Autosomal Dominant Polycystic Kidney Disease (ADPKD)AI4PKDNCT06688981
Artificial Intelligence-based Image Processing Methods to Advance the Characterization of Polycystic Kidney Disease
An observational study of Autosomal Dominant Polycystic Kidney Disease (ADPKD), run by Mario Negri Institute for Pharmacological Research at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Autosomal Dominant Polycystic Kidney Disease (ADPKD)
- How many people
- 100 participants
- Where
- 1 siteItaly
- Study dates
- 12 Oct 2024to Oct 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
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- Patients with ADPKD
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 is to develop AI-based image processing methods to advance the characterization of Polycystic Kidney Disease using medical images and associated clinical data, including:
- AI-based fully automatic segmentation techniques for the accurate identification of kidneys, liver, and cysts, with a focus on AI interpretability and robustness;
- 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;
- multiparametric models including image-based radiomic features alongside clinical and laboratory data to stratify ADPKD patients and predict ADPKD progression over time.
The study will also have the secondary aim of validating the novel techniques against gold standard (manual) methods, when available.
Read the detailed description
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent hereditary kidney disease, affecting 12.5 million people worldwide in all ethnic groups. ADPKD is caused by a gene mutation in either PKD1 or PKD2, that leads to the formation and growing of multiple fluid-filled cysts in the kidneys and often the liver, leading to chronic kidney injury and ultimately end-stage renal disease. In ADPKD, kidney function may remain normal for several decades and is therefore not fully informative. The identification of early biomarkers able to accurately monitor and predict disease progression in order to take prompt action and select targeting treatment options is urgently needed. Total kidney volume has been recognized as a prognostic biomarker to select patients for clinical trials and is acknowledged by the scientific community as a relevant biomarker to monitor disease progression and response to therapy, and to predict ADPKD course. Total kidney volume can be quantified using medical images, such as Ultrasound, Computed Tomography, and Magnetic Resonance Imaging (MRI). Renal non-contrast enhanced MRI, denoted by high resolution and with no need for contrast agents or ionizing radiation, is the most suited to monitor total kidney volume progression over time and clearly detect kidney cysts. Since many ADPKD patients also have polycystic livers, total liver and liver cyst volume may provide additional relevant information.
Total kidney, liver, and cyst volume measurements are based on kidney segmentation, which is generally performed by manual contouring, an operator-dependent and time-consuming task requiring dedicated expertise. Automatic or semi-automatic methods for kidney and cysts segmentation have been proposed in the past based on traditional approaches and artificial intelligence (AI) techniques. However, the low explainability and the need of large and curated datasets allowing to obtain accurate and generalized models have so far hampered their wide adoption in clinical research. A fully automatic segmentation method for the accurate identification of kidneys, liver, and cysts would be highly desirable.
Beyond kidney, liver, and cyst volume quantification, the characterization of non-cystic renal tissue may provide additional relevant information on ADPKD pathophysiology. Few years ago, a contrastenhanced CT study revealed the presence of peritubular interstitial fibrosis in the non-cystic component of ADPKD kidneys, that was associated with renal function and its decline over time, confirmed more recently by an independent study on dynamic contrast-enhanced T1-weighted MRI. Advanced image processing techniques, such as radiomics, which aims to compute high throughput information from radiological images for the characterization of tissue spatial heterogeneity, show potential to characterise tissue microstructure. Preliminary attempts on ADPKD patients were performed on T1-weighted and T2-weighted MRI scans. Besides, radiomics could be helpful to build multiparametric stratification and prediction models including image-based features.
- Keywords
- Polycystic kidney disease, medical images, artificial intelligence, image processing, imaging biomarkers, radiomics
- Medical subject headings
- Polycystic Kidney, Autosomal Dominant, Polycystic Kidney Diseases
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 | ADPKD patients |
What is being measured
The primary outcome is the main question the study is designed to answer.
Image-processing methods
Develop AI-based image processing methods using medical images and associated clinical data from ADPKD studies ad repositories
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 posted14 Nov 2024
- Study start12 Oct 2024
- Primary completionOct 2034 (estimated)
- Study completionOct 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 Autosomal Dominant Polycystic Kidney Disease (ADPKD)Studies 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 only
- 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
- NCT06688981
- Sponsor's protocol ID
- AI4PKD
- Acronym
- AI4PKD
- Status last verified
- Nov 2024
- First submitted
- 13 Nov 2024
- Last updated
- 14 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 37798206Kline 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.
