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

• 批准号: 10197858
• 负责人: Kevin William Eliceiri
• 金额: $ 51.14万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 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/10197858
• 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; Epithelial; Estrogen Receptors; Goals; Gold; Health; Histopathology; Human; Image; Imaging Device; Imaging technology; Institutes; Interference Microscopy; Interobserver Variability; Investigation; Light; Malignant Neoplasms; Mammary Neoplasms; Maps; Measures; Mediating; Methodology; Microscope; Microscopic; Microscopy; Molecular; Morphologic artifacts; Morphology; Mosaicism; 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; Treatment outcome; Tumor Cell Invasion; Tumor-Derived; Wisconsin; Woman; Work; automated segmentation; base; biomarker evaluation; cancer cell; clinically relevant; diagnostic accuracy; image processing; imaging biomarker; immunohistochemical markers; improved; instrument; malignant breast neoplasm; microscopic imaging; nanoscale; new technology; novel; outcome prediction; pathology imaging; personalized medicine; prognostic; prognostic tool; prototype; response; risk stratification; tool; tumor initiation; tumor microenvironment; whole slide imaging

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.

项目总结