Alzheimer's Disease DiagnosisNCT05020626
Modelling Tau Distribution From DTI With Generative Adversarial Network for Alzheimer's Disease Diagnosis
An observational study of Alzheimer's Disease Diagnosis, run by Chinese University of Hong Kong at 1 site in Hong Kong.
Trial summary
The most significant impact of this project is to propose for the first time a novel generative adversarial network (GAN), as one kind of deep learning architecture, to automatically generate synthetic PET images reflecting tau deposition, from brain DTI images. Read more about this study
- Who is it for?
- Ages 55 years and olderHealthy volunteers can take part
- Conditions the study is for
- Alzheimer's Disease Diagnosis
- How many people
- 250 participants
- Where
- 1 siteHong Kong
- Study dates
- 30 Jun 2021to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
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 55 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The data will be collected from a public database, i.e, Alzheimer's Disease Nueroimaging Initiative (ADNI). According to their recruitment protocol at http://adni.loni.usc.edu/methods/documents/, the study enrolls men and women aged 55-90 years across MCI and mild AD dementia participant groups, and control normal aged 65 years with exceptions granted for minority participants.
Who may be able to join
- With the age of 55 years and above
- With brain MRI taken within ±6 months from the date of clinically confirmed diagnosis of AD, MCI or normal cognition.
Who cannot join
- AD with mixed dementia
- Non-AD dementia
- History of severe traumatic brain injury, severe depression, stroke, brain tumors, and incident major systemic illness
Trial locations and contacts
1 site in 1 country.
Hong Kong1 site, 1 recruiting
- Recruiting
The Chinese University of Hong Kong, Prince of Wale Hospital
Hong Kong, Shatin
Chiu Wing CHU, MBChB, MD, (852)35052299, winniechu@cuhk.edu.hk
About this study
Official title: Modelling Tau Deposition and Distribution From Diffusion Tensor Imaging With Generative Adversarial Network for Alzheimer's Disease Diagnosis
The most significant impact of this project is to propose for the first time a novel generative adversarial network (GAN), as one kind of deep learning architecture, to automatically generate synthetic PET images reflecting tau deposition, from brain DTI images. If successful, this framework will become the most state-of-the-art approach to simulate the stereotypical pattern of intracerebral tau accumulation and distribution in vivo.
Synthetic tau-PET images via DTI, possessing overwhelming superiority in radiation-free, non-invasiveness and cost-effectiveness, will potentially serve as one of alternative modalities of PET in detecting tau-load and probably outperform PET on accessibility, generalizability, and availability in future, making it much more attractive in clinical application. A big conceptual shift may occur preferring a fire-new tau-PET simulated via DTI.
The DTI data-driven deep learning framework to be created in this project will constitute an accurate, robust, clinically applicable and explainable tool to efficiently categorize the subjects into tau-burden positive and tau-burden negative cases, which will undoubtedly contribute to both clinical and research activities.
- Conditions
- Alzheimer's Disease Diagnosis
- Keywords
- Generative Adversarial Network, Tau, Diffusion Tensor Imaging
- Medical subject headings
- Pick Disease of the Brain
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 |
|---|---|---|
| Controls | Not described | |
| Mild cognitive impairment | Not described | |
| AD dementia | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Structural similarity index to measure the similarity between synthetic image and ground truth for 20% of data in testing set
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Aug 2021
- Study start30 Jun 2021
- Primary completion29 Jun 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (33)Papers the sponsor has linked to this study.
- Other trials for Alzheimer's Disease DiagnosisStudies of the same condition, open and under way.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Professor Winnie W.C. Chu, Chinese University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05020626
- Sponsor's protocol ID
- 2021.243
- Status last verified
- Aug 2024
- First submitted
- 4 Aug 2021
- Last updated
- 22 Aug 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Lane CA, Hardy J, Schott JM. Alzheimer's disease. Eur J Neurol. 2018 Jan;25(1):59-70. doi: 10.1111/ene.13439. Epub 2017 Oct 19.
