Derived from deep-learning models, the findings
demonstrate novel AI methods of identifying tumor-infiltrating
lymphocytes (TILs) in lung cancers
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.
“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.”
Study Design:
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”
Authors: Mustafa I. Jaber, Christopher W. Szeto, Bing
Song, Liudmila Beziaeva, Stephen Benz, Patrick Soon-Shiong, and
Shahrooz Rabizadeh
Session and Number: Image Processing: Algorithms and
Systems XVIII (IPAS-064)
Location: Hyatt Regency San Francisco Airport,
Burlingame, CA
Date and Time: January 27, 2020 at 4:10 PM
About NantOmics
NantOmics, a member of the NantWorks ecosystem of companies,
delivers molecular diagnostic capabilities with the intent of
providing actionable intelligence and molecularly driven decision
support for cancer patients and their providers at the point of
care. NantOmics is the first molecular diagnostics company to
pioneer an integrated approach to unearthing the genomic and
proteomic variances that initiate and drive cancer, by analyzing
both normal and tumor cells from the same patient and following
identified variances through from DNA to RNA to protein to drug.
NantOmics has a highly scalable cloud-based infrastructure capable
of storing and processing thousands of genomes a day, computing
genomic variances in near real-time, and correlating proteomic
pathway analysis with quantitative multi-plexed protein expression
analysis from the same micro-dissected tumor sample used for
genomic analysis. For more information please visit
www.nantomics.com and follow Dr. Soon-Shiong on Twitter
@DrPatSoonShiong.
About NantHealth:
NantHealth, a member of the NantWorks ecosystem of companies,
provides leading solutions across the continuum of care for
physicians, payors, patients and biopharmaceutical organizations.
NantHealth enables the use of cutting-edge data and technology
toward the goals of empowering clinical decision support and
improving patient outcomes. NantHealth’s comprehensive product
portfolio combines the latest technology in payor/provider
platforms that exchange information in near-real time (NaviNet and
Eviti), connected care solutions that deliver medical device
interoperability (DCX device connectivity platform and VCX patient
vitals software) and molecular profiling services that combine
comprehensive DNA & RNA tumor-normal profiling with
pharmacogenomics analysis (GPS Cancer®). For more information,
please visit www.nanthealth.com or follow us on Twitter, Facebook
and LinkedIn.
Forward-Looking Statements: NantHealth
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 Securities
and Exchange Commission.
1 M Jaber, C. Szeto, B. Song, L. Beziaeva, S. Benz, P.
Soon-Shiong and S. Rabizadeh, “Pathology image-based lung cancer
subtyping using deep-learning features and cell-density maps,” To
be published as part of the proceedings from the IS&T
International Symposium on Electronic Imaging 2020.
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version on businesswire.com: https://www.businesswire.com/news/home/20200127005739/en/
NANT Jen Hodson Jen@nant.com 562-397-3639
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