Bladder Cancer2024-SR-386NCT06565923
Study on the Staging and Prognosis Model of Bladder Cancer
An observational study of Bladder Cancer, run by The First Affiliated Hospital with Nanjing Medical University at 1 site in China.
Trial summary
Firstly, we retrospectively gathered the patient information who compliant with the criteria from 2012 to 2023, encompassing basic information, clinical information, along with MRI images, blood/urine samples, and tissue samples, for conducting relevant analyses of radiomics. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Bladder Cancer
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 18 Mar 2024to 18 Jul 2025
- How long will I take part?
- 1 Year
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 1. Patients with bladder cancer in preoperative examination; 2. Gender is not limited; 3. Age≥ 18 years old; 4. Be able to provide MRI images, pathological data and laboratory examination data before the operation; 5. Agree to provide basic personal clinical information and pathological and imaging data for scientific research use, and sign the informed consent form; 6. Agree to provide monitoring results during follow-up recurrence monitoring;
Who may be able to join
- 1. Patients with bladder cancer in preoperative examination; 2. Gender is not limited; 3. Age≥ 18 years old; 4. Be able to provide MRI images, pathological data and laboratory examination data before the operation; 5. Agree to provide basic personal clinical information and pathological and imaging data for scientific research use, and sign the informed consent form; 6. Agree to provide monitoring results during follow-up recurrence monitoring;
Who cannot join
- 1. Incomplete clinicopathological data; 2. Combined with upper tract urothelial carcinoma or previously diagnosed upper tract urothelial carcinoma; 3. Is participating in the rest of the clinical studies; Unable to cooperate with the relevant examinations of this project, and do not agree to sign the informed consent form.
Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital with Nanjing Medical University
Nanjing, Jiangsu
About this study
Official title: Study on the Staging and Prognosis Model of Bladder Cancer Based on Artificial Intelligence and Multimodal Omics Features
Firstly, we retrospectively gathered the patient information who compliant with the criteria from 2012 to 2023, encompassing basic information, clinical information, along with MRI images, blood/urine samples, and tissue samples, for conducting relevant analyses of radiomics. Subsequently, based on artificial intelligence technology, deep learning and machine learning models were established on the basis of MRI radiomics and pathological histomics. Ultimately, the following research aims were accomplished: 1. Primary research objective: To explore the role of artificial intelligence and multimodal omics features in the staging and prognosis monitoring of bladder cancer. 2. Secondary objective: To explore the correlations among radiomics, case histomics, and test omics.
- Conditions
- Bladder Cancer
- Keywords
- Multimodal omics features, artificial intelligence, staging and prognostic models, Bladder cancer
- Medical subject headings
- Urinary Bladder Neoplasms
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 |
|---|---|---|
| Adult bladder cancer patients with MRI, pathology, and laboratory data provided | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall survival (OS)
Overall survival (OS) is defined as the duration from surgery to death or the date of the last follow-up.
Measured: 2013-
Progression-free survival (PFS)
Progression-free survival (PFS) refers to the time from surgery until disease progression, the date of the last follow-up, or death from causes other than disease recurrence
Measured: 2013-
Recurrence-Free Survival (RFS)
Recurrence-Free Survival (RFS)
Measured: 2013-
Secondary outcomes (2)
Tumor Infiltration Status
Tumor Infiltration Status
Measured: 2013-
Lymph node metastasis status
Lymph node metastasis status
Measured: 2013-
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Aug 2024
- Study start18 Mar 2024
- Primary completion18 Jul 2025 (estimated)
- Study completion18 Jul 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for Bladder CancerStudies of the same condition, open and under way.
- More trials run by The First Affiliated Hospital with Nanjing Medical UniversityThe 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
- Prospective
- Enrolment
- 200 participants (estimated)
- Target duration
- 1 Year
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Suzhou Municipal Hospital, Yixing People's Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Huai an First People Hospital, Jiangsu Province Hospital of Traditional Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, The second affiliated hospital of Xuzhou medical university
- Responsible party
- Principal investigator, Qiang Lv, The First Affiliated Hospital with Nanjing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06565923
- Sponsor's protocol ID
- 2024-SR-386
- Acronym
- 2024-SR-386
- Status last verified
- Aug 2024
- First submitted
- 20 Aug 2024
- Last updated
- 22 Aug 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Ge L, Chen Y, Yan C, Zhao P, Zhang P, A R, Liu J. Study Progress of Radiomics With Machine Learning for Precision Medicine in Bladder Cancer Management. Front Oncol. 2019 Nov 28;9:1296. doi: 10.3389/fonc.2019.01296. eCollection 2019.
