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

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2021 · $576,268

## Abstract

Summary
Fast, accurate, and scalable testing has been recognized unanimously as crucial for mitigating the
impact of COVID-19 and future pandemics. We propose a technology that allows rapid (~2
minutes) testing for SARS CoV-2. Our technology combines novel label-free imaging and
dedicated deep-learning algorithms to detect and classify viral populations in exhaled air. If
successful, this project will result in a device based on quantitative phase imaging and integrated
AI tools, which will detect the unlabeled virus acquired by the patient’s breath condensed on a
microscope slide. Toward this goal, we will advance Spatial Light Interference Microscopy
(SLIM), an ultrasensitive label-free imaging technique, proven to measure structures down to the
sub-nanometer scale. SLIM was developed in the PI’s Lab at UIUC, its original publication
received 490 citations to date, and has been commercialized by Phi Optics (Research Park,
UIUC), with sales across the world in both academia and industry.
Applying the computed fluorescence maps back to the QPI data, we propose to measure
nanoscale features of viral particles, with high specificity, minimal preparation time, and
independent of clinical infrastructure. As a result, the new technology will eventually be ideal for
point-of-care settings, surveillance screening and as a home monitoring device. We anticipate
that our approach will be scalable to other viruses, with new imaging and training data.

## Key facts

- **NIH application ID:** 10249738
- **Project number:** 3R01CA238191-02S1
- **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:** $576,268
- **Award type:** 3
- **Project period:** 2021-03-16 → 2024-03-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249738

## Citation

> US National Institutes of Health, RePORTER application 10249738, Quantitative histopathology for cancer prognosis using quantitative phase imaging on stained tissues (3R01CA238191-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10249738. Licensed CC0.

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