# GVHD quantitative assessment in skin of Veterans post-HCT

> **NIH VA IK2** · VETERANS HEALTH ADMINISTRATION · 2020 · —

## Abstract

Chronic graft-versus-host disease (cGVHD) is the leading cause of nonrelapse mortality
following allogeneic hematopoietic stem cell transplantation (HCT). Once the diagnosis is made, a
fundamental practice gap remains the determination of whether disease is stable or progressing.
Clinical trials of promising new potential treatments are limited by the lack of reproducible and sensitive
measures of cGVHD severity.
 Skin is central to cGVHD evaluation because it is the most commonly involved organ. Features
are divided into erythema (visualized changes) and sclerosis (palpated mechanical changes). This
project will implement an objective, longitudinal monitoring approach to cGVHD by combining 3D digital
photography, machine learning, and biomechanical assessment with the Myoton device. These
technologies look at and feel skin analogously to a clinical exam, but in a precise and quantitative
fashion. The hypothesis of the proposed research is that this integrated technological approach will
reliably detect clinically important changes in disease severity. This will provide the opportunity to
overcome the shortcomings in existing methods, enabling quantitative assessments to validate and
guide therapy.
 Aim 1 will test the reliability and reproducibility to quantify erythema body surface area with 3D
photography and deep learning. A large patient image data set will be created to optimize and test the
reliability of a deep learning convolutional neural network to independently identify, demarcate and
grade regions of erythema. Aim 2 will test the reproducibility of biomechanical assessment of skin
sclerosis with the Myoton, a handheld commercial device that is widely used to noninvasively measure
biomechanical and viscoelastic properties of muscle. Aim 3 will evaluate the ability of the integrated
quantitative approach to measure clinically meaningful changes in cGVHD severity in a year of follow-
up of a prospective cohort of cGVHD patients.
 The proposed neural-network assessment of erythema and skin biomechanical assessment
with Myoton are each significant innovations, which can later be applied to a broad range of other
progressive cutaneous diseases. The proposed work is significant because it addresses the inability to
accurately measure cGVHD severity and treatment response, which is currently the fundamental barrier
to permanent successful treatment by HCT of hematologic malignancies and other diseases.

## Key facts

- **NIH application ID:** 9840394
- **Project number:** 5IK2CX001785-02
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** Eric R Tkaczyk
- **Activity code:** IK2 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9840394

## Citation

> US National Institutes of Health, RePORTER application 9840394, GVHD quantitative assessment in skin of Veterans post-HCT (5IK2CX001785-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9840394. Licensed CC0.

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