PubMed 31850202Tataru OS, Vartolomei MD, Rassweiler JJ, Virgil O, Lucarelli G, Porpiglia F, Amparore D, Manfredi M, Carrieri G, Falagario U, Terracciano D, de Cobelli O, Busetto GM, Del Giudice F, Ferro M. Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel). 2021 Feb 20;11(2):354. doi: 10.3390/diagnostics11020354.
PubMed 33672608Ferro M, de Cobelli O, Musi G, Del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tataru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol. 2022 Jul 4;14:17562872221109020. doi: 10.1177/17562872221109020. eCollection 2022 Jan-Dec.
PubMed 35814914Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954-961. doi: 10.1038/s41591-019-0447-x. Epub 2019 May 20.
PubMed 31110349Liu KL, Wu T, Chen PT, Tsai YM, Roth H, Wu MS, Liao WC, Wang W. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit Health. 2020 Jun;2(6):e303-e313. doi: 10.1016/S2589-7500(20)30078-9.
PubMed 33328124Vente C, Vos P, Hosseinzadeh M, Pluim J, Veta M. Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI. IEEE Trans Biomed Eng. 2021 Feb;68(2):374-383. doi: 10.1109/TBME.2020.2993528. Epub 2021 Jan 20.
PubMed 32396068Wang K, Lu X, Zhou H, Gao Y, Zheng J, Tong M, Wu C, Liu C, Huang L, Jiang T, Meng F, Lu Y, Ai H, Xie XY, Yin LP, Liang P, Tian J, Zheng R. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut. 2019 Apr;68(4):729-741. doi: 10.1136/gutjnl-2018-316204. Epub 2018 May 5.
PubMed 29730602Nishiyama H, Habuchi T, Watanabe J, Teramukai S, Tada H, Ono Y, Ohshima S, Fujimoto K, Hirao Y, Fukushima M, Ogawa O. Clinical outcome of a large-scale multi-institutional retrospective study for locally advanced bladder cancer: a survey including 1131 patients treated during 1990-2000 in Japan. Eur Urol. 2004 Feb;45(2):176-81. doi: 10.1016/j.eururo.2003.09.011.
PubMed 14734003Witjes JA, Bruins HM, Cathomas R, Comperat EM, Cowan NC, Gakis G, Hernandez V, Linares Espinos E, Lorch A, Neuzillet Y, Rouanne M, Thalmann GN, Veskimae E, Ribal MJ, van der Heijden AG. European Association of Urology Guidelines on Muscle-invasive and Metastatic Bladder Cancer: Summary of the 2020 Guidelines. Eur Urol. 2021 Jan;79(1):82-104. doi: 10.1016/j.eururo.2020.03.055. Epub 2020 Apr 29.
PubMed 32360052Xylinas E, Kent M, Kluth L, Pycha A, Comploj E, Svatek RS, Lotan Y, Trinh QD, Karakiewicz PI, Holmang S, Scherr DS, Zerbib M, Vickers AJ, Shariat SF. Accuracy of the EORTC risk tables and of the CUETO scoring model to predict outcomes in non-muscle-invasive urothelial carcinoma of the bladder. Br J Cancer. 2013 Sep 17;109(6):1460-6. doi: 10.1038/bjc.2013.372. Epub 2013 Aug 27.
PubMed 23982601Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022 Jan;72(1):7-33. doi: 10.3322/caac.21708. Epub 2022 Jan 12.
PubMed 35020204Babjuk M, Burger M, Capoun O, Cohen D, Comperat EM, Dominguez Escrig JL, Gontero P, Liedberg F, Masson-Lecomte A, Mostafid AH, Palou J, van Rhijn BWG, Roupret M, Shariat SF, Seisen T, Soukup V, Sylvester RJ. European Association of Urology Guidelines on Non-muscle-invasive Bladder Cancer (Ta, T1, and Carcinoma in Situ). Eur Urol. 2022 Jan;81(1):75-94. doi: 10.1016/j.eururo.2021.08.010. Epub 2021 Sep 10.
PubMed 34511303Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
PubMed 33538338
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
- Study on the Staging and Prognosis Model of Bladder Cancer Based on Artificial Intelligence and Multimodal Omics Features
- ClinicalTrials.gov ID
- NCT06565923
- Acronym / sponsor ID
- 2024-SR-386 / 2024-SR-386
- Conditions
- Bladder Cancer
- Keywords
- Multimodal omics features, artificial intelligence, staging and prognostic models, Bladder cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital with Nanjing Medical UniversityUniversity, hospital or other
- Collaborators
- Suzhou Municipal Hospital, Yixing People's Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Huai an First People Hospital, Jiangsu Province Hospital of Traditional Chinese Medicine, The First Affiliated Hospital of Zhengzhou University, The second affiliated hospital of Xuzhou medical university
- Study start
- 18 Mar 2024 (actual)
- Primary completion
- 18 Jul 2025 (estimated)
- Study completion
- 18 Jul 2025 (estimated)
- First posted
- 22 Aug 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.
