# Quantitative, non-invasive characterization of urinary stone composition and fragility using multi-energy CT and machine learning techniques

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2022 · $357,486

## Abstract

PROJECT SUMMARY
Symptomatic urinary stone disease (USD) affects >8% of the United States population, resulting in an
estimated annual medical cost exceeding $10 billion. Computed tomography (CT) is the established method for
imaging urinary calculi and can provide accurate sub-millimeter details of the size and location of renal stones.
However, in vivo characterization of more than just size and location is critical for quantifying stone
characteristics important for optimal patient health management and essential for clinical research. A complete
characterization of renal stones, including stone composition and fragility, is needed for safe and cost effective
management of USD, as well as for phenotyping of research subjects. Our proposal meets these needs by
developing methods to accurately and non-invasively characterize stones using low-dose, multi-energy CT.
Our long-term goal is to use advanced CT methodologies to characterize urinary calculi for the purpose of
directing clinical treatment and facilitating clinical investigation. Our objectives in this application are to develop
and validate in vivo quantitative techniques for characterizing mixed and non-uric-acid stone types, as well as
for predicting the likelihood of successful stone comminution, a novel concept we refer to as stone fragility.
These image-based stone biometrics will enable evidence-based identification of treatment strategies that
maximize effectiveness while minimizing risk, as well as accurate and non-invasive classification of research
subjects to accelerate scientific advances in the understanding and treatment of USD. We will meet these
objectives by accomplishing the following specific aims:
 Specific Aim 1: Develop and validate CT techniques to characterize mixed and non-uric-acid
 stone types.
 Specific Aim 2: Develop and validate CT techniques to predict stone fragility.
Current state-of-the-art stone imaging technology cannot accurately identify the composition of mixed and non-
uric-acid stone types, nor can it provide quantitative indications of the likelihood of efficient comminution using
the lowest risk technique. The innovation of this proposal lies in the use of newly developed statistical, deep
learning and texture analysis techniques to quantitatively describe essential characteristics of urinary calculi,
namely composition and fragility. The significance of this proposal is that the knowledge derived from using
such techniques represents unique quantitative biomarkers that will allow physicians and researchers to more
effectively manage and study USD. The developed methods respond to critical needs in the field of stone
disease and will advance the ability of physicians to optimally direct patient therapy and scientists to phenotype
research subjects.

## Key facts

- **NIH application ID:** 10377461
- **Project number:** 5R01EB028591-04
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Cynthia H McCollough
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $357,486
- **Award type:** 5
- **Project period:** 2019-06-01 → 2023-11-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10377461

## Citation

> US National Institutes of Health, RePORTER application 10377461, Quantitative, non-invasive characterization of urinary stone composition and fragility using multi-energy CT and machine learning techniques (5R01EB028591-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10377461. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*