||1UG3CA225021-01 Interpret this number
||State University New York Stony Brook
||Methods and Tools for Integrating Pathomics Data Into Cancer Registries
The goal of this project is to enrich SEER registry data with high?quality population?based
biospecimen data in the form of digital pathology, machine learning based classifications and
quantitative pathomics feature sets. We will create a well?curated repository of high?quality
digitized pathology images for subjects whose data is being collected by the registries. These
images will be processed to extract computational features and establish deep linkages with
registry data, thus enabling the creation of information?rich, population cohorts containing
objective imaging and clinical attributes. Specific examples of digital Pathology derived feature
sets include quantification of tumor infiltrating lymphocytes and segmentation and
characterization of cancer or stromal nuclei. Features will also include spectral and spatial
signatures of the underlying pathology. The scientific premise for this approach stems from
increasing evidence that information extracted from digitized pathology images
(pathomic features) are a quantitative surrogate of what is described in a pathology report. The
important distinction being that these features are quantitative and reproducible, unlike human
observations that are highly qualitative and subject to a high degree of inter? and intra?observer
variability. This dataset will provide, a unique, population?wide tissue based view of cancer,
and dramatically accelerate our understanding of the stages of disease progression, cancer
outcomes, and predict and assess therapeutic effectiveness.
This work will be carried out in collaboration with three SEER registries. We will partner
with The New Jersey State Cancer Registry during the development phase of the project (UG3).
During the validation phase of the project (UH3), the Georgia and Kentucky State Cancer
Registries will join the project. The infrastructure will be developed in close collaboration with
SEER registries to ensure consistency with registry processes, scalability and ability support
creation of population cohorts that span multiple registries. We will deploy visual analytic tools
to facilitate the creation of population cohorts for epidemiological studies, tools to support
visualization of feature clusters and related whole?slide images while providing advanced
algorithms for conducting content based image retrieval. The scientific validation of the
proposed environment will be undertaken through three studies in Prostate Cancer, Lymphoma
and NSCLC, led by investigators at the three sites.
Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images.
, Hacihaliloglu I.
, Singer E.A.
, Foran D.J.
, Qi X.
Medical Image Computing And Computer-assisted Intervention : Miccai ... International Conference On Medical Image Computing And Computer-assisted Intervention, 2018 Sep; 11071, p. 201-209.