## Protocol Section
### Identification Module
**NCT ID:** NCT06412900

**Acronym:** RISUS_AI

**Brief Title:** Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

**Official Title:** Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence

#### Organization Study ID Info
**ID:** 31347039
#### Organization
**Class:** OTHER
**Full Name:** Oslo University Hospital
#### Secondary ID Infos
**Domain:** Regional Committees for Medical Research Ethics (in Norway)
**ID:** 660399
**Type:** OTHER
### Status Module
#### Completion Date
**Date:** 2028-03-28

**Type:** ESTIMATED

#### Expanded Access Info
#### Last Update Post Date
**Date:** 2024-05-14

**Type:** ACTUAL

**Last Update Submit Date:** 2024-05-08

**Overall Status:** NOT_YET_RECRUITING

#### Primary Completion Date
**Date:** 2025-11-01

**Type:** ESTIMATED

#### Start Date
**Date:** 2024-05-15

**Type:** ESTIMATED

**Status Verified Date:** 2024-05

#### Study First Post Date
**Date:** 2024-05-14
**Type:** ACTUAL
**Study First Submit Date:** 2024-04-30
**Study First Submit QC Date:** 2024-05-08
### Sponsor Collaborators Module

#### Lead Sponsor

**Class:** OTHER

**Name:** Oslo University Hospital

#### Responsible Party

**Investigator Affiliation:** Oslo University Hospital

**Investigator Full Name:** Peter Mæhre Lauritzen

**Investigator Title:** Principal investigator

**Type:** PRINCIPAL_INVESTIGATOR

### Oversight Module

**Is FDA Regulated Device:** False

**Is FDA Regulated Drug:** False

**Oversight Has DMC:** False

### Description Module

**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.

### Conditions Module

**Conditions:**

- Urinary Stone

- Renal Colic

- Obstruction Ureter

- Urosepsis

- Urolithiasis

- Ureter Stone

- Kidney Stone

**Keywords:**

- Urolithiasis

- Computed tomography

- Image segmentation

- Ureteral obstruction

- Artificial intelligence

### Design Module

#### Design Info

**Observational Model:** COHORT

**Time Perspective:** PROSPECTIVE

#### Enrollment Info

**Count:** 500

**Type:** ESTIMATED

**Patient Registry:** True

**Study Type:** OBSERVATIONAL

**Target Duration:** 3 Months

### Arms Interventions Module

#### Arm Group 1

**Description:** Newly occurring colic pain and clinical suspicion of kidney stones or known kidney stone with new/increasing symptoms.

Age ≥ 18 years

**Label:** Adults investigated with CT for suspected urinary stone disease

### Outcomes Module

#### Primary Outcomes

**Description:** Stone diameter (in mm) compared between manual segmentation and radiology report (paired t-test or wilcoxon rank sum test if non-normally distributed data)

**Measure:** Comparison of stone diameter from manual segmentation with radiology report

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** DICE-score for AI-segmentation of stones, compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of stones (DICE-score) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** Comparison of differences in dicotomous proportions in paired data according to Newcombe

**Measure:** Prospective performance (diagnostic accuracy) of AI detection of ureteral stone (compared to radiology report (gold standard)

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

#### Secondary Outcomes

**Description:** 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)

**Measure:** Comparison of stone density from manual segmentation with radiology report

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** 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)

**Measure:** Comparison of distention of renal pelvis from manual segmentation with radiology report

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** Haussdorff distance for AI-segmentation of stones, compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of stones (Hausdorff distance) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** Diagnostic accuracy for AI-segmentation of stones compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of stones (diagnostic accuracy) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** DICE-score for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of renal pelvis (Dice-score) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** Hausdorff distance for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of renal pelvis (Hausdorff distance) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** Diagnostic accuracy for AI-segmentation of renal pelvis compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of renal pelvis (diagnostic accuracy) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** DICE-score for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of renal parenchyma (DICE-score) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** Hausdorff distance for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of renal parenchyma (Hausdorff distance) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** Diagnostic accuracy for AI-segmentation of renal parenchyma compared to manual segmenation (gold standard)

**Measure:** Comparison of AI-segmentation of renal parenchyma (diagnostic accuracy) with manual segmentation

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

**Description:** Comparison of differences in dicotomous proportions in paired data according to Newcombe

**Measure:** Prospective performance (diagnostic accuracy) of AI detection of ureteral obstruction (compared to radiology report (gold standard)

**Time Frame:** At time of CT examination (inclusion and follow up - expected average 12 weeks)

### Eligibility Module

**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.

**Minimum Age:** 18 Years

**Sampling Method:** NON_PROBABILITY_SAMPLE

**Sex:** ALL

**Standard Ages:**

- ADULT

- OLDER_ADULT

**Study Population:** Patients referred for CT for new/acute episode of renal colic and suspicion of /or known urinary stone disease.

