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

IMAT-ITCR 合作：结合 FIBI 和拓扑数据分析：肿瘤结构微环境探索的协同方法

• 批准号: 10884028
• 负责人: Chao Chen
• 金额: $ 7.33万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10884028
• 关键词: Address; Algorithmic Analysis; Blood Vessels; Breast biopsy; Clinical; Code; Collaborations; Collagen; Color; Consumption; Cues; Data Analyses; Descriptor; Development; Diagnostic; Diagnostic Imaging; Dimensions; Environment; Evaluation; Evolution; Fluorescence; Future; Generations; Geometry; Glass; Goals; Grant; Hematoxylin and Eosin Staining Method; Histology; Histopathology; Image; Image Analysis; Image Enhancement; Imaging Techniques; Immunofluorescence Immunologic; Interruption; Light; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Mathematics; Measures; Methodology; Methods; Modality; Morphology; Needles; Organism; Pathologist; Pattern; Phenotype; Pilot Projects; Pre-Clinical Model; Procedures; Process; Property; Reporting; Research; Role; Sampling; Scanning; Slide; Specimen; Speed; Stains; Structure; Surface; Taxonomy; Techniques; Thick; Thinness; Time; Tissue Sample; Tissue imaging; Tissues; Tumor Biology; Universities; Validation; Visual; Visualization; X-Ray Computed Tomography; algebraic topology; anticancer research; biomedical imaging; cancer subtypes; clinical diagnostics; clinical practice; cost effectiveness; deep learning; design; diagnostic tool; diagnostic value; imaging modality; improved; innovation; insight; interest; learning strategy; malignant breast neoplasm; novel; point of care; predictive modeling; prognostic tool; response; three dimensional structure; tissue fixing; tool; tumor; tumor microenvironment

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.

摘要：针对 PA-20-272，我们提议在石溪大学团队之间进行一项协作试点研究 （ITCR - MPI Prasanna 和 Chen）和加州大学戴维斯分校（IMAT - PI Levenson）利用新颖的数学拓扑工具。 由ITCR团队开发，我们的目标是理解和量化结构微环境的异同 荧光模拟明场成像 (FIBI) 和传统苏木精和伊红 (H&E) 图像的性能。 FIBI 是 IMAT 团队开发的一种廉价的无载玻片组织成像技术，可提供即时的高质量图像 来自新鲜或固定组织的年龄，类似于通过耗时的方法制备传统 H&E 后产生的组织 FIBI 的一个关键优势是它可以检测中断或可视化效果不佳的连续线性结构。 标准载玻片，这一功能对于评估肿瘤微结构环境和临床诊断具有重要意义。 然而，为了推动 FIBI 作为一种诊断成像方式，更深入地了解此类技术非常重要。 虽然试点验证报告表明 FIBI 图像仍具有复杂的结构特征。 对于 H&E 图像，还缺乏对这两种成像方式之间差异的全面量化； 此外，还没有 FIBI 特定的定量组织形态计量工具可以帮助表征和定量评估 不同的结构在厚组织切片和薄组织切片上更加明显。 ITCR 团队将采用 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 R33 项目中生成的 FIBI 图像中，该分析将重点关注具有精细尺度的结构。 连接性和表面轮廓特征在 FIBI 图像中很容易观察到，例如胶原蛋白束和血管。 提取的特征将进行颜色编码并映射到 FIBI 图像上，以进行可解释的可视化，以建立 所发现的拓扑配置文件的综合分类法，每个分析将包括至少 100 个 FIBI 样本。 包含 FIBI 和 H&E 图像，比较从图像中提取的拓扑特征以进行量化 描述结构微环境的差异。 将获得 FIBI 图像，然后提取描述符来表征拓扑之间的关系。 将研究感兴趣的结构和区域，例如癌症和正常区域以及癌症的不同亚型 使用统计技术和预测模型，该研究将增强对 FIBI（IMAT 团队）的理解。 诊断成像模式和完善的拓扑分析方法（ITCR 团队）将包括一套。 用于对 FIBI 扫描中观察到的结构环境进行整体表征的工具和技术。 将利用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。有意义的表型特征关联的成功识别将证明临床效果。 FIBI 技术的实用性，特别是作为指导治疗决策的诊断工具。

项目成果

Token Sparsification for Faster Medical Image Segmentation.

用于更快医学图像分割的令牌稀疏化。

• 发表时间: 2023-06
• 作者: Zhou, Lei;Liu, Huidong;Bae, Joseph;He, Junjun;Samaras, Dimitris;Prasanna, Prateek
• 通讯作者: Prasanna, Prateek

Anomaly-guided weakly supervised lesion segmentation on retinal OCT images

视网膜 OCT 图像上异常引导弱监督病变分割

• DOI: 10.1016/j.media.2024.103139
• 发表时间: 2024-03-12
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: Jiaqi Yang;Nitish Mehta;G. Demirci;Xiaoling Hu;Meera S. Ramakrishnan;Mina Naguib;Chao Chen;Chia
• 通讯作者: Chia

A tumor vasculature-based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors.

一种基于肿瘤血管系统的成像生物标志物，用于预测接受检查点抑制剂治疗的肺癌患者的反应和生存。

• 发表时间: 2022-11-25
• 影响因子: 13.6
• 作者: Alilou, Mehdi;Khorrami, Mohammadhadi;Prasanna, Prateek;Bera, Kaustav;Gupta, Amit;Viswanathan, Vidya Sankar;Patil, Pradnya;Velu, Priya Darsini;Fu, Pingfu;Velcheti, Vamsidhar;Madabhushi, Anant
• 通讯作者: Madabhushi, Anant