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

用于利用三维数字切片进行综合癌症研究的计算病理学软件

基本信息

批准号：
10238813
负责人：
Jun Kong
金额：
$ 30.81万
依托单位：
GEORGIA STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10238813
关键词：
3-Dimensional Algorithmic Analysis Algorithms Architecture Atlases Automobile Driving Biological Markers Brain Neoplasms Cancer Biology Cancer Research Project Cells Clinical Clinical Treatment Communities Complex Computer software Custom Data Data Analytics Data Set Development Diagnosis Disease Disease Progression Early Intervention Environment Funding Genetic Markers Glioblastoma Goals Head Cancer Hematoxylin and Eosin Staining Method High Performance Computing Histologic Histology Histopathology Human Image Image Analysis Imaging technology Immune Immune system Immunohistochemistry Immunotherapy Infiltration Informatics Infrastructure Intervention Investigation Malignant Neoplasms Malignant neoplasm of brain Malignant neoplasm of liver Malignant neoplasm of lung Malignant neoplasm of pancreas Maps Memory Methods Modeling Molecular Molecular Biology Motivation Neck Cancer Neoplasm Metastasis Online Systems Pancreatic Ductal Adenocarcinoma Pathologic Pathology Pattern Phenotype Process Reproducibility Research Research Personnel Resolution Slide Stains Structure System Technology Testing Therapeutic Studies Three-Dimensional Image Time Tissues Treatment Protocols Visualization anticancer research base cancer therapy clinical decision support clinically relevant clinically significant community engaged research cost effective data management deep learning digital digital imaging digital pathology fluorescence imaging high throughput analysis image registration imaging Segmentation improved informatics tool innovation interest microscopic imaging molecular imaging molecular marker multimodality novel optical imaging pathology imaging personalized medicine precision medicine predictive modeling programs reconstruction research and development response serial imaging spatiotemporal synergism targeted treatment therapeutic development therapy design therapy development tool tool development translational cancer research treatment response tumor tumor heterogeneity tumor initiation tumor microenvironment tumor progression two-dimensional web app

项目摘要

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 progression mechanisms, and enable clinicians an easy access to clinically relevant information from large scale microscopy images for computer based diagnosis and therapeutic development.

项目总结:

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

3DPro: Querying Complex Three-Dimensional Data with Progressive Compression and Refinement.

DOI：
10.48786/edbt.2022.02
发表时间：
2022-03
期刊：
Advances in database technology : proceedings. International Conference on Extending Database Technology
影响因子：
0
作者：
Teng D;Liang Y;Baig F;Kong J;Hoang V;Wang F
通讯作者：
Wang F

Building Efficient CNN Architectures for Histopathology Images Analysis: A Case-Study in Tumor-Infiltrating Lymphocytes Classification.

DOI：
10.3389/fmed.2022.894430
发表时间：
2022
期刊：
FRONTIERS IN MEDICINE
影响因子：
3.9
作者：
Meirelles, Andre L. S.;Kurc, Tahsin;Kong, Jun;Ferreira, Renato;Saltz, Joel H.;Teodoro, George
通讯作者：
Teodoro, George

Deep learning based registration of serial whole-slide histopathology images in different stains.

DOI：
10.1016/j.jpi.2023.100311
发表时间：
2023
期刊：
Journal of pathology informatics
影响因子：
0
作者：
Roy, Mousumi;Wang, Fusheng;Teodoro, George;Bhattarai, Shristi;Bhargava, Mahak;Rekha, T Subbanna;Aneja, Ritu;Kong, Jun
通讯作者：
Kong, Jun

Accelerating Spatial Cross-Matching on CPU-GPU Hybrid Platform With CUDA and OpenACC.

使用 CUDA 和 OpenACC 加速 CPU-GPU 混合平台上的空间交叉匹配。