Pioneer and Conquer
analysis of tissue slides using
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.
- Multiplex IHC
How It Works
Enabling Better Immunotherapy
analysis of tissue
Detects cancer stroma, cancer epithelium, lymphocytes, and etc.
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
Tissue Slide (FFPE)
Pre-treatment & H&E staining
Digitization using a scanner
Supports various scanner vendors
File upload to server
(both cloud or on-prem)
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
Lunit SCOPE IO
Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.
Abstract : Pathologic validation of artificial intelligence-powered prediction of combined positive score of PD-L1 immunohistochemistry in urothelial carcinoma.
Abstract: Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.
Abstract : Deep learning based radiomic biomarker for predicting the presence of high-grade histologic patterns in lung adenocarcinoma
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...
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
Abstract: Deep learning-based immune phenotype analysis reveals distinct resistance pattern of immune checkpoint inhibitor in non-small cell lung cancer
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...
Abstract: Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images
Abstract: Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer
Abstract: Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma
Abstract : Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer
Abstract : Clinical performance of artificial intelligence-powered annotation of tumor cell PD-L1 expression for treatment of immune-checkpoint inhibitor (ICI) ...
Abstract: Distinct subset of immune cells assessed by multiplex immunohistochemistry correlates with immune phenotype classified by ...