PubMed 28872215Alzheimer's Association. 2016 Alzheimer's disease facts and figures. Alzheimers Dement. 2016 Apr;12(4):459-509. doi: 10.1016/j.jalz.2016.03.001.
PubMed 27570871Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol. 2011 Sep;10(9):819-28. doi: 10.1016/S1474-4422(11)70072-2. Epub 2011 Jul 19.
PubMed 21775213Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR Jr, Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y, Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):280-92. doi: 10.1016/j.jalz.2011.03.003. Epub 2011 Apr 21.
PubMed 21514248Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM. Alzheimer's disease. Lancet. 2016 Jul 30;388(10043):505-17. doi: 10.1016/S0140-6736(15)01124-1. Epub 2016 Feb 24.
PubMed 26921134Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC, Dickson DW, Duyckaerts C, Frosch MP, Masliah E, Mirra SS, Nelson PT, Schneider JA, Thal DR, Thies B, Trojanowski JQ, Vinters HV, Montine TJ. National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimers Dement. 2012 Jan;8(1):1-13. doi: 10.1016/j.jalz.2011.10.007.
PubMed 22265587Khan TK. An Algorithm for Preclinical Diagnosis of Alzheimer's Disease. Front Neurosci. 2018 Apr 30;12:275. doi: 10.3389/fnins.2018.00275. eCollection 2018.
PubMed 29760644Mito R, Raffelt D, Dhollander T, Vaughan DN, Tournier JD, Salvado O, Brodtmann A, Rowe CC, Villemagne VL, Connelly A. Fibre-specific white matter reductions in Alzheimer's disease and mild cognitive impairment. Brain. 2018 Mar 1;141(3):888-902. doi: 10.1093/brain/awx355.
PubMed 29309541Espay AJ, Vizcarra JA, Marsili L, Lang AE, Simon DK, Merola A, Josephs KA, Fasano A, Morgante F, Savica R, Greenamyre JT, Cambi F, Yamasaki TR, Tanner CM, Gan-Or Z, Litvan I, Mata IF, Zabetian CP, Brundin P, Fernandez HH, Standaert DG, Kauffman MA, Schwarzschild MA, Sardi SP, Sherer T, Perry G, Leverenz JB. Revisiting protein aggregation as pathogenic in sporadic Parkinson and Alzheimer diseases. Neurology. 2019 Feb 12;92(7):329-337. doi: 10.1212/WNL.0000000000006926.
PubMed 30745444Baker JE, Lim YY, Pietrzak RH, Hassenstab J, Snyder PJ, Masters CL, Maruff P. Cognitive impairment and decline in cognitively normal older adults with high amyloid-beta: A meta-analysis. Alzheimers Dement (Amst). 2016 Oct 18;6:108-121. doi: 10.1016/j.dadm.2016.09.002. eCollection 2017.
PubMed 28239636Hedden T, Oh H, Younger AP, Patel TA. Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology. 2013 Apr 2;80(14):1341-8. doi: 10.1212/WNL.0b013e31828ab35d.
PubMed 23547267Gordon BA, McCullough A, Mishra S, Blazey TM, Su Y, Christensen J, Dincer A, Jackson K, Hornbeck RC, Morris JC, Ances BM, Benzinger TLS. Cross-sectional and longitudinal atrophy is preferentially associated with tau rather than amyloid beta positron emission tomography pathology. Alzheimers Dement (Amst). 2018 Mar 6;10:245-252. doi: 10.1016/j.dadm.2018.02.003. eCollection 2018.
PubMed 29780869Xia C, Makaretz SJ, Caso C, McGinnis S, Gomperts SN, Sepulcre J, Gomez-Isla T, Hyman BT, Schultz A, Vasdev N, Johnson KA, Dickerson BC. Association of In Vivo [18F]AV-1451 Tau PET Imaging Results With Cortical Atrophy and Symptoms in Typical and Atypical Alzheimer Disease. JAMA Neurol. 2017 Apr 1;74(4):427-436. doi: 10.1001/jamaneurol.2016.5755.