### Contacts Locations Module

#### Central Contacts

**Contact 1:**

**Email:** petlau@ous-hf.no

**Name:** Peter M. Lauritzen, MD, PhD

**Phone:** +4795248249

**Role:** CONTACT

#### Overall Officials

**Official 1:**

**Affiliation:** Oslo University Hospital

**Name:** Peter M. Lauritzen, MD, PhD

**Role:** PRINCIPAL_INVESTIGATOR

### IPD Sharing Statement Module

**IPD Sharing:** NO

## Document Section

### Large Document Module

#### Large Docs

- Date: 2024-04-30

- Filename: Prot_SAP_000.pdf

- Has ICF: False

- Has Protocol: True

- Has SAP: True

- Label: Study Protocol and Statistical Analysis Plan

- Size: 250787

- Type Abbrev: Prot_SAP

- Upload Date: 2024-05-08T09:19

## Derived Section

### Condition Browse Module - Ancestors

- ID: D000020763

- Term: Pathological Conditions, Anatomical

- ID: D000053040

- Term: Nephrolithiasis

- ID: D000007674

- Term: Kidney Diseases

- ID: D000014570

- Term: Urologic Diseases

- ID: D000052776

- Term: Female Urogenital Diseases

- ID: D000005261

- Term: Female Urogenital Diseases and Pregnancy Complications

- ID: D000091642

- Term: Urogenital Diseases

- ID: D000052801

- Term: Male Urogenital Diseases

- ID: D000010146

- Term: Pain

- ID: D000009461

- Term: Neurologic Manifestations

- ID: D000014515

- Term: Ureteral Diseases

### Condition Browse Module - Browse Branches

- Abbrev: BXS

- Name: Urinary Tract, Sexual Organs, and Pregnancy Conditions

- Abbrev: BC23

- Name: Symptoms and General Pathology

- Abbrev: All

- Name: All Conditions

- Abbrev: BC16

- Name: Diseases and Abnormalities at or Before Birth

- Abbrev: BC10

- Name: Nervous System Diseases

### Condition Browse Module - Browse Leaves

- ID: M10693

- Name: Kidney Calculi

- Relevance: HIGH

- As Found: Kidney Stone

- ID: M27126

- Name: Nephrolithiasis

- Relevance: LOW

- As Found: Unknown

- ID: M5399

- Name: Calculi

- Relevance: HIGH

- As Found: Stone

- ID: M27103

- Name: Urolithiasis

- Relevance: HIGH

- As Found: Urinary Stone

- ID: M17295

- Name: Urinary Calculi

- Relevance: HIGH

- As Found: Urinary Stone

- ID: M17267

- Name: Ureteral Obstruction

- Relevance: HIGH

- As Found: Obstruction Ureter

- ID: M28610

- Name: Renal Colic

- Relevance: HIGH

- As Found: Renal Colic

- ID: M6313

- Name: Colic

- Relevance: LOW

- As Found: Unknown

- ID: M17264

- Name: Ureteral Calculi

- Relevance: LOW

- As Found: Unknown

- ID: M27125

- Name: Ureterolithiasis

- Relevance: LOW

- As Found: Unknown

- ID: M22519

- Name: Pathological Conditions, Anatomical

- Relevance: LOW

- As Found: Unknown

- ID: M10698

- Name: Kidney Diseases

- Relevance: LOW

- As Found: Unknown

- ID: M17319

- Name: Urologic Diseases

- Relevance: LOW

- As Found: Unknown

- ID: M2875

- Name: Urogenital Diseases

- Relevance: LOW

- As Found: Unknown

- ID: M27093

- Name: Female Urogenital Diseases

- Relevance: LOW

- As Found: Unknown

- ID: M14127

- Name: Pregnancy Complications

- Relevance: LOW

- As Found: Unknown

- ID: M8399

- Name: Female Urogenital Diseases and Pregnancy Complications

- Relevance: LOW

- As Found: Unknown

- ID: M27095

- Name: Male Urogenital Diseases

- Relevance: LOW

- As Found: Unknown

- ID: M13066

- Name: Pain

- Relevance: LOW

- As Found: Unknown

- ID: M12404

- Name: Neurologic Manifestations

- Relevance: LOW

- As Found: Unknown

- ID: M17265

- Name: Ureteral Diseases

- Relevance: LOW

- As Found: Unknown

### Condition Browse Module - Meshes

- ID: D000007669

- Term: Kidney Calculi

- ID: D000052878

- Term: Urolithiasis

- ID: D000014545

- Term: Urinary Calculi

- ID: D000014517

- Term: Ureteral Obstruction

- ID: D000002137

- Term: Calculi

- ID: D000056844

- Term: Renal Colic

### Misc Info Module

- Version Holder: 2024-05-31
