# PRISTINE: Pre-cancer histology identification of Endobronchial biopsies using deep learning

> **NIH NIH R21** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $424,256

## Abstract

PROJECT SUMMARY
Lung cancer is the leading cause of cancer death. In order to increase survival, therapies are urgently needed
to intercept the cancer development process and decrease the rate of patients presenting with advanced
disease. A potential promising point of interception is to develop therapies to reverse or delay the development
of lung premalignant lesions (PMLs). About 20% of lung cancers arise in the epithelial layer of the bronchial
airways and these are preceded by the development of PMLs that are important clinical indicators of lung cancer
risk in the airways or at remote parenchymal sites. As part of the NCI-Moonshot our group is engaged in creating
a multi-omic lung Pre-Cancer Atlas (PCA). The success of this project in creating clinically relevant biomarkers
and therapeutics depends on accurate assessments of histology and immune infiltrates in PMLs. Currently,
however, pathologic assessment of the morphological stages of increasing abnormality from hyperplasia,
metaplasia, dysplasia (mild, moderate, and severe), to invasive carcinoma is challenging and not routine. The
objective of the proposed study is to develop and disseminate a computationally efficient deep learning
framework to annotate a variety of histologic features in PMLs from the Lung PCA and associate these features
with clinical and genomic data. Our central hypothesis is that deep learning can be applied to digitized H&E
whole slide images (WSIs) of bronchial PMLs to identify a comprehensive set of histologic features and metrics
summarizing their spatial organization that may enhance biomarkers of PML progression to cancer. We will test
this hypothesis by pursuing two specific aims. First, we will annotate PMLs and develop a semantic
segmentation framework using deep learning to predict histologic features of PMLs. Second, we will disseminate
our deep learning framework and show its utility in enhancing PML-associated biomarkers. The proposed study
is significant because the framework we develop can be applied to predict other features in the WSIs from PMLs
and be modified to encompass other PMLs of the lung (e.g. those associated with lung adenocarcinoma) as well
as other premalignant lesions found in other epithelial tissue types such as breast, colon, prostate, etc. Currently,
deep learning approaches have not been applied to PMLs, and this proposal is innovative in the unique clinical
specimens that it leverages with corresponding genomic and clinical data and in its development of a patch-
based convolutional neural network to predict histologic features of PMLs. Our long-term goal is to develop a
deep learning framework to predict a variety of features from lung PML WSIs and integrate these with genomic
data on these same samples to discover robust biomarkers of PML progression and therapeutics to prevent
invasive cancer development.

## Key facts

- **NIH application ID:** 10059031
- **Project number:** 1R21CA253498-01
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Jennifer Ellen Beane
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $424,256
- **Award type:** 1
- **Project period:** 2020-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10059031

## Citation

> US National Institutes of Health, RePORTER application 10059031, PRISTINE: Pre-cancer histology identification of Endobronchial biopsies using deep learning (1R21CA253498-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10059031. Licensed CC0.

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