PubMed 28241163Bejanin A, Schonhaut DR, La Joie R, Kramer JH, Baker SL, Sosa N, Ayakta N, Cantwell A, Janabi M, Lauriola M, O'Neil JP, Gorno-Tempini ML, Miller ZA, Rosen HJ, Miller BL, Jagust WJ, Rabinovici GD. Tau pathology and neurodegeneration contribute to cognitive impairment in Alzheimer's disease. Brain. 2017 Dec 1;140(12):3286-3300. doi: 10.1093/brain/awx243.
PubMed 29053874Sepulcre J, Grothe MJ, d'Oleire Uquillas F, Ortiz-Teran L, Diez I, Yang HS, Jacobs HIL, Hanseeuw BJ, Li Q, El-Fakhri G, Sperling RA, Johnson KA. Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nat Med. 2018 Dec;24(12):1910-1918. doi: 10.1038/s41591-018-0206-4. Epub 2018 Oct 29.
PubMed 30374196
Study at a glance
From the registry record, last updated by the sponsor on 22 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Modelling Tau Deposition and Distribution From Diffusion Tensor Imaging With Generative Adversarial Network for Alzheimer's Disease Diagnosis
- ClinicalTrials.gov ID
- NCT05020626
- Acronym / sponsor ID
- 2021.243
- Conditions
- Alzheimer's Disease Diagnosis
- Keywords
- Generative Adversarial Network, Tau, Diffusion Tensor Imaging
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Study start
- 30 Jun 2021 (actual)
- Primary completion
- 29 Jun 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 25 Aug 2021 (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 most significant impact of this project is to propose for the first time a novel generative adversarial network (GAN), as one kind of deep learning architecture, to automatically generate synthetic PET images reflecting tau deposition, from brain DTI images. If successful, this framework will become the most state-of-the-art approach to simulate the stereotypical pattern of intracerebral tau accumulation and distribution in vivo.
Synthetic tau-PET images via DTI, possessing overwhelming superiority in radiation-free, non-invasiveness and cost-effectiveness, will potentially serve as one of alternative modalities of PET in detecting tau-load and probably outperform PET on accessibility, generalizability, and availability in future, making it much more attractive in clinical application. A big conceptual shift may occur preferring a fire-new tau-PET simulated via DTI.
The DTI data-driven deep learning framework to be created in this project will constitute an accurate, robust, clinically applicable and explainable tool to efficiently categorize the subjects into tau-burden positive and tau-burden negative cases, which will undoubtedly contribute to both clinical and research activities.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Controls | – | – |
| Mild cognitive impairment | – | – |
| AD dementia | – | – |
Who may qualify
- Ages
- 55 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The data will be collected from a public database, i.e, Alzheimer's Disease Nueroimaging Initiative (ADNI). According to their recruitment protocol at http://adni.loni.usc.edu/methods/documents/, the study enrolls men and women aged 55-90 years across MCI and mild AD dementia participant groups, and control normal aged 65 years with exceptions granted for minority participants.
Key inclusion criteria (2 listed)
- With the age of 55 years and above
- With brain MRI taken within ±6 months from the date of clinically confirmed diagnosis of AD, MCI or normal cognition.
Key exclusion criteria (3 listed)
- AD with mixed dementia
- Non-AD dementia
- History of severe traumatic brain injury, severe depression, stroke, brain tumors, and incident major systemic illness
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
Structural similarity index to measure the similarity between synthetic image and ground truth for 20% of data in testing set
Time frame: Through study completion, an average of 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Aug 2024.
- Background: PubMed 28872215Lane CA, Hardy J, Schott JM. Alzheimer's disease. Eur J Neurol. 2018 Jan;25(1):59-70. doi: 10.1111/ene.13439. Epub 2017 Oct 19.
- Background: PubMed 27570871Alzheimer's Association. 2016 Alzheimer's disease facts and figures. Alzheimers Dement. 2016 Apr;12(4):459-509. doi: 10.1016/j.jalz.2016.03.001.
- Background: PubMed 21775213Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol. 2011 Sep;10(9):819-28. doi: 10.1016/S1474-4422(11)70072-2. Epub 2011 Jul 19.
