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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

    • Features Detected

      The AI generates the feature detection results, visualized on the slide image.

    • Quantification Results

      The AI generates quantified analysis result of key features within the slide image.

    • AI Score and Report

      The AI provides its analysis result in “AI Score” that reflects its prediction for immunotherapy response. The results are provided via report format.

  • 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

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-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

Breast

A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer

Byungjae Lee et al.

MICCAI 2018

Breast

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology

Kyunghyun Paeng et al.

MICCAI 2017 DLMIA Workshop

Breast

Deep Convolutional Neural Network-Based Mitosis Detection in Invasive Carcinoma of Breast by Smartphone-Based Histologic Image Acquisition

Seokhwi Kim et al.

USCAP 2016

Our solutions