# Improving Colorectal Cancer Screening and Risk Assessment through Deep Learning on Medical Images and Records

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2022 · $356,700

## Abstract

PROJECT SUMMARY/ABSTRACT
Most colorectal cancer cases start as a small growth, known as a polyp, on the lining of the colon or rectum.
Although colorectal polyps are precursors to colorectal cancer, it takes several years for these polyps to
potentially transform into cancer. If colorectal polyps are detected early, they can be removed before they can
progress to cancer. The microscopic examination of stained tissue from colorectal polyps on glass slides—the
practice of histopathology—is a key part of colorectal cancer screening and forms the current basis for
prognosis and patient management. Histopathological characterization of polyps is an important principle for
determining the risk of colorectal cancer and future rates of surveillance for patients; however, it is time-
intensive, requires years of specialized training, and suffers from high variability and low accuracy. In addition,
as is evident by the domain literature, other health factors, such as medical and family history, play an
important role in colorectal cancer risk; however, they are not considered in current standard guidelines for
colorectal cancer risk assessment. Therefore, there is a critical need for computational tools that can
incorporate both histopathological and relevant clinical/familial information to help clinicians better characterize
colorectal polyps and more accurately assess risk for colorectal cancer.
To address this critical need, this application proposes to build a novel, automatic, image-analysis method that
can accurately detect and classify different types of colorectal polyps on whole-slide microscopic images. The
proposed approach will be able to identify discriminative regions and features on these images for each
colorectal polyp type, which will provide support and insight into the automatic detection of colorectal polyps on
whole-slide images. Finally, this project will provide an accurate risk prediction model to integrate visual
histology features from microscopic images with other risk factors and relevant clinical information from
medical records for a comprehensive colorectal cancer risk assessment. The proposed image analysis and
prediction methods in this project are based on a novel deep-learning methodology and rely on numerous
levels of abstraction for data representation and analysis. The technology developed in this proposal will be
rigorously validated on data from patients undergoing colorectal cancer screening at the investigators’
academic medical center and on the records from the New Hampshire statewide colonoscopy data registry.
Upon successful completion of this project, the proposed bioinformatics approach is expected to reduce the
cognitive burden on pathologists and improve their accuracy and efficiency in the histopathological
characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations. As a
result, this project can have a significant, positive impact on improving the efficacy of co...

## Key facts

- **NIH application ID:** 10316231
- **Project number:** 5R01LM012837-04
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Saeed Hassanpour
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $356,700
- **Award type:** 5
- **Project period:** 2019-02-12 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10316231

## Citation

> US National Institutes of Health, RePORTER application 10316231, Improving Colorectal Cancer Screening and Risk Assessment through Deep Learning on Medical Images and Records (5R01LM012837-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10316231. Licensed CC0.

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