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New Studies at ASCO 2022 Validate Effectiveness of Lunit AI as Diagnostic Aid in Cancer Treatment

May 27, 2022 — 5 min read

Lunit Meida

  • Lunit to showcase 7 poster presentations and 4 online publications, the company’s largest publication set at ASCO to date

  • Findings demonstrate the practical effectiveness of Lunit’s AI solutions in the pathological assessment of tumors across more than 16 primary cancer types



 

[SEOUL, South Korea, May 27, 2022] New findings demonstrate the clinical efficacy of AI-powered tissue analysis as a guide in cancer treatment, according to medical AI provider Lunit. The findings will be presented at the 2022 ASCO Annual Meeting, to be held from June 3 to 7. This year’s ASCO meeting will showcase the largest number of studies by Lunit, including seven poster presentations and four online publications.

 

One of the poster presentations by Lunit elaborates on the validation of the Inflamed Immune Phenotype (IIP) as a practical biomarker to guide immune checkpoint inhibitor (ICI) treatment. The IIP is assessed by Lunit SCOPE IO, Lunit’s AI-powered immune phenotype analyzer, from H&E slide images.

 

Lunit SCOPE IO analyzes a patient’s cancer tissue slide image by observing the distribution of tumor-infiltrating lymphocytes – TILs — one of the immunocytes that fight cancer cells. Based on the spatial distribution pattern of TILs and cancer cells in the tumor microenvironment, Lunit SCOPE IO identifies the tissue sample as one of three immune phenotypes: inflamed, immune-excluded, or immune-desert.



Lunit SCOPE IO ▲

 

Findings upon evaluation showed that the Inflamed Immune Phenotype (IIP) may represent a practical, clinically actionable biomarker predictive of favorable ICI treatment outcomes across more than 16 primary cancer types. This study included more than 1,800 samples paired with real-world clinical outcomes data.

 

“Patient outcomes after ICI treatment were analyzed with specific indicators including objective response rate and progression-free survival. This study is especially noteworthy in that it demonstrates the utility of the AI-assessed IIP as a biomarker across diverse cancer patient populations, including those with PD-L1 negative, MSS/TMB-low tumors, in whom predictive biomarkers are urgently needed,” said Chan-Young Ock, Chief Medical Officer at Lunit.

 

Lunit will also deliver a presentation on Lunit SCOPE PD-L1 TPS, the company’s AI-powered PD-L1 tumor proportion score (TPS) analyzer.

 

“While PD-L1 expression is the standard biomarker for advanced non-small cell lung cancer (NSCLC), manual evaluation of PD-L1 TPS by pathologists has practical limitations including interobserver variation and lengthy time demands,” said Kyunghyun Paeng, Chief Product Officer of Lunit. “Through a randomized trial, this study aimed to test the benefit of our AI-based PD-L1 TPS analyzer in assisting pathologists’ evaluation in terms of accuracy and evaluation time.”

 



Lunit SCOPE PD-L1 ▲



12 board-certified pathologists scored the PD-L1 TPS of 199 NSCLC whole-slide images in two separate intervals, with and without AI assistance, respectively. The results demonstrated the feasibility of Lunit SCOPE PD-L1 TPS to assist pathologists’ evaluation: with AI assistance, the overall accuracy of pathologists’ TPS scores increased from 79.9% to 83.2%, while the mean reading time was reduced by 30%.

 

Lunit SCOPE PD-L1 TPS recently received the CE mark, becoming the first Lunit SCOPE product to receive regulatory approval. Lunit is currently conducting a multi-center pivotal clinical trial in the U.S., following the results of this study.

 

The company’s other scheduled poster presentations include a clinical trial demonstrating the accuracy of Lunit’s AI imaging solution in detecting high-risk breast cancer patients, as well as a study assessing the clinical efficacy of Lunit SCOPE IO in the prediction of response to neoadjuvant chemotherapy in triple-negative breast cancer patients.

 

“Lunit has been presenting groundbreaking findings at ASCO every year since 2019, and we are proud to showcase our largest set of research yet with 11 abstracts,” said Brandon Suh, CEO of Lunit. “As our AI biomarker platform continues to gain validation and recognition, we are expanding our early research and commercial access programs for Lunit SCOPE throughout the year.”



<ASCO 2022 루닛 단독 및 협업 연구 초록 정보>


  1. #2621 . 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. (Poster)

  2. #8529. 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. (Poster)

  3. #595. 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). (Poster)

  4. #2570. Artificial intelligence-powered pathology image analysis merged with spatial transcriptomics reveals distinct TIGIT expression in the immune-excluded tumor-infiltrating lymphocytes. (Poster)

  5. #4096. 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). (Poster)

  6. #10533. Robust artificial intelligence-powered imaging biomarker based on mammography for risk prediction of breast cancer. (Poster)

  7. #2663. Tumor-infiltrating lymphocyte enrichment predicted by CT radiomic analysis is associated with clinical outcomes of immune checkpoint inhibitor in non–small cell lung cancer. (Poster)

  8. #e12543. Artificial intelligence-powered human epidermal growth factor receptor 2 (HER2) analyzer in breast cancer as an assistance tool for pathologists to reduce interobserver variation. (Online Publication Only)

  9. #e16214. Artificial intelligence-powered whole-slide image analyzer reveals a distinctive distribution of tumor-infiltrating lymphocytes in neuroendocrine tumors and carcinomas. (Online Publication Only)

  10. #e14557. Safety and efficacy of YBL-006, an anti-PD-1 monoclonal antibody in advanced solid tumors: A phase I study. (Online Publication Only)

  11. #e24001. Need of pretreatment support of breast cancer patient caregivers compared to patients. (Online Publication Only)






AbstractASCObiomarkerCancerLunit SCOPEMedicalOncologyTreatment

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