Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs (AI-TIME)

November 14, 2024 updated by: Mario Negri Institute for Pharmacological Research

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.

Study Overview

Status

Active, not recruiting

Conditions

End-Stage Renal Disease

Detailed Description

Morphology-based histopathological analysis of kidney tissue plays a key role in the diagnosis and therapeutic decisions of many kidney diseases. To date, histopathological analysis is mainly performed qualitatively, by visual inspection, requiring highly trained expert pathologists.

Histopathologic findings are often scored by pathologists using semiquantitative diagnostic classification scales, such as the Oxford classification of IgA nephropathy, or disease severity scales. Despite such scoring systems, histopathological analysis remains semi-quantitative, time consuming, and highly operator-dependent. Manual techniques have been proposed to quantitatively assess kidney microstructure on histological images, showing potential to monitor disease progression and response to therapy in chronic kidney disease (CKD). As an example, peritubular interstitial volume, responsible for crucial endocrine functions and undergoing significant, albeit reversible, expansion in CKD, has been recently quantified on kidney biopsy specimens by point counting on each frame. Despite allowing accurate quantification, these manual techniques are labour-intensive and operator dependent. Fast and objective quantitative assessment of kidney microstructure would be highly desirable.

The digitalisation of histological images, same as for diagnostic images, has made it possible to benefit from advanced image analysis techniques allowing identification and segmentation of relevant histopathological structures, and quantitative assessment of tissue microstructure.

In the recent years, Artificial Intelligence (AI) and, in particular, Deep Learning (DL) techniques have shown promise for (semi)automated segmentation of relevant morphological structures on histological images, limiting the need for expert operators, ensuring reproducibility and massively reducing the time demand. Convolutional neural networks (CNNs) have recently demonstrated outstanding performance in image segmentation tasks, also in the medical field. In particular, the so-called U-Nets, consisting of a contracting and an expanding path, have become increasingly popular since first used. Few studies, so far, have used CNNs to investigate kidney microstructure on histological images. Hermsen et al. used CNNs for multi-class segmentation of histological images from kidney biopsies [8]. A similar study aimed to develop a CNN for segmentation of mouse renal tissue structures, such as glomeruli, tubules, arteries, and veins, based on densely annotated images from different renal diseases and various animal species.

Despite these promising preliminary efforts, the high heterogeneity of morphological patterns poses challenges to the generalizability of the segmentation techniques. Automated DL-based methods able to accurately segment and quantify relevant morphological structures on histological kidney images from patients with different kidney pathologies and/or different disease stage would be highly desirable.

Study Type

Observational

Enrollment (Estimated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

Italy
- BG
  - Ranica, BG, Italy, 24020
    - Clinical Research Centre for Rare Diseases Aldo e Cele Daccò

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

Child
Adult
Older Adult

Accepts Healthy Volunteers

Yes

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.

Description

Inclusion Criteria:

Any kidney disease or
Healthy kidney

Exclusion Criteria:

None

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Number of groups / cohorts

Cohorts and Interventions

Group / Cohort
Patients Patients with any kidney disease
Healthy subjects Subjects with normal kidneys

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Image processing techniques Time Frame: From image acquisition to study end at 10 years	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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Mario Negri Institute for Pharmacological Research

Investigators

Study Director: Giuseppe Remuzzi, M.D., Istituto di Ricerche Farmacologiche Mario Negri IRCCS

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 8, 2024

Primary Completion (Estimated)

November 1, 2034

Study Completion (Estimated)

November 1, 2034

Study Registration Dates

First Submitted

November 13, 2024

First Submitted That Met QC Criteria

November 14, 2024

First Posted (Actual)

November 15, 2024

Study Record Updates

Last Update Posted (Actual)