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

Sponsor

Centre Hospitalier Universitaire, Amiens (Other)

Overall Status

Unknown status

CT.gov ID

NCT04490343

Collaborator

(none)

100

Enrollment

Location

23.3

Anticipated Duration (Months)

4.3

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Urolithiasis Urinary Tract Stones Renal Colic Deep Learning Reconstruction	Diagnostic Test: Abdominopelvic low dose CT	N/A

Study Design

Study Type:

Interventional

Anticipated Enrollment :

100 participants

Allocation:

N/A

Intervention Model:

Single Group Assignment

Masking:

None (Open Label)

Primary Purpose:

Diagnostic

Official Title:

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

Actual Study Start Date :

Jul 21, 2020

Anticipated Primary Completion Date :

Jul 1, 2022

Anticipated Study Completion Date :

Jul 1, 2022

Outcome Measures

Primary Outcome Measures

Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones [day 1]
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).

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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