End-Stage Renal DiseaseAI-TIMENCT06690190
Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs
An observational study of End-Stage Renal Disease, run by Mario Negri Institute for Pharmacological Research at 1 site in Italy.
Trial 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… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- End-Stage Renal Disease
- How many people
- 100 participants
- Where
- 1 siteItaly
- Study dates
- 8 Nov 2024to Nov 2034
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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.
Who may be able to join
- Any kidney disease or
- Healthy kidney
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
Italy1 site
Clinical Research Centre for Rare Diseases Aldo e Cele Daccò
Ranica, BG, 24020
About this study
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.
Read more
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.
Read the 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.
- Conditions
- End-Stage Renal Disease
- Keywords
- kidney, histology, microstructure, artificial intelligence, image processing
- Medical subject headings
- Kidney Failure, Chronic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Patients | Patients with any kidney disease | |
| Healthy subjects | Subjects with normal kidneys |
What is being measured
The primary outcome is the main question the study is designed to answer.
Image processing techniques
Develop novel deep learning-based image processing techniques allowing to characterize kidney microstructure across different pathologies and/or disease stages.
Measured: From image acquisition to study end at 10 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Nov 2024
- Study start8 Nov 2024
- Primary completionNov 2034 (estimated)
- Study completionNov 2034 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for End-Stage Renal DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mario Negri Institute for Pharmacological Research
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Giuseppe Remuzzi, M.D., Study director, Istituto Di Ricerche Farmacologiche Mario Negri
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06690190
- Sponsor's protocol ID
- AI-TIME
- Acronym
- AI-TIME
- Status last verified
- Nov 2024
- First submitted
- 13 Nov 2024
- Last updated
- 15 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 33990804Bouteldja 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.
PubMed 33154175Becker 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.
PubMed 32475607
Study at a glance
From the registry record, last updated by the sponsor on 15 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-based Techniques to Characterize KIdney Microstructure on Histological ImagEs
- ClinicalTrials.gov ID
- NCT06690190
- Acronym / sponsor ID
- AI-TIME / AI-TIME
- Conditions
- End-Stage Renal Disease
- Keywords
- kidney, histology, microstructure, artificial intelligence, image processing
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Mario Negri Institute for Pharmacological ResearchUniversity, hospital or other
- Study start
- 8 Nov 2024 (actual)
- Primary completion
- Nov 2034 (estimated)
- Study completion
- Nov 2034 (estimated)
- First posted
- 15 Nov 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients | – | –Patients with any kidney disease |
| Healthy subjects | – | –Subjects with normal kidneys |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- Any kidney disease or
- Healthy kidney
Key exclusion criteria (1 listed)
- None
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Nov 2024.
- Background: PubMed 33990804van 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.
- Background: PubMed 33154175Bouteldja 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.
- Background: PubMed 32475607Becker 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Giuseppe Remuzzi, M.D. study director
Istituto Di Ricerche Farmacologiche Mario Negri
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of End-Stage Renal Disease, run by Mario Negri Institute for Pharmacological Research at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 15 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Image processing techniques (time frame: From image acquisition to study end at 10 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06690190, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Nov 2024.