scoring TIL% was 0.831 (95% confidence interval, 0.725-0.890). Conclusions There was a notable interobserver variation to score TIL% in breast cancer. Assistance with AI-powered TIL analyzer substantially improved the pathologist’s consensus and could be regarded as one of references for the final labeling of TIL%. Citation Format: Soo Ick Cho, Wonkyung Jung, Sangjoon Choi, Seokhwi Kim, Sanghoon Song, Gahee Park, Minuk Ma, Seonwook Park, Sergio Pereira, Sangheon Ahn, Brian Jaehong Aum, Seunghwan Shin, Kyunghyun Paeng, Donggeun Yoo, Chan-Young Ock. Assistance with an artificial intelligence-powered tumor infiltrating lymphocytes (TIL) analyzer reduces interobserver variation in pathologic scoring of TIL in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-05-07.