IMAT-ITCR Collaboration: Combining FIBI and topological data analysis: Synergistic approaches for tumor structural microenvironment exploration

摘要：为了响应PA-20-272，我们建议石溪大学的团队之间进行一项合作的试点研究 (ITCR-MPIs Prasanna和Chen)和UC Davis(IMAT-PI Levenson)。通过利用新的数学拓扑工具 由ITCR团队开发，我们的目标是了解和量化结构微环境中的相似和差异- 通过荧光模拟亮场成像(FIBI)和常规的苏木精-伊红(H&E)图像进行成像。 FIBI是IMAT团队开发的一种廉价的无载玻片组织成像技术，可提供即时高质量的组织成像。 来自新鲜或固定组织的AGE，类似于用耗时的方法制备传统H&E后产生的AGE 幻灯片。FIBI的一个关键优势是它可以检测到被中断或可视化不佳的连续线性结构 标准载玻片，对评价肿瘤微结构环境和临床诊断有重要意义。 然而，为了推动FIBI作为一种诊断成像手段，对这种情况有更深的理解将是重要的 复杂的结构特征。虽然一份试点验证报告显示，FIBI图像保持了与 对于H&E图像，缺乏对这两种成像模式之间的差异的全面量化；此外- 此外，目前还没有FIBI特有的定量组织形态计量学工具来帮助表征和定量评估 不同的结构在厚切的组织切片上比在薄切的组织切片上更加突出。 ITCR团队将采用他们的R21项目中提出的拓扑数据分析(TDA)工具来研究3D结构 在IMAT团队在他们的R33项目中生成的FIBI图像中。本分析将侧重于具有以下特性的精细结构 连通性和表面轮廓特征在FIBI图像中很容易察觉，如胶原束和血管。 提取的要素将被颜色编码并映射到FIBI图像上，以便进行可解释的可视化以建立 已发现的拓扑配置文件的全面分类。分析将包括至少100个FIBI样本，每个样本 包含FIBI和H&E图像，比较从图像中提取的拓扑特征进行量化 在描述结构微环境方面的差异。对内部感兴趣的特定结构进行专家细分 将获得FIBI图像，然后提取描述符来表征拓扑。两国之间的关系 将对感兴趣的结构和区域，如癌症和正常区域，以及不同的癌症亚型进行研究 使用统计技术和预测模型。这项研究将增进对FIBI(IMAT Team)作为一个 诊断成像模式和改进的拓扑分析方法(ITCR团队)。交付成果将包括一套 用于对FIBI扫描上观察到的结构环境进行整体表征的工具和技术。付出的努力 将利用两个团队在数学工具和图像分析方面的专业知识，以之前在乳房方面的合作为基础 癌症图像分析。成功识别有意义的表型-特征关联将证明临床 FIBI技术的实用性，特别是作为指导治疗决策的诊断工具。

SUMMARY: In response to PA-20-272, we propose a collaborative pilot study between teams at Stony Brook University (ITCR - MPIs Prasanna and Chen) and UC Davis (IMAT - PI Levenson). By leveraging novel mathematical topology tools developed by the ITCR team, we aim to understand and quantify the similarities and differences in the structural microenvi- ronment across Fluorescence Imitating Brightfield Imaging (FIBI) and conventional hematoxylin and eosin (H&E) images. FIBI, an inexpensive slide-free tissue imaging technique developed by the IMAT team, provides immediate high-quality im- ages from fresh or fixed tissues that resemble those generated after time-consuming methods used to prepare traditional H&E slides. A key advantage of FIBI is that it can detect continuous linear structures that are interrupted or poorly visualized on standard slides, a feature with significance to the evaluation of tumor micro-structural environment and clinical diagnostics. However, to advance FIBI as a diagnostic imaging modality, it will be important to gain a deeper understanding of such intricate structural features. While a pilot validation report has shown that FIBI images retain diagnostic power compared to H&E images, a comprehensive quantification of the differences between these two imaging modalities is lacking; further- more, there are no FIBI-specific quantitative histomorphometry tools that can help characterize and quantitatively evaluate different structures that are more salient on thickly vs. thinly cut tissue sections. The ITCR team will adapt topological data analysis (TDA) tools proposed in their R21 project to study the 3D structures in FIBI images generated by the IMAT team in their R33 project. This analysis will focus on fine-scale structures with connectivity and surface-profile features easily appreciable in FIBI images such as collagen bundles and blood vessels. The extracted features will be color coded and mapped onto the FIBI images for interpretable visualization to establish comprehensive taxonomies for the discovered topology profiles. Analysis will include at least 100 FIBI samples, each containing FIBI and H&E images, with comparisons between the topological features extracted from images to quantify the differences in describing the structural microenvironment. Expert segmentations of specific structures of interest within FIBI images will be obtained, followed by the extraction of descriptors to characterize topology. The relationships between structures and regions of interest, such as cancer and normal regions, and different subtypes of cancer, will be investigated using statistical techniques and predictive models. The study will enhance the understanding of FIBI (IMAT team), as a diagnostic imaging modality and refine topological analysis methodology (ITCR team). Deliverables will include a set of tools and techniques for holistic characterization of the structural environment as observed on the FIBI scans. The effort will leverage the teams’ expertise in mathematical tools and image analysis, building upon previous collaborations on breast cancer image analysis. Successful identification of meaningful phenotype-feature associations will demonstrate the clinical utility of the FIBI technique, particularly as a diagnostic tool for guiding treatment decisions.