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LunitSCOPE IO

AI-Powered Biomarker
for Immuno-Oncology

Online Demo

  • 94%

    Demonstrated to perform with
    94% accuracy for pan-cancer
    tissue slide image analysis.

  • 1 Giga pixel

    Size of one tissue slide image,
    digitized for AI analysis.
    (c.f. An HD video is about 1,280 pixels)

  • 15+

    Trained to cover pan-cancer tissue data,
    including lung, breast,
    colorectal cancer and more.

  • Background
  • Our Vision
  • How It Works
BackgroundOur VisionHow It Works
Online Demo

Background

What if
AI can predict
cancer treatment outcomes?

  • Predicting immunotherapy outcome with AI analysis

    Lunit lab in Samsung Medical Center, Seoul, Korea

  • Finding more responders to
    cancer treatment

  • Tissue Data is
    huge and complex

  • Complex is
    what AI does best

Our Vision

Pioneer and Conquer
"Tissue Phenomics"

  • Accurate
    analysis of tissue slides using
    deep learning
    technology.

  • Lunit is one of the few companies with high-level tissue data analysis technology. We provide not only detection of features, but link it to clinical outcomes, developing biomarkers specific to drugs.

    • H&E
    • IHC
    • Multiplex IHC

How It Works

Enabling Better Immunotherapy
based on
Tissue Phenomics

  • Accurate
    analysis of tissue
    slides using
    deep learning
    technology.

    Detects cancer stroma, cancer epithelium, lymphocytes, and etc.

  • Generated by
    Lunit SCOPE IO

    • Detection & Quantification

      Detects intratumoral TILs and stromal TILs from whole slide images.

    • 3 Immune Phenotype Map Generation

      Classifies three immune phenotypes—inflamed, excluded, desert.

    • AI Score & Cutoff

      Calculates AI biomarker score based on the phenotype map. If the score is higher than the cut-off value, it indicates that the patient would be more likely to respond to immunotherapy.

  • The solution covers
    all major types of cancer including

  • How Our AI Works

    • STEP 1

      Tissue Slide (FFPE)
      Pre-treatment & H&E staining

    • STEP 2

      Digitization using a scanner
      Supports various scanner vendors

    • STEP 3

      File upload to server
      (both cloud or on-prem)

    • STEP 4

      Lunit SCOPE IO analysis

  • Lunit SCOPE IO

    • Detection results for visualization
    • Quantification results of key features detected
    • Final score for response prediction
    • Turnaround time: <10 min

    *Lunit SCOPE IO is currently available for
    research use only

  • Presented in

Studies featuring
Lunit SCOPE IO

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

Oncology

Abstracts : 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

Abstracts : 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: 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

Abstracts : 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: 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

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

Liron Pantanowitz et al.

Diagnostic Pathology (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

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: 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 adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer

Soo Youn Cho 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

Onology

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

Mitko Veta et al.

Medical Image Analysis (2019)

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

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

Abstracts : 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

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

Yoon-La Choi et al.

ASCO 2021

Our solutions