Firstly, we retrospectively gathered the patient information who compliant with the criteria from 2012 to 2023, encompassing basic information, clinical information, along with MRI images, blood/urine samples, and tissue samples, for conducting relevant analyses of radiomics. Subsequently, based on artificial intelligence technology, deep learning and machine learning models were established on the basis of MRI radiomics and pathological histomics. Ultimately, the following research aims were accomplished: 1. Primary research objective: To explore the role of artificial intelligence and multimodal omics features in the staging and prognosis monitoring of bladder cancer. 2. Secondary objective: To explore the correlations among radiomics, case histomics, and test omics.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Year
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adult bladder cancer patients with MRI, pathology, and laboratory data provided | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 1. Patients with bladder cancer in preoperative examination; 2. Gender is not limited; 3. Age≥ 18 years old; 4. Be able to provide MRI images, pathological data and laboratory examination data before the operation; 5. Agree to provide basic personal clinical information and pathological and imaging data for scientific research use, and sign the informed consent form; 6. Agree to provide monitoring results during follow-up recurrence monitoring;
Key inclusion criteria (1 listed)
- 1. Patients with bladder cancer in preoperative examination; 2. Gender is not limited; 3. Age≥ 18 years old; 4. Be able to provide MRI images, pathological data and laboratory examination data before the operation; 5. Agree to provide basic personal clinical information and pathological and imaging data for scientific research use, and sign the informed consent form; 6. Agree to provide monitoring results during follow-up recurrence monitoring;
Key exclusion criteria (1 listed)
- 1. Incomplete clinicopathological data; 2. Combined with upper tract urothelial carcinoma or previously diagnosed upper tract urothelial carcinoma; 3. Is participating in the rest of the clinical studies; Unable to cooperate with the relevant examinations of this project, and do not agree to sign the informed consent form.
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
Overall survival (OS)
Time frame: 2013-
Overall survival (OS) is defined as the duration from surgery to death or the date of the last follow-up.
Progression-free survival (PFS)
Time frame: 2013-
Progression-free survival (PFS) refers to the time from surgery until disease progression, the date of the last follow-up, or death from causes other than disease recurrence
Recurrence-Free Survival (RFS)
Time frame: 2013-
Recurrence-Free Survival (RFS)
2 secondary outcomes
Tumor Infiltration Status
Time frame: 2013-
Lymph node metastasis status
Time frame: 2013-
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.
- Results publication: PubMed 31850202Ge L, Chen Y, Yan C, Zhao P, Zhang P, A R, Liu J. Study Progress of Radiomics With Machine Learning for Precision Medicine in Bladder Cancer Management. Front Oncol. 2019 Nov 28;9:1296. doi: 10.3389/fonc.2019.01296. eCollection 2019.
- Results publication: PubMed 33672608Tataru OS, Vartolomei MD, Rassweiler JJ, Virgil O, Lucarelli G, Porpiglia F, Amparore D, Manfredi M, Carrieri G, Falagario U, Terracciano D, de Cobelli O, Busetto GM, Del Giudice F, Ferro M. Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel). 2021 Feb 20;11(2):354. doi: 10.3390/diagnostics11020354.
- Results publication: PubMed 35814914Ferro M, de Cobelli O, Musi G, Del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tataru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol. 2022 Jul 4;14:17562872221109020. doi: 10.1177/17562872221109020. eCollection 2022 Jan-Dec.
- Results publication: PubMed 31110349Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954-961. doi: 10.1038/s41591-019-0447-x. Epub 2019 May 20.
- Results publication: PubMed 33328124Liu KL, Wu T, Chen PT, Tsai YM, Roth H, Wu MS, Liao WC, Wang W. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit Health. 2020 Jun;2(6):e303-e313. doi: 10.1016/S2589-7500(20)30078-9.
- Results publication: PubMed 32396068Vente C, Vos P, Hosseinzadeh M, Pluim J, Veta M. Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI. IEEE Trans Biomed Eng. 2021 Feb;68(2):374-383. doi: 10.1109/TBME.2020.2993528. Epub 2021 Jan 20.
