# Harnessing Coded Ptychography to Deliver AI-powered Evaluation of Unstained Lung Biopsies at the Point-Of-Care

> **NIH NIH R43** · PATHWARE INC. · 2022 · $399,583

## Abstract

Project Summary
 This Small Business Innovation Research (SBIR) Phase I project aims to develop an
imaging platform to analyze lung biopsy samples at the point of care. The system will
immediately digitize unstained specimens and utilize computer-assisted detection/diagnostics to
enable quick and efficient evaluations of samples by pathologists. The broad, long-term
objective of this proposed research is to enable real-time diagnostics of pathology samples at
the point-of-care.
 Early diagnosis and treatment of lung cancer is essential; the survival rate is low and is
highly dependent on the stage of the disease. Primary diagnoses through microscopic analysis
of biopsies (FNAs) have a 20-40% failure rate due to inadequate specimens. As a result, repeat
biopsies must be performed causing delays in diagnosis and treatment up to 90 days, which is
enough time for cancer to upstage and can reduce survivability by as much as 20%. Rapid-
onsite (ROSE) assessment of the adequacy by a pathologist can guarantee the diagnostic
quality of biopsies but is performed in <10% of lung biopsies due to financial and operational
barriers.
 In this project, we propose to overcome the historical barriers to ROSE to standardize
the procedure for lung biopsies. This can be accomplished with two key innovations: (i) the
Application of a novel microscopy modality (coded ptychography microscopy - CPM) for digital
pathology; and (ii) imaging and automated analysis of unstained lung FNAs using Artificial
Intelligence (AI) object detection algorithms. Preliminary work on thyroid FNAs indicates that
CPM can produce super-resolution quantitative phase images (QPIs) with well-visualized
cellularity on unstained slides. A multiwavelength compact prototype will be built and tested to
optimize image quality and speed on unstained lung FNAs. After demonstrating reproducible
high-quality images, object detection algorithms will be trained and validated on unstained QPIs,
and will direct pathologists to areas of interest for quick analysis. Adequacy assessments made
with and without AI object detection assistance will be compared to demonstrate the clinical
validity. The data produced in this feasibility study will fast-track product development and serve
as the foundation for clinical validation. Successful completion of this work is a key step in the
long-term goal of increasing the availability of pathology services worldwide in a concerted effort to
reduce cancer-associated mortality.

## Key facts

- **NIH application ID:** 10602206
- **Project number:** 1R43CA278604-01
- **Recipient organization:** PATHWARE INC.
- **Principal Investigator:** Torsten Lyon
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $399,583
- **Award type:** 1
- **Project period:** 2022-09-07 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10602206

## Citation

> US National Institutes of Health, RePORTER application 10602206, Harnessing Coded Ptychography to Deliver AI-powered Evaluation of Unstained Lung Biopsies at the Point-Of-Care (1R43CA278604-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10602206. Licensed CC0.

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