Radiomics and Image Segmentation of Urinary Stones by Artificial Intelligence
Conditions: Urinary Stone, Renal Colic, Obstruction Ureter, Urosepsis, Urolithiasis, Ureter Stone, Kidney Stone
Sponsor: Oslo University Hospital

Radiomics and Image Segmentation of Urinary Stones by Artificial 
Intelligence (RISUS-AI) 
 
Study protocol and Statistical Analysis Plan 
 
Principal Investigator: Peter Lauritzen 
 
This document is a translated version of the document reviewed by 
the Regional Committee for medical and health research ethics in 
South-East Norway and approved March 12 2024. 
 
Translated document date: April 30 2024 
 
 
 

Abstract: 
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, we aim 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. 
Background: 
Prevalence: 
In industrialized countries, 5-13% of the population experiences 
acute renal colic caused by kidney stones. The prevalence of 
urolithiasis is increasing, with approximately 10-15% of individuals 
experiencing recurrent attacks. Kidney stones cause significant 
morbidity due to pain, obstruction, or infection. Different chemical 
compositions of kidney stones, such as calcium oxalate, calcium 
phosphate, uric acid, and struvite, can impact treatment. 
Treatment: 
Conservative (non-surgical) treatment, such as lifestyle advice, 
dietary changes, or medication, may be sufficient for small, 

uncomplicated stones. Surgical intervention may be necessary for 
larger stones or those causing complications, with various treatment 
methods available. 
Examination: 
Computed tomography (CT) plays a significant role in the 
examination, monitoring, and selection of treatment strategies. The 
CT protocol used for the assessment and follow-up of these patients 
is a low-dose scan from the upper pole of the kidney to the pelvic 
floor, without intravenous contrast medium. The presence of kidney 
stones is indicated, and stone burden is typically assessed, along with 
signs of complications such as obstruction. 
Segmentation and Radiomics: 
Image segmentation involves annotating specific objects in images, 
such as anatomical structures or pathologies like kidney stones. 
Through segmentation, large amounts of quantitative data can be 
extracted from radiological images, often referred to as "radiomics." 
Artificial Intelligence: 
Deep learning (DL) using convolutional neural networks (CNN) is a 
form of artificial intelligence (AI) that mimics the structure of neurons 
in the human brain. This technology is well-suited for processing 
large amounts of image data and has revolutionized automated 
image segmentation. Manual segmentation data is suitable for 
developing an AI tool for fully automated segmentation of the same 
structures. These methods show great potential for medical 
applications but are still not well explored and exploited. Integrating 
advanced AI techniques, we aim to offer enhanced image solutions 
that not only improve diagnostic accuracy but also advance the field 
of radiomics in kidney stone disease. 

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: 
Inclusion and Exclusion: 
Inclusion criteria: 
• Newly occurring colic pain and clinical suspicion of kidney stones or 
• Known kidney stone with new/increasing symptoms 
• Age ≥ 18 years 
• Exclusion criteria: 
• Controls of asymptomatic patients with known kidney stone 
disease or 
• Follow-up after treatment of kidney stone disease or 
• Controls for spontaneous stone passage (before URS) or 
• Lack of informed consent, regardless of reason. 
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. 
Endpoints: 

• Paper I: Descriptive statistics for manual segmentation data and 
comparison with clinical radiology report. 
• 
Null hypothesis: There is no significant difference in diameter 
or density of stone between manual segmentation and 
clinical radiology report. 
• 
Analysis: Paired T-test, or Wilcoxon rank-sum test for non-
normally distributed data. 
• Paper II: Validation of AI segmentation of stone, renal pelvis, and 
renal parenchyma against manually segmented gold standard. 
• 
Null hypothesis: There is no difference between manual 
segmentation and AI segmentation of stone, renal pelvis, 
and renal parenchyma. 
• 
Analysis: DICE score, Hausdorff distance, sensitivity/specificity 
(of AI segmentation against manual gold standard), 
comparison of segmented volume and largest diameter. 
• Paper III: Prospective application and clinical utility of AI tool 
(including baseline and follow-up CT) compared to clinical 
radiology report. Diagnostic accuracy for: ureteral stone and 
obstruction. 
• 
Null hypothesis: There is no difference in diagnostic accuracy 
for ureteral stone and obstruction. 
• 
Analysis: Comparison of difference in dichotomous 
proportions in paired data (Newcombe). 
Prediction of spontaneous passage is reported with descriptive data 
as there is no gold standard for comparison.