Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

Urolithiasis Clinical Trial

— RISUS_AI  

Official title:

Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06412900
• Other study ID #: 31347039
• Secondary ID: 660399
• Status: Not yet recruiting
• Start date: May 15, 2024
• Est. completion date: March 28, 2028

Study information

• Verified date: May 2024
• Source: Oslo University Hospital
• Contact: Peter M. Lauritzen, MD, PhD
• Phone: +4795248249
• Email: petlau[@]ous-hf.no
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

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 and/or 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 surgical/invasive procedure

• Stone chemical analysis

• Clinical biochemistry: creatinine/eGFR, 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 dilated/slight/moderate/severe).

• 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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 500
• Est. completion date: March 28, 2028
• Est. primary completion date: November 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Referral for CT due to episode of renal colic and clinical suspicion of urinary stone disease or

• Referral for CT due to new episode of pain in patient with known urinary stone disease

• Age = 18 years

Exclusion Criteria:

• Referral for control CT of asymptomatic patients with known urinary stone disease

• Referral for control CT after treatment

• Referral for control CT for spontaneous passage of stone.

• Lack of informed consent for any reason.

Related Conditions & MeSH terms

• Calculi
• Kidney Calculi
• Kidney Stone
• Obstruction Ureter
• Renal Colic
• Ureter Stone
• Ureteral Obstruction
• Urinary Calculi
• Urinary Stone
• Urolithiasis
• Urosepsis

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Oslo University Hospital

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Comparison of stone diameter from manual segmentation with radiology report	Stone diameter (in mm) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Primary	Comparison of AI-segmentation of stones (DICE-score) with manual segmentation	DICE-score for AI-segmentation of stones, compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Primary	Prospective performance (diagnostic accuracy) of AI detection of ureteral stone (compared to radiology report (gold standard)	Comparison of differences in dicotomous proportions in paired data according to Newcombe	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of stone density from manual segmentation with radiology report	Stone density (in Hounsfield Units) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of distention of renal pelvis from manual segmentation with radiology report	Distention of renal pelvis (in mm) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of AI-segmentation of stones (Hausdorff distance) with manual segmentation	Haussdorff distance for AI-segmentation of stones, compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of AI-segmentation of stones (diagnostic accuracy) with manual segmentation	Diagnostic accuracy for AI-segmentation of stones compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of AI-segmentation of renal pelvis (Dice-score) with manual segmentation	DICE-score for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of AI-segmentation of renal pelvis (Hausdorff distance) with manual segmentation	Hausdorff distance for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of AI-segmentation of renal pelvis (diagnostic accuracy) with manual segmentation	Diagnostic accuracy for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of AI-segmentation of renal parenchyma (DICE-score) with manual segmentation	DICE-score for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of AI-segmentation of renal parenchyma (Hausdorff distance) with manual segmentation	Hausdorff distance for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Comparison of AI-segmentation of renal parenchyma (diagnostic accuracy) with manual segmentation	Diagnostic accuracy for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)	At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary	Prospective performance (diagnostic accuracy) of AI detection of ureteral obstruction (compared to radiology report (gold standard)	Comparison of differences in dicotomous proportions in paired data according to Newcombe	At time of CT examination (inclusion and follow up - expected average 12 weeks)