Artificial Intelligence-based Image Processing Methods to Advance the Characterization of Polycystic Kidney Disease

NCT06688981 | Active Not Recruiting

• 1 other identifier: AI4PKD
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

The primary aim of this observational exploratory study is to develop AI-based image processing methods to advance the characterization of Polycystic Kidney Disease using medical images and associated clinical data, including:

1. 1.AI-based fully automatic segmentation techniques for the accurate identification of kidneys, liver, and cysts, with a focus on AI interpretability and robustness;
2. 2.advanced AI-based image processing techniques allowing to identify new imaging biomarkers, including through the use of radiomics, to characterize ADPKD tissue microstructure and therefore stage the disease and monitor and predict disease progression and response to therapy;
3. 3.multiparametric models including image-based radiomic features alongside clinical and laboratory data to stratify ADPKD patients and predict ADPKD progression over time.

Conditions

Autosomal Dominant Polycystic Kidney Disease (ADPKD)

Keywords

Polycystic kidney disease; medical images; artificial intelligence; image processing; imaging biomarkers; radiomics

Outcome Measures

Primary Outcomes (1)

• Image-processing methods

  Develop AI-based image processing methods using medical images and associated clinical data from ADPKD studies ad repositories

  From image acquisition to study end at 10 years

Study Arms (1)

Patients

ADPKD patients

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

For this study the following pseudonymised medical images and associated clinical data will be used: 1. Images and clinical data acquired in the context of ADPKD studies promoted by IRFMN 2. Images and clinical data coming from the CYSTic1 study repository, required in accordance with CYSTic1 Sponsor (University of Sheffield, UK) guidance 3. Images and clinical data coming from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) public dataset (https://repository.niddk.nih.gov/studies/crisp1/), required in accordance with the CRISP guidance.

You may qualify if:

• Patients with ADPKD

You may not qualify if:

• None

Sponsors & Collaborators

• Mario Negri Institute for Pharmacological Research: lead

Study Sites (1)

Clinical Research Centre for Rare Diseases Aldo e Cele Daccò

Ranica, BG, 24020, Italy

Location

Related Publications (3)

• He X, Hu Z, Dev H, Romano DJ, Sharbatdaran A, Raza SI, Wang SJ, Teichman K, Shih G, Chevalier JM, Shimonov D, Blumenfeld JD, Goel A, Sabuncu MR, Prince MR. Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning. Acad Radiol. 2024 Mar;31(3):889-899. doi: 10.1016/j.acra.2023.09.009. Epub 2023 Oct 3.

  PMID: 37798206 BACKGROUND

• Kline TL, Edwards ME, Fetzer J, Gregory AV, Anaam D, Metzger AJ, Erickson BJ. Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal-dominant polycystic kidney disease. Abdom Radiol (NY). 2021 Mar;46(3):1053-1061. doi: 10.1007/s00261-020-02748-4. Epub 2020 Sep 17.

  PMID: 32940759 BACKGROUND

• Kline TL, Korfiatis P, Edwards ME, Bae KT, Yu A, Chapman AB, Mrug M, Grantham JJ, Landsittel D, Bennett WM, King BF, Harris PC, Torres VE, Erickson BJ; CRISP Investigators. Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease. Kidney Int. 2017 Nov;92(5):1206-1216. doi: 10.1016/j.kint.2017.03.026. Epub 2017 May 20.

  PMID: 28532709 BACKGROUND

Biospecimen

Retention: SAMPLES WITHOUT DNA

Pseudonymised medical images and associated clinical data

MeSH Terms

Conditions

Polycystic Kidney, Autosomal Dominant; Polycystic Kidney Diseases

Condition Hierarchy (Ancestors)

Kidney Diseases, Cystic; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Abnormalities, Multiple; Congenital Abnormalities; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Ciliopathies; Genetic Diseases, Inborn

Study Officials

• Giuseppe Remuzzi, M.D.

  Istituto Di Ricerche Farmacologiche Mario Negri

  STUDY DIRECTOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 13, 2024
• First Posted: November 14, 2024
• Study Start: October 12, 2024
• Primary Completion (Estimated): October 1, 2034
• Study Completion (Estimated): October 1, 2034
• Last Updated: November 14, 2024

Record last verified: 2024-11

Locations

IT (1)