Lung CancersNCT06632886
AI-Assisted Non-Contrast CT for Multi-Cancer Screening
A study testing AI-Assisted Non-Contrast CT for Multi-Cancer Screening in people with Lung Cancers and Liver Cancer, run by Guo ShiWei at 1 site in China.
Trial summary
Cancer poses a major public health challenge in China. Early detection can improve treatment outcomes and survival rates. In this study, we will conduct a large-scale, prospective, multi-center cohort study to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Lung Cancers, Liver Cancer, Gastric Cancers
- What is being tested?
- AI-Assisted Non-Contrast CT for Multi-Cancer Screening
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000,000 participants
- Where
- 1 siteChina
- Study dates
- 7 Oct 2024to 7 Oct 2027
- Phase
- Interventional
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Subject is able and willing to provide informed consent and sign an informed consent form.
- Subject has undergone an abdominal or chest non-contrast CT scan.
Who cannot join
- Subject has been diagnosed with one of the following cancers within the last five years: lung, liver, stomach, colon, esophageal, pancreatic, or breast cancer;
- Subject has any medical condition that contraindicates high-resolution MRI/CT/Endoscopy;
- Subject cannot be followed up or is participating in other clinical trials.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Changhai Hospital
Shanghai, Shanghai Municipality, 200433
Wang Beilei, M.D., 86-13774238083, lilly_wang@126.com
Jin Gang, M.D.
About this study
Official title: A Prospective Cohort Study Evaluating the Utility of Artificial Intelligence-Assisted Non-Contrast Computed Tomography for Multi-Cancer Screening in Asymptomatic Individuals Undergoing Routine Health Examinations
Cancer poses a major public health challenge in China. Early detection can improve treatment outcomes and survival rates. In this study, we will conduct a large-scale, prospective, multi-center cohort study to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening.
The study aims to enroll 1 million asymptomatic participants undergoing routine health examinations, using an AI imaging model based on non-contrast CT to detect seven cancers such as lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancers. Positive cases will be required to be referred to Shanghai Changhai Hospital for further imaging and care based on National Comprehensive Cancer Network (NCCN) and American College of Radiology (ACR) guidelines. The goal is to assess the AI model's diagnostic performance for seven cancer types, especially for early-stage, resectable tumors.
Read the detailed description
Cancer has become a major public health issue in China, seriously affecting population health, the economy, and social development. In 2022, there were an estimated 4.82 million new cancer cases and 2.57 million cancer-related deaths. Lung cancer, liver cancer, gastric cancer, colorectal cancer, esophageal cancer, pancreatic cancer, and breast cancer are the seven leading causes of cancer-related mortality. A successful earlier detection strategy would allow patients to receive timely interventions, improve treatment outcomes, enhance overall survival, and reduce the complexity and cost of treatment.
In this study, we will conduct a large-scale, prospective, multi-center cohort study, aiming to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening. The population consists of individuals who have undergone non-contrast abdominal or chest CT scans at Meinian Onehealth Health Examination Center or Shanghai Changhai Health Examination Center, with an expected enrollment of 1 million participants. A multi-cancer screening model via non-contrast CT, developed by Alibaba DAMO Academy, will be integrated into the PACS system of health examination centers. The imaging AI model will be used to automatically detect various cancerous lesions, including lung cancer, liver cancer, gastric cancer, colorectal cancer, esophageal cancer, pancreatic cancer, and breast cancer. Subjects identified with positive lesions by the AI model will be required to be referred to Shanghai Changhai Hospital for further imaging examinations (such as contrast-enhanced CT, MRI, Endoscopy, etc.) to confirm the final disease status and formulate a treatment plan. Additionally, the medical team should follow care pathways developed based on guidelines from NCCN and ACR, and if necessary, patients will be directed to the multidisciplinary team (MDT) clinic for specific cancer types to determine the diagnostic procedures. The ultimate goal of this study is to comprehensively assess the diagnostic performance metrics of the AI model for each of the seven cancer types individually. These metrics include, but are not limited to, sensitivity, specificity, and positive/negative predictive value. Particular emphasis will be placed on evaluating the model's efficacy in detecting early-stage, resectable tumors. The overarching aim is to determine whether the implementation of this AI-assisted screening approach could potentially lead to improved overall survival rates through earlier detection and intervention.
