Clinical trial NCT06412900: Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
Official title: Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Urinary Stone
Renal Colic
Obstruction Ureter
Urosepsis
Urolithiasis
Ureter Stone
Kidney Stone
Keywords: Urolithiasis
Computed tomography
Image segmentation
Ureteral obstruction
Artificial intelligence
Lead sponsor: Oslo University Hospital
Sponsor class: OTHER
First posted: 2024-05-14
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients referred for CT for new/acute episode of renal colic and suspicion of /or known urinary stone disease.

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.

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 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Comparison of stone diameter from manual segmentation with radiology report
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of stones (DICE-score) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Prospective performance (diagnostic accuracy) of AI detection of ureteral stone (compared to radiology report (gold standard)
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Secondary outcomes:
measure: Comparison of stone density from manual segmentation with radiology report
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of distention of renal pelvis from manual segmentation with radiology report
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of stones (Hausdorff distance) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of stones (diagnostic accuracy) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of renal pelvis (Dice-score) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of renal pelvis (Hausdorff distance) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of renal pelvis (diagnostic accuracy) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of renal parenchyma (DICE-score) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of renal parenchyma (Hausdorff distance) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Comparison of AI-segmentation of renal parenchyma (diagnostic accuracy) with manual segmentation
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
measure: Prospective performance (diagnostic accuracy) of AI detection of ureteral obstruction (compared to radiology report (gold standard)
timeFrame: At time of CT examination (inclusion and follow up - expected average 12 weeks)
Other outcomes:
Locations:
Study officials:
affiliation: Oslo University Hospital
name: Peter M. Lauritzen, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: