# Automated Measurement of Bowel Damage Using Enterography Imaging to Predict Clinical Outcomes in Crohn’s Disease.

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $526,016

## Abstract

Current management of Crohn’s disease (CD) relies on monitoring objective endpoints of mucosal
inflammation. While structural bowel damage drives surgery in more than half of patients with CD,
assessments of structural bowel damage are challenging to quantify and incorporate into treatment decision-
making. Cross-sectional imaging can survey deep bowel damage and fibrostenotic changes, but the time and
expertise needed, and the susceptibility of qualitative features to interobserver variation, pose challenges in the
broader use of imaging data to personalize care. The long-term goal of this research is to develop methods to
objectively measure structural bowel damage and individualize predictions of clinical outcomes in CD. The
overall objectives in this application are to test (i) the ability of computational image analysis methods to collect
traditional and novel characterizations of bowel damage using common enterography imaging studies and (ii)
to evaluate these measures’ ability to improve predictions of CD outcomes. The central hypothesis is that
bowel damage features collected by computational image analysis methods will improve the accuracy of
models predicting therapeutic and clinical outcomes in CD. This central hypothesis will be tested through three
specific aims: (1) Determine the performance of computational analysis of enterography studies capturing
bowel damage measurements for predicting CD clinical outcomes in the regular course of care, (2)
Prospectively compare the performance of enterography image analysis for predicting therapeutic response to
existing laboratory and endoscopic measures, and (3) Evaluate image analysis capacity to determine
underlying tissue histology in CD using conventional imaging. In the first aim, enterography studies in a
national prospective CD natural history dataset will undergo image analysis to extract measurements used to
model surgical, hospitalization, and steroid use outcomes. Further work in this aim will test the agreement
between expert radiologists and computer-derived bowel measurements. In the second aim, subjects starting
new biologic therapies will undergo scheduled enterography to compare the prognostic capabilities of
computationally derived bowel features to inflammatory biomarkers and endoscopy for predicting therapeutic
response. Finally, in the third aim, patients undergoing elective surgical resection of intestine for CD will have
pre-operative enterography. High dimensional image features will be used to model histologic grading of
inflammation and fibrosis. The proposed research is innovative in approaching structural bowel damage as a
related, but independent and equally important, companion assessment to inflammation in the prognosis and
treatment of CD. Further, computational image analysis opens new horizons not only in objectivity and
reproducibility, but also concepts of how to measure CD burden. The proposed research is significant because
it will demonstrate the indisp...

## Key facts

- **NIH application ID:** 10173763
- **Project number:** 5R01DK124779-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Ryan William Stidham
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $526,016
- **Award type:** 5
- **Project period:** 2020-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10173763

## Citation

> US National Institutes of Health, RePORTER application 10173763, Automated Measurement of Bowel Damage Using Enterography Imaging to Predict Clinical Outcomes in Crohn’s Disease. (5R01DK124779-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10173763. Licensed CC0.

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