Detection of Urinary Stones on ULDCT With Deep-learning Image Reconstruction Algorithm (URO DLIR)

Detection of Urinary Tract Stones on Ultra-low Dose Abdominopelvic CT Imaging With Deep-learning Image Reconstruction Algorithm

Urolithiasis has an increasing incidence and prevalence worldwide, and some patients may have multiple recurrences. Because these stone-related episodes may lead to multiple diagnostic examinations requiring ionizing radiation, urolithiasis is a natural target for dose reduction efforts. Abdominopelvic low dose CT, which has the highest sensitivity and specificity among available imaging modalities, is the most appropriate diagnostic exam for this pathology. The main objective of this study is to evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in urolithiasis patients.

Study Overview

• Status: Unknown
• Conditions: Renal Colic; Urolithiasis; Urinary Tract Stones; Deep Learning Reconstruction
• Intervention / Treatment: Diagnostic test: Abdominopelvic low dose CT

• Study Type: Interventional
• Enrollment (Anticipated): 100
• Phase: Not Applicable

Contacts and Locations

Study Locations

• France
  • Amiens, France, 80480
    • Recruiting
    • CHU Amiens
    • Contact: Cédric Renard, MD; Phone Number: (33)322087537; Email: renard.cedric@chu-amiens.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Age ≥ 18 years old,
• Patient referred for abdominopelvic CT to confirm urolithiasis or for follow-up,
• Affiliation to a social security program,
• Ability of the subject to understand and express opposition

Exclusion Criteria:

• Age <18 years old,
• Person under guardianship or curators,
• Pregnant woman,
• Any contraindications to CT

Study Plan

How is the study designed?

Design Details

• Primary Purpose: DIAGNOSTIC
• Allocation: NA
• Interventional Model: SINGLE_GROUP
• Masking: NONE

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones	Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones. Patients who were referred to the department for abdominopelvic CT exam for urolithiasis diagnostic or follow-up, and had consented to participate in the study, will undergo an additional ultra-low dose acquisition (ULD, <1 mSv) with deep learning-based reconstruction (DLIR).	day 1

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire, Amiens

Study record dates

Study Major Dates

• Study Start (ACTUAL): July 21, 2020
• Primary Completion (ANTICIPATED): July 1, 2022
• Study Completion (ANTICIPATED): July 1, 2022

Study Registration Dates

• First Submitted: July 24, 2020
• First Submitted That Met QC Criteria: July 24, 2020
• First Posted (ACTUAL): July 29, 2020

Study Record Updates

• Last Update Posted (ACTUAL): July 29, 2020
• Last Update Submitted That Met QC Criteria: July 24, 2020
• Last Verified: July 1, 2020

More Information

Terms related to this study

• Keywords: Urolithiasis; dose reduction; radiation dose; Renal Colic; low dose CT; deep learning reconstruction; Urinary Tract Stones
• Additional Relevant MeSH Terms: Urologic Diseases; Pain; Neurologic Manifestations; Pathological Conditions, Anatomical; Renal Colic; Urinary Calculi; Urolithiasis; Calculi
• Other Study ID Numbers: PI2020_843_0053

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No