Ignite Creation Date:
2024-05-19 @ 5:35 PM
Last Modification Date:
2024-10-26 @ 3:29 PM
Study NCT ID:
NCT06412900
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-05-14
First Post:
2024-04-30
Brief Title:
Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
Sponsor:
Oslo University Hospital
Organization:
Oslo University Hospital
Study Overview
Official Title:
Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
RISUS_AI
Brief Summary:
Kidney stone disease causes significant morbidity and stones obstructing the ureter can have serious consequences Imaging diagnostics with computed tomography CT are crucial for diagnosis treatment selection and follow-up Segmentation of CT images can provide objective data on stone burden and signs of obstruction Artificial intelligence AI can automate such segmentation but can also be used for the diagnosis of stone disease and obstruction
In this project the aim is to investigate if
Manual segmentation of CT scans can provide more accurate information about kidney stone disease compared to conventional interpretation
AI segmentation yields valid results compared to manual segmentation AI can detect ureteral stones and obstruction or predict spontaneous passage
In this project the aim is to investigate if
Manual segmentation of CT scans can provide more accurate information about kidney stone disease compared to conventional interpretation
AI segmentation yields valid results compared to manual segmentation AI can detect ureteral stones and obstruction or predict spontaneous passage
Detailed Description:
Background
Goals and Objectives
The project aims to contribute to personalized and improved treatment and follow-up of patients with kidney stones using radiomics and the development of an artificial intelligence tool for CT examination assessment The objectives are to assess
Whether manual segmentation of CT images of the urinary tract provides equivalent or more accurate information about kidney stone disease compared to conventional interpretation and reporting
Whether segmentation performed with AI yields valid results compared to manual segmentation
Whether AI can detect ureteral stones and obstruction andor predict spontaneous passage of stones
Method
Cohort
Patients are recruited to the study at Oslo University Hospital Radiology Department Section Aker which performs approximately 1350 CT examinations for urinary tract stones in approximately 1000 patients each year Approximately 500 patients with a new episode or newly occurring colic pain and clinical suspicion of kidney stones are expected to be included
Clinical data where available
Baseline CT date and image data
Initial treatment conservative URS PCN ESWL decision after baseline CT
Follow-up CT date and image data
Time to spontaneous stone passage negative control CT or completed surgical intervention URS
Any other surgicalinvasive procedure
Stone chemical analysis
Clinical biochemistry creatinineeGFR CRP leukocytes at baseline and follow-ups
Image data
Clinical radiology report
Stone largest calculus and any obstructing calculus largest diameter in any plane density ROI set by clinical judgment largest possible ROI - in the slice where the stone is largest location upper ureter above crossing of vessels lower ureter below crossing of vessels ostial in bladder wall
Renal pelvis largest diameter of calyx neck lower calyx clinical assessment of dilation not dilatedslightmoderatesevere
Segmentation
Stone total segmented stone volume largest diameter and density of segmented stone
Collecting system total segmented volume of the collecting system and renal pelvis
Goals and Objectives
The project aims to contribute to personalized and improved treatment and follow-up of patients with kidney stones using radiomics and the development of an artificial intelligence tool for CT examination assessment The objectives are to assess
Whether manual segmentation of CT images of the urinary tract provides equivalent or more accurate information about kidney stone disease compared to conventional interpretation and reporting
Whether segmentation performed with AI yields valid results compared to manual segmentation
Whether AI can detect ureteral stones and obstruction andor predict spontaneous passage of stones
Method
Cohort
Patients are recruited to the study at Oslo University Hospital Radiology Department Section Aker which performs approximately 1350 CT examinations for urinary tract stones in approximately 1000 patients each year Approximately 500 patients with a new episode or newly occurring colic pain and clinical suspicion of kidney stones are expected to be included
Clinical data where available
Baseline CT date and image data
Initial treatment conservative URS PCN ESWL decision after baseline CT
Follow-up CT date and image data
Time to spontaneous stone passage negative control CT or completed surgical intervention URS
Any other surgicalinvasive procedure
Stone chemical analysis
Clinical biochemistry creatinineeGFR CRP leukocytes at baseline and follow-ups
Image data
Clinical radiology report
Stone largest calculus and any obstructing calculus largest diameter in any plane density ROI set by clinical judgment largest possible ROI - in the slice where the stone is largest location upper ureter above crossing of vessels lower ureter below crossing of vessels ostial in bladder wall
Renal pelvis largest diameter of calyx neck lower calyx clinical assessment of dilation not dilatedslightmoderatesevere
Segmentation
Stone total segmented stone volume largest diameter and density of segmented stone
Collecting system total segmented volume of the collecting system and renal pelvis
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 660399 | OTHER | Regional Committees for Medical Research Ethics in Norway | None |