Topology-based tumor analysis for medical images

项目总结/摘要 肿瘤的形状和模式已被用作癌症诊断和治疗的重要标志物。最近 医学成像技术的发展使得能够更详细地描述高密度的肿瘤区域。 分辨率然而，现有的研究在有限的范围内描述了肿瘤的形状。有一个科学 需要利用医学图像增强对肿瘤转移的理解，并为医学诊断提供新的见解。 决策的该项目旨在为不同类型的medi开发拓扑肿瘤形状表示， 计算图像，并提供基于拓扑图像特征的基于模型的方法。我们的初步结果 表明医学图像拓扑特征捕捉肿瘤区域的形状和模式，并预测肿瘤的预后。 控制关键临床参数后的诊断和生存率。拟议项目将进一步开发基于拓扑的 肿瘤分析方法的病理学和放射影像，并提供工具，以帮助医疗决策。 该项目的目标将通过三个目标实现：（1）制定方法，将空间和 肿瘤的形状信息，并研究基因组特征和拓扑特征之间的关系 用于放射学图像的三维胶质瘤;（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的形状表示， 二维肺腺癌病理学图像的选择;以及（3）为 研究社区提供用户友好的软件和教育。我们在肺腺癌中的应用 原发性胶质瘤的病理图像和磁共振成像图像将提供预后信息 超出标准临床因素，并作为拓扑肿瘤形状分析的临床用途的有力证据。 这项提案还将允许来自南卫理公会大学各种背景的学生体验一个 广泛的医学研究，包括但不限于涉及肿瘤的基因组和细胞过程， 生存建模、拓扑数据分析和医学成像中的深度学习， 区域机构的研究人员。

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.