Combination of Deep-Learning Models Provides Significant Risk-Assessment of Survival in Pancreatic Cancer Patients, Holding P...
13 October 2020 - 11:00PM
Business Wire
NantHealth, ImmunityBio and NantOmics Presented
Results at the 2020 AACR Virtual Special Conference: Pancreatic
Cancer
NantHealth, Inc. (NASDAQ: NH), a next-generation,
evidence-based, personalized healthcare company, presented
virtually with NantOmics and ImmunityBio on September 29-30 2020, a
session entitled “Deep-learning image-based tumor, stroma and
lymphocytes spatial relationships and clinical features that affect
survival in pancreatic cancer patients,” at the American
Association for Cancer Research (AACR) virtual special conference
on pancreatic cancer. This Nant technology is an example of how
digital pathology solutions may support the treatment of
cancer.
Prepared in collaboration with NantOmics, ImmunityBio, and the University of Colorado
School of Medicine, the presentation examined differential survival
in pancreatic cancer patients via stromal and lymphocyte density.
In this study, the contributing researchers developed an automated
deep-learning system to provide risk assessment upon spatial
relationships between tumor, stroma, and lymphocyte regions in
pancreatic pathology images from 82 pancreatic adenocarcinoma
patients who underwent chemotherapy. Using Gaussian mixture models,
researchers identified optimal thresholds in the image-based
features and organized patients into unsupervised clusters, then
linked those to differences in survival. Risk models were generated
on standard clinicopathological features and used to compare
against the proposed image-based risk models.
The study’s key findings include:
- Cox PH models trained on image features more significantly
stratified risk on unseen test patients than an optimal set of
clinicopathological features
- Image-based models suggest low-risk patients have:
- Higher tumor-infiltrating lymphocytes despite having fewer
overall lymphocytes
- Higher tumor-adjacent stroma
- The combination of both risk-models proved to be superior in
training and testing sets and achieved better separation in
survival curves than either model by itself
- Image-based risk-associated features are independently
prognostic of clinicopathological features
- Even with a limited sample-size, the results showed
significance to warrant a larger study
“Our combined analysis with NantOmics, ImmunityBio and the
University of Colorado School of Medicine, provided results with a
level of significance that show great promise for future studies
using deep-learning models as a prognostic tool for pancreatic
cancer patients,” said Christopher Szeto, Director of Machine
Learning, NantHealth. “These results provide a strong clinical
platform not only to better understand pancreatic cancer for
evidence-based prognosis but also reinforce the essential nature of
advanced technology in the continued evolution of medicine.”
The AACR Virtual Special Conference: Pancreatic Cancer is a
conference focused on bringing together experts across industries
to better understand and further advance pancreatic cancer research
and treatment. The conference attracts a range of attendees,
including those in government, clinical roles, patient advocacy,
and companies who make groundbreaking technology and discoveries in
oncology.
About NantHealth, Inc.
NantHealth, a member of the NantWorks ecosystem of companies,
provides leading solutions across the continuum of care for
physicians, payers, 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 payer/provider
platforms that exchange information in near-real time (NaviNet and
Eviti), and molecular profiling services that combine comprehensive
DNA & RNA tumor-normal profiling with pharmacogenomics analysis
(GPS Cancer®). For more information, please visit nanthealth.com or
follow us on Twitter, Facebook and LinkedIn.
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MEDIA CONTACT NantHealth Jen Hodson Jen@nant.com
562-397-3639
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