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

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2021 · $511,391

## Abstract

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.

## Key facts

- **NIH application ID:** 10197858
- **Project number:** 5R01CA238191-03
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Kevin William Eliceiri
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $511,391
- **Award type:** 5
- **Project period:** 2019-07-12 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10197858

## Citation

> US National Institutes of Health, RePORTER application 10197858, Quantitative histopathology for cancer prognosis using quantitative phase imaging on stained tissues (5R01CA238191-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10197858. Licensed CC0.

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