# Computational pathology software for integrative cancer research with three-dimensional digital slides

> **NIH NIH U01** · GEORGIA STATE UNIVERSITY · 2020 · $374,477

## Abstract

PROJECT SUMMARY:
Tissue-based investigation remains a cornerstone of cancer research. With the advent of cost-effective digital
scanners, large-scale quantitative investigations are now feasible using high throughput analysis of two-
dimensional (2D) image datasets. However, 2D image analytics has its limitations, since pathologic diseases
occur in three-dimensional (3D) space and 2D representations suffer from significant information loss. There
are major gaps for 3D analytical digital pathology, including lack of image analysis tools to quantitatively
process 3D data volumes and lack of an effective and scalable data management and analytical infrastructure
to model, curate, query and mine large-scale spatial pathology features and biomarkers. We propose to fill
these gaps with a new informatics solution directed at better understanding of 3D tumor micro-environments,
with driving use cases on immunotherapy study for enhanced immune cell infiltration for pancreatic ductal
adenocarcinoma (PDAC) and pathophysiological study of rapid tumor progression in brain tumor glioblastoma
(GBM). In line with Human Tumor Atlas program, we propose to create a novel and comprehensive 3D digital
pathology analytics framework to quantitatively analyze spatial patterns of pathologic hallmarks and
biomarkers related to disease progression in an authentic 3D tissue environment with quantitative digital
pathology image volume processing, spatially integrative histology-molecular image analysis, large-scale
spatial data analytics, and key cellular compartment tracking for clinical treatment response test and
immunotherapy development. To enable a wide use of informatics tools for 3D digital pathology imaging data
in cancer research, we will further upgrade a comprehensive, web-based system for multi-modality microscopy
image management, dissemination, and visualization. We will leverage a large set of informatics tools and
algorithms we have developed for microscopy image analysis, integrative translational cancer research,
pathology spatial analytics, and high performance computing in the past 14 years. The developed tools will be
tested and used by a suite of well-funded cancer research projects on pancreatic cancer, brain tumor, head
and neck, liver, and lung cancers. The proposed informatics tools will enable precise and comprehensive
characterizations of the histologic, molecular, cellular and tissue-level interactions at critical transition stages in
cancer progression. They will also allow for a precise interrogation of physical and spatial signatures of
immune cell infiltration into tumors, and the interactions between the host immune system and tumor cell
metastasis within a complex tumor micro-environment architecture, essential for immunotherapy development.
The completion of the proposed study will boost our informatics technology capabilities for large scale
microscopy image analytics, help cancer researchers accurately understand cancer biology and prog...

## Key facts

- **NIH application ID:** 9980817
- **Project number:** 5U01CA242936-02
- **Recipient organization:** GEORGIA STATE UNIVERSITY
- **Principal Investigator:** Jun Kong
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $374,477
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9980817

## Citation

> US National Institutes of Health, RePORTER application 9980817, Computational pathology software for integrative cancer research with three-dimensional digital slides (5U01CA242936-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9980817. Licensed CC0.

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