Topology-based tumor analysis for medical images

基于拓扑的医学图像肿瘤分析

基本信息

批准号：
10653350
负责人：
Chul Moon
金额：
$ 41.91万
依托单位：
SOUTHERN METHODIST UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10653350
关键词：
3-Dimensional Anatomy Cell physiology Characteristics Clinical Communities Complex Confounding Factors (Epidemiology)DNA Sequence Alteration Data Data Analyses Data Set Decision Making Development Diagnostic Education Educational workshop Genomics Glioblastoma Glioma Goals Growth Image Imaging technology Institution Learning Location Lung Lung Adenocarcinoma Magnetic Resonance Imaging Manuals Medical Medical Imaging Medical Research Mentors Methodist Church Methodology Methods Modeling Molecular Neoplasm Metastasis Outcome Pathologic Pathology Pathology processes Patients Pattern Performance Play Process Prognosis Regional Cancer Research Research Assistant Research Personnel Resolution Resource Sharing Resources Role Series Shapes Signal Transduction Source Statistical Data Interpretation Statistical Models Structure Students The Cancer Genome Atlas Training Universities Work brain tumor imaging cancer biomarkers cancer diagnosis cancer therapy cell type clinical application clinical diagnosis computerized tools deep learning deep neural network experience gene interaction genomic signature high dimensionality imaging Segmentation insight neoplastic cell neural parallel computer pathology imaging prognostic radiological imaging radiomics screening shape analysis standard of care survival outcome survival prediction tool tumor tumor progression two-dimensional user friendly software user-friendly web based software web-based tool

项目摘要

Project Summary/Abstract Tumor shapes and patterns have been used as important markers for cancer diagnosis and treatment. Recent developments in medical imaging technology have enabled a more detailed description of tumor regions in high resolution. However, existing studies have described the shape of tumors in a limited scope. There is a scientific need to enhance understanding of tumor metastasis using medical images and provide a new insight for medical decision-making. This project aims to develop topological tumor shape representations for different types of medi- cal images and provide model-based approaches based on the topological image features. Our preliminary results suggest that topological features of medical images capture shapes and patterns of tumor regions and predict prog- nosis and survival after controlling key clinical parameters. The proposed project will further develop topology-based tumor analysis methods for pathology and radiographic images and provide tools to aid medical decision-making. The objective of the project will be accomplished by three aims: (1) develop methodologies to pair spatial and shape information of tumors and investigate relationships between genomic characteristics and topological features for three-dimensional gliomas of radiographic images; (2) develop topological tumor shape analysis methods that analyze shapes and interactions of multiple cell-type regions and extract image size-invariant shape representa- tions for two-dimensional lung adenocarcinoma pathology images; and (3) provide an accessible resource to the research community by offering user-friendly software and education. Our applications using lung adenocarcinoma pathological images and magnetic resonance imaging of primary gliomas images will provide prognostic information beyond standard clinical factors and serve as strong evidence for clinical usage of topological tumor shape analysis. This proposal will also allow students from various backgrounds at Southern Methodist University to experience a broad spectrum of medical research, including but not limited to genomic and cellular processes involved in tumors, survival modeling, topological data analysis, and deep learning in medical imaging while working with collaborative researchers from regional institutions.

项目摘要/摘要肿瘤形状和模式已被用作癌症诊断和治疗的重要标记。最近的医学成像技术的发展使对高肿瘤区域的更详细描述解决。但是，现有的研究描述了有限范围中肿瘤的形状。有科学需要使用医学图像增强对肿瘤转移的理解，并为医学提供新的见解决策。该项目旨在为不同类型的医疗 CAL图像并根据拓扑图像特征提供基于模型的方法。我们的初步结果建议医学图像的拓扑特征捕获肿瘤区域的形状和模式，并预测前进控制关键临床参数后的NOSIS和生存。拟议的项目将进一步发展基于拓扑的项目病理和射线照相图像的肿瘤分析方法，并提供了帮助医疗决策的工具。该项目的目的将通过三个目的来完成：（1）开发与空间和配对的方法论塑造肿瘤的信息并研究基因组特征与拓扑特征之间的关系用于射线照相图像的三维神经膜瘤；（2）开发拓扑肿瘤形状分析方法分析多个细胞类型区域的形状和相互作用，并提取图像大小不变形状代表二维肺腺癌病理学图像的影响；（3）为通过提供用户友好的软件和教育来研究社区。我们使用肺腺癌的应用原发性神经胶质瘤图像的病理图像和磁共振成像将提供预后信息除了标准临床因素之外，还可以作为拓扑肿瘤形状分析的临床使用的有力证据。该建议还将允许来自南方卫理公会大学各种背景的学生体验广泛的医学研究，包括但不限于与肿瘤有关的基因组和细胞过程，合作时，生存建模，拓扑数据分析和医学成像中的深度学习来自地区机构的研究人员。