Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs

NCT06690190 | Active Not Recruiting

• 1 other identifier: AI-TIME
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

The primary aim of this observational exploratory study will be to use fully anonymized histological images of kidney human tissue from patients with any kidney disease and normal kidney tissue to develop novel deep learning-based image processing techniques allowing to characterize kidney microstructure across different pathologies and/or disease stages. Secondly, the study will aim at validating the novel techniques against gold standard (manual) methods, when available, and at developing novel histological imaging biomarkers that could support differential diagnosis, staging of the disease, monitoring of disease progression and response to therapy, and prediction of the disease progression. Other exploratory aims will include:

• The use of radiomics techniques to identify disease-specific kidney morphology patterns.
• The implementation of uncertainty quantification techniques, able to increase AI explainability.

Conditions

End-Stage Renal Disease

Keywords

kidney; histology; microstructure; artificial intelligence; image processing

Outcome Measures

Primary Outcomes (1)

• Image processing techniques

  Develop novel deep learning-based image processing techniques allowing to characterize kidney microstructure across different pathologies and/or disease stages.

  From image acquisition to study end at 10 years

Study Arms (2)

Patients

Patients with any kidney disease

Healthy subjects

Subjects with normal kidneys

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study will use fully anonymized histological images of both human renal tissue samples from patients with renal disease and healthy renal tissue, acquired in the context of clinical studies promoted by the Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, via optical microscopy, immunofluorescence or electron microscopy, after appropriate staining.

You may qualify if:

• Any kidney disease or
• Healthy kidney

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)

• van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med. 2021 May;27(5):775-784. doi: 10.1038/s41591-021-01343-4. Epub 2021 May 14.

  PMID: 33990804 BACKGROUND

• Bouteldja N, Klinkhammer BM, Bulow RD, Droste P, Otten SW, Freifrau von Stillfried S, Moellmann J, Sheehan SM, Korstanje R, Menzel S, Bankhead P, Mietsch M, Drummer C, Lehrke M, Kramann R, Floege J, Boor P, Merhof D. Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology. J Am Soc Nephrol. 2021 Jan;32(1):52-68. doi: 10.1681/ASN.2020050597. Epub 2020 Nov 5.

  PMID: 33154175 BACKGROUND

• Becker JU, Mayerich D, Padmanabhan M, Barratt J, Ernst A, Boor P, Cicalese PA, Mohan C, Nguyen HV, Roysam B. Artificial intelligence and machine learning in nephropathology. Kidney Int. 2020 Jul;98(1):65-75. doi: 10.1016/j.kint.2020.02.027. Epub 2020 Apr 1.

  PMID: 32475607 BACKGROUND

Biospecimen

Retention: SAMPLES WITHOUT DNA

Fully anonymized histological images of kidney human tissue.

MeSH Terms

Conditions

Kidney Failure, Chronic

Condition Hierarchy (Ancestors)

Renal Insufficiency, Chronic; Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Giuseppe Remuzzi, M.D.

  Istituto Di Ricerche Farmacologiche Mario Negri

  STUDY DIRECTOR

Study Design

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

Study Record Dates

• First Submitted: November 13, 2024
• First Posted: November 15, 2024
• Study Start: November 8, 2024
• Primary Completion (Estimated): November 1, 2034
• Study Completion (Estimated): November 1, 2034
• Last Updated: November 15, 2024

Record last verified: 2024-11

Locations

IT (1)