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LunitSCOPE PD-L1

AI Solution for PD-L1 Expression

Online Demo

  • 50 %

    Finds 50% more patients eligible for
    immunotherapy from the human-classified
    PD-L1(-) patient group.

  • 1M+

    Trained with large-scale training
    set including 1M+ cell annotations by 150+
    pathologists worldwide.

  • 96 %

    Demonstrated to perform TPS
    classification (above or below 50) with
    96% accuracy.

  • Lunit SCOPE PD-L1 TPS has
    received CE Mark.

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

Background

What if AI can find
more patients
eligible for
immunotherapy?

  • Quantifying
    PD-L1 positivity
    with AI analysis

    Lunit lab in Samsung Medical Center, Seoul, Korea

  • Finding more PD-L1
    positive patients

  • Counting every PD-L1
    positive tumor cell

  • Complex is what
    AI does best

Our Vision

Accurate
Quantification of
PD-L1
Expression Level
With AI

  • Trained on large
    datasets reviewed
    by 150+
    pathologists

    Lunit SCOPE PD-L1 analyzes every PD-L1 positive tumor cell and generates analysis results close to the ground truth

  • How AI Improves
    PD-L1 Assessment

    AS-IS (Only Human)

    • Discrepancy between pathologists
    • Non-reproducible

    TO-BE (With AI)

    • Objective result
    • Reproducible

How It Works

Enabling Better Immunotherapy
based on AI-powered PD-L1 Analysis

  • Objective
    analysis of PD-L1
    positivity using
    deep learning
    technology.

    * Lunit SCOPE PD-L1 is currently applicable to NSCLC (non-small cell lung cancer) only.

  • Generated by
    Lunit SCOPE PD-L1

    • Detection

      Detects tumor cell PD-L1 +/- and inflammatory cell PD-L1 +/- from whole slide images.

    • Quantification

      Calculates PD-L1 positive score including TPS (Tumor Proportion Score) and CPS (Combined Positive Score).

    • AI Score and Report

      Provides AI analysis results including PD-L1 TPS score and map within a report.

  • Improving PD-L1 test efficiency through
    artificial intelligence

  • How Our AI Works

    • STEP 1

      Tissue slide (FFPE)
      slicing & IHC 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 PD-L1 analysis

  • Lunit SCOPE PD-L1

    • Detection results for visualization
    • Quantification results of key features detected
    • Final score for response prediction

    *Lunit SCOPE PD-L1 is currently available for
    research use only

Studies featuring
Lunit SCOPE PD-L1

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)

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 : 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

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: 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 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

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

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

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

Yoon-La Choi et al.

ASCO 2021

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