- Results publication: PubMed 29730602Wang K, Lu X, Zhou H, Gao Y, Zheng J, Tong M, Wu C, Liu C, Huang L, Jiang T, Meng F, Lu Y, Ai H, Xie XY, Yin LP, Liang P, Tian J, Zheng R. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut. 2019 Apr;68(4):729-741. doi: 10.1136/gutjnl-2018-316204. Epub 2018 May 5.
- Results publication: PubMed 14734003Nishiyama H, Habuchi T, Watanabe J, Teramukai S, Tada H, Ono Y, Ohshima S, Fujimoto K, Hirao Y, Fukushima M, Ogawa O. Clinical outcome of a large-scale multi-institutional retrospective study for locally advanced bladder cancer: a survey including 1131 patients treated during 1990-2000 in Japan. Eur Urol. 2004 Feb;45(2):176-81. doi: 10.1016/j.eururo.2003.09.011.
- Results publication: PubMed 32360052Witjes JA, Bruins HM, Cathomas R, Comperat EM, Cowan NC, Gakis G, Hernandez V, Linares Espinos E, Lorch A, Neuzillet Y, Rouanne M, Thalmann GN, Veskimae E, Ribal MJ, van der Heijden AG. European Association of Urology Guidelines on Muscle-invasive and Metastatic Bladder Cancer: Summary of the 2020 Guidelines. Eur Urol. 2021 Jan;79(1):82-104. doi: 10.1016/j.eururo.2020.03.055. Epub 2020 Apr 29.
- Results publication: PubMed 23982601Xylinas E, Kent M, Kluth L, Pycha A, Comploj E, Svatek RS, Lotan Y, Trinh QD, Karakiewicz PI, Holmang S, Scherr DS, Zerbib M, Vickers AJ, Shariat SF. Accuracy of the EORTC risk tables and of the CUETO scoring model to predict outcomes in non-muscle-invasive urothelial carcinoma of the bladder. Br J Cancer. 2013 Sep 17;109(6):1460-6. doi: 10.1038/bjc.2013.372. Epub 2013 Aug 27.
- Results publication: PubMed 35020204Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022 Jan;72(1):7-33. doi: 10.3322/caac.21708. Epub 2022 Jan 12.
- Results publication: PubMed 34511303Babjuk M, Burger M, Capoun O, Cohen D, Comperat EM, Dominguez Escrig JL, Gontero P, Liedberg F, Masson-Lecomte A, Mostafid AH, Palou J, van Rhijn BWG, Roupret M, Shariat SF, Seisen T, Soukup V, Sylvester RJ. European Association of Urology Guidelines on Non-muscle-invasive Bladder Cancer (Ta, T1, and Carcinoma in Situ). Eur Urol. 2022 Jan;81(1):75-94. doi: 10.1016/j.eururo.2021.08.010. Epub 2021 Sep 10.
- Individual participant dataSharing planned: Undecided
Related trials for Bladder Cancer
RecruitingPhase 2/3
Testing the Role of DNA Released From Tumor Cells Into the Blood in Guiding the Use of Immunotherapy After Surgical Removal of the Bladder, Kidney, Ureter, and Urethra for Urothelial Cancer Treatment, MODERN Study
Muscle Invasive Bladder Urothelial Carcinoma, Muscle Invasive Renal Pelvis Urothelial Carcinoma
RecruitingPhase 3
A Clinical Trial of Sacituzumab Tirumotecan (Sac-TMT, MK-2870) and Pembrolizumab (MK-3475A) in Participants With Bladder Cancer (MK-2870-055)
Bladder Cancer
Active, not recruitingPhase 2/3
A Study of CTX-009 in Combination With Paclitaxel in Adult Patients With Unresectable Advanced, Metastatic or Recurrent Biliary Tract Cancers (COMPANION-002)
Biliary Tract Cancer, Cholangiocarcinoma
Recruiting
Effect of Cyclic Sighing Breathing Exercise on CRBD, Pain, and Sleep Quality in Patients Undergoing TUR
Transurethral Resection of Bladder Tumor, Transurethral Resection of the Prostate (TURP)
Recruiting
The Vanguard Study: Testing a New Way to Screen for Cancer
Bladder Carcinoma, Breast Carcinoma
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 record lists no contact. Use the official record to reach the sponsor.
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 Bladder Cancer, run by The First Affiliated Hospital with Nanjing Medical University at 1 site in China.
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: 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 China. 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?
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?
Overall survival (OS) (time frame: 2013-). The study lists 3 primary outcomes in all. 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 NCT06565923, which also lists 13 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.