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.

项目概要大约八分之一的美国女性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帮助。做出治疗决策并完善患者风险分层。新研究扩展了当前的研究范围。预后标志物可以更好地提供个性化治疗方案然而，分析前的可变性。因素（活检采集、处理和储存）可能对生物标志物评估产生重大影响这可能会在患者护理方面造成潜在的严重后果。开发与生物样本制备无关的临床相关生物标志物。该项目提出了一种提取内在组织形态信息的技术解决方案，不受组织染色、切片厚度或切片误差的变化。（SLIM）被证明可以提供源自肿瘤微环境的预后标志物与癌细胞相邻的非恶性组织的纳米级组织，即基质对癌细胞的反应初步结果表明，SLIM 可以区分“匹配”的患者（良好与不匹配）。不良结果），并有能力消除误报并帮助临床医生分配适当的治疗。对于这个项目，我们将验证颜色 SLIM (cSLIM) 功能作为现有、染色的组织病理学载玻片将同时呈现明场和定量相。单次扫描中的图像将在带有颜色的全幻灯片成像 (WSI) 仪器中实现。病理学家熟悉的明场图像，同时保持与染色无关的信号，该信号具有完整的 WSI 仪器对基质和胶原纤维的高敏感性将用于开发预后价值。乳房预后的强大标记，与组织切片厚度、组织内颜色变化无关相同的染色类型（例如，H 和 E），以及跨染色（H 和 E、各种免疫化学染色等）。仪器，我们将在 196 个 TMA 案例上测试染色不变性性能，并用 300 个案例进行验证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）

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

具有计算特异性的相位成像 (PICS)，用于测量亚细胞区室中的干质量变化。

DOI：
发表时间：
2020-12-07
期刊：
影响因子：
16.6
作者：
Kandel, Mikhail E;He, Yuchen R;Lee, Young Jae;Chen, Taylor Hsuan;Sullivan, Kathryn Michele;Aydin, Onur;Saif, M Taher A;Kong, Hyunjoon;Sobh, Nahil;Popescu, Gabriel
通讯作者：
Popescu, Gabriel

Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS).

使用具有计算特异性的相位成像 (PICS) 对脑组织髓磷脂含量进行无标记筛查。

DOI：
发表时间：
2021-07-01
期刊：
影响因子：
5.6
作者：
Fanous, Michael;Shi, Chuqiao;Caputo, Megan P;Rund, Laurie A;Johnson, Rodney W;Das, Tapas;Kuchan, Matthew J;Sobh, Nahil;Popescu, Gabriel
通讯作者：
Popescu, Gabriel

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

使用部分相干照明的透明结构的沃尔夫相位断层扫描 (WPT)。