- Background: PubMed 21514248Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, Iwatsubo T, Jack CR Jr, Kaye J, Montine TJ, Park DC, Reiman EM, Rowe CC, Siemers E, Stern Y, Yaffe K, Carrillo MC, Thies B, Morrison-Bogorad M, Wagster MV, Phelps CH. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):280-92. doi: 10.1016/j.jalz.2011.03.003. Epub 2011 Apr 21.
- Background: PubMed 26921134Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM. Alzheimer's disease. Lancet. 2016 Jul 30;388(10043):505-17. doi: 10.1016/S0140-6736(15)01124-1. Epub 2016 Feb 24.
- Background: PubMed 22265587Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC, Dickson DW, Duyckaerts C, Frosch MP, Masliah E, Mirra SS, Nelson PT, Schneider JA, Thal DR, Thies B, Trojanowski JQ, Vinters HV, Montine TJ. National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease. Alzheimers Dement. 2012 Jan;8(1):1-13. doi: 10.1016/j.jalz.2011.10.007.
- Background: PubMed 29760644Khan TK. An Algorithm for Preclinical Diagnosis of Alzheimer's Disease. Front Neurosci. 2018 Apr 30;12:275. doi: 10.3389/fnins.2018.00275. eCollection 2018.
- Background: PubMed 29309541Mito R, Raffelt D, Dhollander T, Vaughan DN, Tournier JD, Salvado O, Brodtmann A, Rowe CC, Villemagne VL, Connelly A. Fibre-specific white matter reductions in Alzheimer's disease and mild cognitive impairment. Brain. 2018 Mar 1;141(3):888-902. doi: 10.1093/brain/awx355.
- Background: PubMed 30745444Espay AJ, Vizcarra JA, Marsili L, Lang AE, Simon DK, Merola A, Josephs KA, Fasano A, Morgante F, Savica R, Greenamyre JT, Cambi F, Yamasaki TR, Tanner CM, Gan-Or Z, Litvan I, Mata IF, Zabetian CP, Brundin P, Fernandez HH, Standaert DG, Kauffman MA, Schwarzschild MA, Sardi SP, Sherer T, Perry G, Leverenz JB. Revisiting protein aggregation as pathogenic in sporadic Parkinson and Alzheimer diseases. Neurology. 2019 Feb 12;92(7):329-337. doi: 10.1212/WNL.0000000000006926.
- Background: PubMed 28239636Baker JE, Lim YY, Pietrzak RH, Hassenstab J, Snyder PJ, Masters CL, Maruff P. Cognitive impairment and decline in cognitively normal older adults with high amyloid-beta: A meta-analysis. Alzheimers Dement (Amst). 2016 Oct 18;6:108-121. doi: 10.1016/j.dadm.2016.09.002. eCollection 2017.
- Background: PubMed 23547267Hedden T, Oh H, Younger AP, Patel TA. Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology. 2013 Apr 2;80(14):1341-8. doi: 10.1212/WNL.0b013e31828ab35d.
- Background: PubMed 29780869Gordon BA, McCullough A, Mishra S, Blazey TM, Su Y, Christensen J, Dincer A, Jackson K, Hornbeck RC, Morris JC, Ances BM, Benzinger TLS. Cross-sectional and longitudinal atrophy is preferentially associated with tau rather than amyloid beta positron emission tomography pathology. Alzheimers Dement (Amst). 2018 Mar 6;10:245-252. doi: 10.1016/j.dadm.2018.02.003. eCollection 2018.
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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.
The Chinese University of Hong Kong, Prince of Wale Hospital
Hong Kong, Hong Kong
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 Alzheimer's Disease Diagnosis, run by Chinese University of Hong Kong at 1 site in Hong Kong.
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 3 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: status not confirmed, as of the sponsor's last update on 22 Aug 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 Hong Kong. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Structural similarity index to measure the similarity between synthetic image and ground truth for 20% of data in testing set (time frame: Through study completion, an average of 1 year). 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 NCT05020626, which also lists 33 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 22 Aug 2024; AzTrials last refreshed its copy on 28 Oct 2026.