PubMed 32940759Kline 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.
PubMed 28532709
Study at a glance
From the registry record, last updated by the sponsor on 14 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-based Image Processing Methods to Advance the Characterization of Polycystic Kidney Disease
- ClinicalTrials.gov ID
- NCT06688981
- Acronym / sponsor ID
- AI4PKD / AI4PKD
- Keywords
- Polycystic kidney disease, medical images, artificial intelligence, image processing, imaging biomarkers, radiomics
- 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
- 12 Oct 2024 (actual)
- Primary completion
- Oct 2034 (estimated)
- Study completion
- Oct 2034 (estimated)
- First posted
- 14 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 is to develop AI-based image processing methods to advance the characterization of Polycystic Kidney Disease using medical images and associated clinical data, including:
- AI-based fully automatic segmentation techniques for the accurate identification of kidneys, liver, and cysts, with a focus on AI interpretability and robustness;
- 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;
- multiparametric models including image-based radiomic features alongside clinical and laboratory data to stratify ADPKD patients and predict ADPKD progression over time.
The study will also have the secondary aim of validating the novel techniques against gold standard (manual) methods, when available.
Detailed description
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent hereditary kidney disease, affecting 12.5 million people worldwide in all ethnic groups. ADPKD is caused by a gene mutation in either PKD1 or PKD2, that leads to the formation and growing of multiple fluid-filled cysts in the kidneys and often the liver, leading to chronic kidney injury and ultimately end-stage renal disease. In ADPKD, kidney function may remain normal for several decades and is therefore not fully informative. The identification of early biomarkers able to accurately monitor and predict disease progression in order to take prompt action and select targeting treatment options is urgently needed. Total kidney volume has been recognized as a prognostic biomarker to select patients for clinical trials and is acknowledged by the scientific community as a relevant biomarker to monitor disease progression and response to therapy, and to predict ADPKD course. Total kidney volume can be quantified using medical images, such as Ultrasound, Computed Tomography, and Magnetic Resonance Imaging (MRI). Renal non-contrast enhanced MRI, denoted by high resolution and with no need for contrast agents or ionizing radiation, is the most suited to monitor total kidney volume progression over time and clearly detect kidney cysts. Since many ADPKD patients also have polycystic livers, total liver and liver cyst volume may provide additional relevant information.
Total kidney, liver, and cyst volume measurements are based on kidney segmentation, which is generally performed by manual contouring, an operator-dependent and time-consuming task requiring dedicated expertise. Automatic or semi-automatic methods for kidney and cysts segmentation have been proposed in the past based on traditional approaches and artificial intelligence (AI) techniques. However, the low explainability and the need of large and curated datasets allowing to obtain accurate and generalized models have so far hampered their wide adoption in clinical research. A fully automatic segmentation method for the accurate identification of kidneys, liver, and cysts would be highly desirable.
Beyond kidney, liver, and cyst volume quantification, the characterization of non-cystic renal tissue may provide additional relevant information on ADPKD pathophysiology. Few years ago, a contrastenhanced CT study revealed the presence of peritubular interstitial fibrosis in the non-cystic component of ADPKD kidneys, that was associated with renal function and its decline over time, confirmed more recently by an independent study on dynamic contrast-enhanced T1-weighted MRI. Advanced image processing techniques, such as radiomics, which aims to compute high throughput information from radiological images for the characterization of tissue spatial heterogeneity, show potential to characterise tissue microstructure. Preliminary attempts on ADPKD patients were performed on T1-weighted and T2-weighted MRI scans. Besides, radiomics could be helpful to build multiparametric stratification and prediction models including image-based features.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients | – | –ADPKD patients |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Patients with ADPKD
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 methods
Time frame: From image acquisition to study end at 10 years
Develop AI-based image processing methods using medical images and associated clinical data from ADPKD studies ad repositories
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 14 Nov 2024.
- Background: PubMed 37798206He 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.
- Background: PubMed 32940759Kline 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.
- Background: PubMed 28532709Kline 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.
- 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 Autosomal Dominant Polycystic Kidney Disease (ADPKD), 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 1 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 14 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 methods (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 NCT06688981, 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 14 Nov 2024.