“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.

Health IT Homepage