Lunit
제품 문의하기
Back to List

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

C. Park et al. — ESMO (2021)
twitterlinkedinfacebook

Abstract


Background

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.


Methods

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.


Results

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).


Conclusions

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

전체 내용 보기
AUTHORS

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

PUBLISHED
ESMO (2021)

Read more

Oncology

Abstract: Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer

Soo Youn Cho et al.

AACR 2019

Oncology

Abstract: Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images

Kyunghyun Paeng et al.

AACR 2019

Oncology

Abstract: Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer

Sehhoon Park et al.

ASCO 2019

Oncology

Abstract: Comprehensive deep learning analysis of H&E tissue phenomics reveals distinct immune landscape and transcriptomic enrichment profile among immune inflamed, excluded and desert subtypes...

Jonghanne Park et al.

AACR 2020

Oncology

Abstract: Deep-learning analysis of H&E images to define three immune phenotypes to reveal loss-of-target in excluded immune cells as a novel resistance mechanism of immune checkpoint inhibitor...

Sehhoon Park et al.

ASCO 2020

Oncology

Abstract: Deep learning-based immune phenotype analysis reveals distinct resistance pattern of immune checkpoint inhibitor in non-small cell lung cancer

Chan-Young Ock et al.

ASCO 2020

AI

Reducing Domain Gap by Reducing Style Bias

Hyeonseob Nam et al.

CVPR (2021)

Onology

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge

Mitko Veta et al.

Medical Image Analysis (2019)

Oncology

Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses

Liron Pantanowitz et al.

Diagnostic Pathology (2020)

Oncology

Abstract: Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.

Chan-Young Ock et al.

ASCO (2021)

Oncology

Abstract: Distinct subset of immune cells assessed by multiplex immunohistochemistry correlates with immune phenotype classified by ...

Yoon-La Choi et al.

ASCO 2021

Oncology

Abstract : Pathologic validation of artificial intelligence-powered prediction of combined positive score of PD-L1 immunohistochemistry in urothelial carcinoma.

Jeong Hwan Park et al.

ASCO 2021

Oncology

Abstract : Clinical performance of artificial intelligence-powered annotation of tumor cell PD-L1 expression for treatment of immune-checkpoint inhibitor (ICI) ...

Hyojin Kim et al.

ASCO 2021

Oncology

Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.

Jeanne Shen et al.

ASCO (2021)

Oncology

Abstract : Deep learning based radiomic biomarker for predicting the presence of high-grade histologic patterns in lung adenocarcinoma

Yeonu Choi et al.

AACR 2021

Oncology

Abstract : Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer

Hyojin Kim et al.

AACR 2021

Oncology

Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma

Chan-Young Ock et al.

AACR (2021)

Oncology

Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients

Soo Youn Cho et al.

Scientific Reports (2021)

Oncolgoy

Abstract : Interim results of phase I dose escalation study of YBL-006: A novel anti-PD-1 monoclonal antibody in advanced solid tumors

K. Lee et al.

ESMO (2021)

Oncology

Abstract : Assistance with an artificial intelligence-powered PD-L1 analyzer reduces interobserver variation in pathologic reading of tumor proportion score in non-small cell lung cancer

S. Choi et al.

ESMO (2021)

Oncology

Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma

Yeonu Choi et al.

Cancers (2021)

Oncology

Artificial Intelligence–Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non–Small-Cell Lung Cancer

Sehhoon Park et al.

Journal of Clinical Oncology (2022)

Oncology

Artificial Intelligence–Powered Hematoxylin and Eosin Analyzer Reveals Distinct Immunologic and Mutational Profiles among Immune Phenotypes in Non–Small-Cell Lung Cancer

Jonghanne Park et al.

The American Journal of Pathology(2022)

Oncology

Abstract : Artificial Intelligence-Powered Analyzer Reduces Inter-observer Variation in PD-L1 Tumor Proportion Score of Non-Small Cell Lung Cancer

Seokhwi Kim et al.

USCAP (2022)

Oncology

Abstract : Artificial Intelligence-Powered Tumor Purity Assessment From H&E Whole Slide Images Correlates...

Gahee Park et al.

USCAP (2022)

Oncology

Abstract : Deep learning-based H&E analyzer reveals distinct immune profiles and clinical outcomes among immune phenotypes in uterine corpus endometrial carcinoma

Horyun Choi et al.

AACR (2022)

Oncology

Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals immune-excluded phenotype is correlated with TGF-beta pathway related genomic aberrations

Gahee Park, et al.

AACR (2022)

Oncology

Observer Performance Study to Examine the Feasibility of the AI-powered PD-L1 Analyzer to Assist Pathologists’ Assessment of PD-L1 Expression Using Tumor Proportion Score in Non-Small Cell Lung Cancer

Seokhwi Kim et al.

ASCO(2022)

Oncology

Artificial intelligence-powered human epidermal growth factor receptor 2 (HER2) analyzer in breast cancer as an assistance tool for pathologists to reduce interobserver variation

Minsun Jung et al.

ASCO(2022)

Oncology

Artificial intelligence-powered whole-slide image analyzer reveals a distinctive distribution of tumor-infiltrating lymphocytes in neuroendocrine tumors and carcinomas

Hyung-Gyo Cho et al.

ASCO(2022)

Oncology

Artificial Intelligence (AI) - powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of response to neoadjuvant chemotherapy (NAC) in triple negative breast cancer (TNBC)

Hee Jin Lee et al.

ASCO(2022)

Oncology

Artificial intelligence (AI)-powered pathology image analysis merged with spatial transcriptomics reveals distinct TIGIT expression in the immune-excluded tumor-infiltrating lymphocytes

Gahee Park et al.

ASCO(2022)

Oncology

Trastuzumab plus FOLFOX for Gemcitabine/Cisplatin refractory HER2-positive biliary tract cancer: a multi-institutional phase II trial of the Korean Cancer Study Group (KCSG-HB19-14)

Choong-kun Lee et al.

ASCO(2022)

Oncology

The Inflamed Immune Phenotype (IIP): a clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across >16 primary tumor types

Jeanne Shen et al.

ASCO(2022)

Oncology

Safety and efficacy of YBL-006, an anti-PD-1 monoclonal antibody in advanced solid tumors: a phase I study

Do-Youn Oh et al.

ASCO(2022)

Oncology

Robust artificial intelligence-powered imaging biomarker based on mammography for risk prediction of breast cancer.

Eun Kyung Park et al.

ASCO(2022)