BURLINGAME, Calif.--(BUSINESS WIRE)--NantHealth, Inc. (NASDAQ: NH), a next-generation, evidence-based, personalized healthcare company and NantOmics, LLC, the leader in molecular analysis, today presented a novel artificial intelligence platform for aiding pathologists in image-based lung cancer subtyping at the Society for Imaging Science and Technology’s International Symposium on Electronic Imaging 2020. This novel machine vision software platform accurately subtypes lung cancer pathology and achieves high concordance with analysis performed by trained medical pathologists.

An initial report of the AI technology was presented at the Sixth American Association for Cancer Research (AACR) and the International Association for the Study of Lung Cancer (IASLC) International Joint Conference. The study entitled, “Tumor-infiltrating lymphocytes (TILs) found elevated in lung adenocarcinomas (LUAD) using automated digital pathology masks derived from deep-learning models” concluded that despite lower overall TMB (tumor mutation burden) and lymphocyte levels, there exists a subset of lung cancers with very high infiltrating lymphocyte counts.

Derived from deep-learning models, together, the findings demonstrate a novel AI-based method for subtyping lung cancer pathologies which impacts treatment options for patients and improved methods of identifying tumor infiltrating white cells found elevated in lung cancer.
“Accurately identifying and quantifying tumor-infiltrating white cells is extremely important for prognosis and treatment decisions in this era of personalized medicine, yet it currently requires manual review of whole slide images by medically trained pathologists, and incurs significant delays and cost,” explains Dr. Patrick Soon-Shiong, MD, Chairman and CEO of NantHealth. “Our goal was to develop a scalable remote cloud-based diagnostic imaging system, a NORAD of pathology diagnosis so to speak. To accomplish this, machine vision of digitally transmitted images of tumor tissue would facilitate a scalable cloud-based infrastructure, with an image patch-based, automated system to classify cancers by their immune status.”

Non-small cell lung cancer (NSCLC) is the most common form of lung cancer, which is further classified as 40 percent adenocarcinoma (Adeno), 30 percent squamous cell carcinoma (Squamous) and the remainder, large cell carcinoma1. As analyses show that lung adenocarcinomas (LUAD) receive slightly more survival benefit from anti-PD1 therapy than squamous-cell lung carcinomas (LUSC), which have a higher TMB, a team of researchers explored whether lymphocyte distribution in the tumor microenvironment may give a rational explanation for the different responses to immuno-oncology agents independent of TMB.

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