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Abstract : AI-powered whole-slide image analysis of tumor-infiltrating lymphocytes for prediction of prognosis in colorectal cancer

C. Park et al. — ESMO 2021



The degree of T-cell infiltration has been suggested as an important prognostic biomarker for colorectal cancer (CRC) patients, regardless of other clinical and/or pathological factors. In this study, we analyzed tumor-infiltrating lymphocyte (TIL) counts of CRC using Lunit SCOPE IO, an artificial intelligence (AI)-powered whole slide image (WSI) software analyzer. Our aim was to analyze the prognostic significance of AI-powered TIL analysis in CRC.


Lunit SCOPE IO was trained and validated with a 2.8 x 109 micrometer2 area and 5.9 x 106 TILs from 3,166 H&E Whole-Slide Images (WSI) of multiple cancer types, annotated by 52 board-certified pathologists. The Inflamed Score (IS) was defined as the proportion of all tumor-containing 1 mm2-size tiles within a WSI classified as being of the inflamed immune phenotype (high TIL density within cancer epithelium). H&E images, sequencing data and survival data of stage I-III CRC patients from The Cancer Genome Atlas (TCGA) were utilized for this analysis.


Stage I-III CRC samples (n = 461) with clinical data were analyzed. The median of IS was 8.56 (IQR 3.74-18.39). IS showed moderate positive correlations with CD8A (rs = 0.422, p < 0.001) and CD3G (rs = 0.377, p < 0.001) expression levels but weaker positive correlations with regulatory T cells (rs = 0.162, p < 0.001), TH1 (rs = 0.209, p < 0.001) or TH2 cell proportions (rs = 0.128, p = 0.006). The IS was higher in CMS1 group compared to CMS 2-4 groups (median 18.49 vs. 6.90, p < 0.001). No significant differences in IS was observed across TNM stages. The recurrence-free survival of the patients with IS higher than third quartile (>= 18.39) were significantly longer compared to the lower group (p = 0.034, HR 0.540, 95% CI 0.306-0.954). The same outcome was observed in cases with MSS tumors (p = 0.023, HR 0.380, 95% CI 0.165-0.877).


AI-powered analysis of WSI can provide prognostic information in stage I-III CRC patients. Further development of AI-powered TIL analysis including the spatial

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C. Park1, Y. Lim2, S. Song2, S. Ahn2, J. Ryu2, H. Song2, M. Ma2, S. Park2, S. Pereira2, B.J. Aum2, S. Shin2, S. Cho2, K. Paeng2, D. Yoo2, W. Jung2, C. Ock2

1 Internal Medicine, Seoul National University Hospital, 03080 - Seoul/KR

2 Oncology Group, Lunit Inc., 06241 - Seoul/KR

ESMO 2021

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