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

Status

Indicates the current recruitment status or the expanded access status.
Active, not recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

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

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Estimated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
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

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

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

Cohorts and Interventions

Group / Cohort

Group / Cohort

A group or subgroup of participants in an observational study that is assessed for biomedical or health outcomes.
Patients
Patients with any kidney disease
Healthy subjects
Subjects with normal kidneys

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
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

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Mario Negri Institute for Pharmacological Research

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • 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)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
November 8, 2024

Primary Completion (Estimated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
November 1, 2034

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
November 1, 2034

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
November 13, 2024

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
November 14, 2024

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
November 15, 2024

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
November 15, 2024

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
November 14, 2024

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
November 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • AI-TIME

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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