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Quantitative histopathology for cancer prognosis using quantitative phase imaging on stained tissues

使用染色组织的定量相位成像进行癌症预后的定量组织病理学

基本信息

批准号：
10703212
负责人：
Mark A Anastasio
金额：
$ 46.72万
依托单位：
UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
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：
2019-07-12 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10703212
关键词：
Algorithms Architecture Attention Basement membrane Biological Assay Biological Markers Biopsy Blood Breast Calibration Cancer Prognosis Classification Clinic Collagen Fiber Collection Color Desmoplastic Development Diagnostic Disease Marker Early Diagnosis Epidermal Growth Factor Receptor Epithelium Estrogen Receptors Goals Health Histopathology Human Image Imaging Device Imaging technology Interference Microscopy Interobserver Variability Invaded Investigation Light Malignant Neoplasms Mammary Neoplasms Maps Measures Mediating Methodology Microscope Microscopic Microscopy Molecular Morphologic artifacts Morphology Non-Malignant Operative Surgical Procedures Optics Outcome Pathologist Pathology Patient Care Patient risk Patients Performance Phase Physicians Preparation Procedures Progesterone Receptors Prognosis Prognostic Marker Prostate Quantitative Microscopy Research Role Scanning Signal Transduction Site Slice Slide Specimen Stains TP53 gene Techniques Technology Testing Thick Time Tissue Stains Tissues Training Translating Treatment Protocols Tumor Cell Invasion Wisconsin Woman Work automated segmentation biomarker evaluation cancer cell clinically relevant diagnostic accuracy image processing imaging biomarker immunohistochemical markers improved instrument malignant breast neoplasm microscopic imaging mosaic nanoscale new technology novel outcome prediction personalized medicine prognostic prognostic tool prototype response risk stratification tool treatment and outcome tumor initiation tumor microenvironment whole slide imaging

项目摘要

Project Summary About 1 in 8 U.S. women will develop invasive breast cancer over the course of her lifetime. Early diagnosis and prognosis are key to improving health outcomes. Prognostic markers in tissue biopsies help clinicians make treatment decisions and refine the patient risk stratification. New research expands the current prognostic markers to better deliver personalized treatment regimens. However, the variability of preanalytical factors (biopsy collection, processing and storage) can have a significant impact on biomarkers evaluation which can result in potentially serious consequences in terms of patient care. There is an identified need for developing clinically relevant biomarkers that are invariant to biospecimen preparation. This project proposes a technical solution to extracting intrinsic tissue morphology information, unaffected by variability in tissue staining, slice thickness, or sectioning errors. Spatial Light Interference Microscopy (SLIM) was shown to provide prognostic markers derived from tumor microenvironment using nanoscale organization of the non-malignant tissue adjacent to cancer cells, i.e., the stromal response to cancer. Preliminary results indicate that SLIM can distinguish between pairs of “matched” patients (good vs. bad outcome) and has the capability to eliminate false positives and help the clinician assign the appropriate treatment. For this project, we will validate color SLIM (cSLIM) capabilities as a prognostic tool for existing, stained histopathology slides. cSLIM will render simultaneously bright field and quantitative phase images, in a single scan. cSLIM will be implemented in a whole slide imaging (WSI) instrument with the color bright field image familiar to pathologists, while maintaining a stain-independent signal, which has intact prognosis value. The WSI instrument’s high sensitivity to stroma and collagen fibers will be used to develop robust markers for breast prognosis, which are independent of tissue slice thickness, color variability within the same stain type (say, H & E), and across stains (H & E, various immunochemical stains, etc). With this new instrument, we will test the staining-invariance performance on 196 TMA cases and validate with 300 biopsies. The work is the results of combining expertise in imaging, pathology, and image processing across four sites: UIUC Beckman Institute, the Mills Breast Cancer Institute in Urbana, UIC Pathology, and U. Wisconsin.

项目摘要大约八分之一的美国妇女在一生中会患上浸润性乳腺癌。早期诊断和预后是改善健康结果的关键。组织活检中的预后标志物有助于临床医生做出治疗决定并细化患者风险分层。新的研究扩大了目前预后标志物，以更好地提供个性化的治疗方案。然而，分析前的变异性因素（活检收集、处理和储存）可能对生物标志物评价产生重大影响这可能在病人护理方面导致潜在的严重后果。已确定需要开发临床相关的生物标志物，这些生物标志物对于生物样本制备是不变的。本项目提出了一种技术解决方案，提取内在组织形态信息，不受组织染色、切片厚度或切片误差的可变性。空间光干涉显微镜（SLIM）显示出提供来自肿瘤微环境的预后标志物，邻近癌细胞的非恶性组织的纳米级组织，即，基质对癌初步结果表明，SLIM可以区分成对的“匹配”患者（良好与不良结果），并有能力消除假阳性，并帮助临床医生分配适当的治疗对于这个项目，我们将验证彩色SLIM（cSLIM）功能作为现有的，染色的组织病理学切片。cSLIM将同时呈现明场和定量相位图像，在一个单一的扫描。cSLIM将在全载玻片成像（WSI）仪器中实施，颜色为病理学家熟悉的明场图像，同时保持与染色无关的信号，预后值WSI仪器对基质和胶原纤维的高灵敏度将用于开发乳腺预后的稳健标志物，其独立于组织切片厚度、相同的染色类型（例如，H & E）和交叉染色（H & E，各种免疫化学染色等）。有了这个新仪器，我们将在196个TMA病例上测试染色不变性性能，并在300个活组织检查这项工作是结合成像，病理学和图像处理方面的专业知识的结果。四个研究中心：UIUC贝克曼研究所，厄巴纳的米尔斯乳腺癌研究所，UIC病理学，和U。威斯康星州。

项目成果

期刊论文数量（32）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data.

在空间光干扰显微镜数据中使用深度学习的自动结直肠癌筛查。

DOI：
10.3390/cells11040716
发表时间：
2022-02-17
期刊：
Cells
影响因子：
6
作者：
Zhang JK;Fanous M;Sobh N;Kajdacsy-Balla A;Popescu G
通讯作者：
Popescu G

White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).

DOI：
10.1038/s41598-022-21250-z
发表时间：
2022-11-21
期刊：
Scientific reports
影响因子：
4.6
作者：
通讯作者：

Live-dead assay on unlabeled cells using phase imaging with computational specificity.

DOI：
10.1038/s41467-022-28214-x
发表时间：
2022-02-07
期刊：
Nature communications
影响因子：
16.6
作者：
Hu C;He S;Lee YJ;He Y;Kong EM;Li H;Anastasio MA;Popescu G
通讯作者：
Popescu G

Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer.

DOI：
10.3390/bioengineering8020017
发表时间：
2021-01-21
期刊：
Bioengineering (Basel, Switzerland)
影响因子：
0
作者：
Ouellette JN;Drifka CR;Pointer KB;Liu Y;Lieberthal TJ;Kao WJ;Kuo JS;Loeffler AG;Eliceiri KW
通讯作者：
Eliceiri KW

Wolf phase tomography (WPT) of transparent structures using partially coherent illumination