NantHealth and NantOmics Reveal a Novel AI Based Machine-Learning Digital Pathology Software for Lung Cancer by Identifying Tumor Infiltrating Killer Cells From Whole Slide Images
Derived from deep-learning models, the findings demonstrate novel AI methods of identifying tumor-infiltrating lymphocytes (TILs) in lung cancers
An initial report of the AI technology was presented at the
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.
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.
“By focusing on classifying regions detected as tumorous, we achieved identification of adenocarcinomas versus squamous cell carcinomas in non-small-cell lung cancers with an approximate accuracy rate of 86 percent,” explained Soon-Shiong. “With highly accurate tumor-region and lymphocyte detection, oncologists may better treat their patients with adeno versus squamous-based therapies and the use of immunotherapies may result in better outcomes.”
The system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic whole slide images (WSI) from 805 patients obtained from The Cancer Genome Atlas (TCGA) sources. Samples were randomly split into training (711 WSIs from 664 patients) and testing (165 WSIs from 141 patients) sets.
Findings show that NantOmics and NantHealth’s fully-automated histopathology subtyping AI method outperforms other algorithms reported in literature for diagnostic WSIs. The system also generated maps of (tumor) regions-of-interest within WSIs, providing novel spatial information on tumor organization.
Details of the oral presentation at the IS&T International Symposium on Electronic Imaging 2020 outlined below:
Title: “Pathology image-based lung cancer subtyping using deep-learning features and cell-density maps”
Session and Number: Image Processing: Algorithms and Systems XVIII (IPAS-064)
Date and Time:
NantOmics, a member of the
This news release contains certain statements of a forward-looking nature relating to future events or future business performance. Forward-looking statements can be identified by the words “expects,” “anticipates,” “believes,” “intends,” “estimates,” “plans,” “will,” “outlook” and similar expressions. Forward-looking statements are based on management’s current plans, estimates, assumptions and projections, and speak only as of the date they are made. Risks and uncertainties include, but are not limited to: our ability to successfully integrate a complex learning system to address a wide range of healthcare issues; our ability to successfully amass the requisite data to achieve maximum network effects; appropriately allocating financial and human resources across a broad array of product and service offerings; raising additional capital as necessary to fund our operations; achieving significant commercial market acceptance for our sequencing and molecular analysis solutions; establish relationships with, key thought leaders or payers’ key decision makers in order to establish GPS Cancer as a standard of care for patients with cancer; our ability to grow the market for our Systems Infrastructure, and applications; successfully enhancing our Systems Infrastructure and applications to achieve market acceptance and keep pace with technological developments; customer concentration; competition; security breaches; bandwidth limitations; our ability to continue our relationship with NantOmics; our ability to obtain regulatory approvals; dependence upon senior management; the need to comply with and meet applicable laws and regulations; unexpected adverse events; clinical adoption and market acceptance of GPS Cancer; and anticipated cost savings. We undertake no obligation to update any forward-looking statement in light of new information or future events, except as otherwise required by law. Forward-looking statements involve inherent risks and uncertainties, most of which are difficult to predict and are generally beyond our control. Actual results or outcomes may differ materially from those implied by the forward-looking statements as a result of the impact of a number of factors, many of which are discussed in more detail in our reports filed with the
1 M Jaber, C. Szeto, B. Song, L. Beziaeva, S. Benz,