- Conditions
- Lung Cancers, Liver Cancer, Gastric Cancers, Colorectal, Cancer, Esophageal Cancer, Pancreatic Cancer
- Being tested
- AI-Assisted Non-Contrast CT for Multi-Cancer Screening
- Keywords
- Screening, Early Diagnosis, Artificial Intelligence, Cancer
- Medical subject headings
- Lung Neoplasms, Liver Neoplasms, Stomach Neoplasms, Colonic Neoplasms, Esophageal Neoplasms, Pancreatic Neoplasms, Breast Neoplasms, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Assisted Non-Contrast CT for Multi-Cancer ScreeningDiagnostic test
Also called AI-MCScreen
Participants identified by the AI model as having potential cancerous lesions, including those suspected of lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer, will be required to undergo blood tests (for tumor markers) and additional imaging studies (such as contrast-enhanced CT, MRI, Endoscopy, etc.) to confirm the diagnosis of cancerous lesions.
| Study group | Role | What they receive |
|---|---|---|
| Health Examination Cohort | Experimental | Asymptomatic participants in routine health examinations receive abdominal or chest non-contrast CT scans, categorized as follows:
1. Meinian cohort
2. Changhai cohort Receives: Diagnostic Test: AI-Assisted Non-Contrast CT for Multi-Cancer Screening |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic yield
Determine the diagnostic performance metrics of the multi-cancer screening model for each of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) independently. The metrics will encompass sensitivity, specificity, positive/negative predictive values, and overall accuracy.
Measured: 3 years
Incidence
Determine the incidence of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) among the health examination cohort.
Measured: 3 years
Resectable rate
Determine the proportion of resectable tumor among detected cases for each of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer).
Measured: 3 years
Secondary outcomes (1)
Survival time
Calculate the survival time of patients diagnosed with the following cancers (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) from the point of diagnosis and treatment initiation.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Oct 2024
- Study start7 Oct 2024 (estimated)
- Primary completion7 Oct 2026 (estimated)
- Study completion7 Oct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Lung CancersStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 1,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Guo ShiWei
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Guo ShiWei, Changhai Hospital
- Investigators
- Jin Gang, M.D., Study chair, Department of general surgery, Changhai Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06632886
- Sponsor's protocol ID
- AI-MCScreen
- Status last verified
- Oct 2024
- First submitted
- 7 Oct 2024
- Last updated
- 9 Oct 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, Li L, Wei W, He J. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024 Feb 2;4(1):47-53. doi: 10.1016/j.jncc.2024.01.006. eCollection 2024 Mar.
PubMed 39036382Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024 Jan-Feb;74(1):12-49. doi: 10.3322/caac.21820. Epub 2024 Jan 17.
PubMed 38230766Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20.
PubMed 37985692Schrag D, Beer TM, McDonnell CH 3rd, Nadauld L, Dilaveri CA, Reid R, Marinac CR, Chung KC, Lopatin M, Fung ET, Klein EA. Blood-based tests for multicancer early detection (PATHFINDER): a prospective cohort study. Lancet. 2023 Oct 7;402(10409):1251-1260. doi: 10.1016/S0140-6736(23)01700-2.
PubMed 37805216Gao Q, Lin YP, Li BS, Wang GQ, Dong LQ, Shen BY, Lou WH, Wu WC, Ge D, Zhu QL, Xu Y, Xu JM, Chang WJ, Lan P, Zhou PH, He MJ, Qiao GB, Chuai SK, Zang RY, Shi TY, Tan LJ, Yin J, Zeng Q, Su XF, Wang ZD, Zhao XQ, Nian WQ, Zhang S, Zhou J, Cai SL, Zhang ZH, Fan J. Unintrusive multi-cancer detection by circulating cell-free DNA methylation sequencing (THUNDER): development and independent validation studies. Ann Oncol. 2023 May;34(5):486-495. doi: 10.1016/j.annonc.2023.02.010. Epub 2023 Feb 26.
PubMed 36849097Klein EA, Richards D, Cohn A, Tummala M, Lapham R, Cosgrove D, Chung G, Clement J, Gao J, Hunkapiller N, Jamshidi A, Kurtzman KN, Seiden MV, Swanton C, Liu MC. Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set. Ann Oncol. 2021 Sep;32(9):1167-1177. doi: 10.1016/j.annonc.2021.05.806. Epub 2021 Jun 24.
PubMed 34176681Hackshaw A, Clarke CA, Hartman AR. New genomic technologies for multi-cancer early detection: Rethinking the scope of cancer screening. Cancer Cell. 2022 Feb 14;40(2):109-113. doi: 10.1016/j.ccell.2022.01.012. Epub 2022 Feb 3.
PubMed 35120599
Study at a glance
From the registry record, last updated by the sponsor on 9 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Cohort Study Evaluating the Utility of Artificial Intelligence-Assisted Non-Contrast Computed Tomography for Multi-Cancer Screening in Asymptomatic Individuals Undergoing Routine Health Examinations
- ClinicalTrials.gov ID
- NCT06632886
- Acronym / sponsor ID
- AI-MCScreen
- Conditions
- Lung Cancers, Liver Cancer, Gastric Cancers, Colorectal, Cancer, Esophageal Cancer, Pancreatic Cancer
- Keywords
- Screening, Early Diagnosis, Artificial Intelligence, Cancer
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,000,000 participants (estimated)
- Lead sponsor
- Guo ShiWeiUniversity, hospital or other
- Study start
- 7 Oct 2024 (estimated)
- Primary completion
- 7 Oct 2026 (estimated)
- Study completion
- 7 Oct 2027 (estimated)
- First posted
- 9 Oct 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.
Cancer poses a major public health challenge in China. Early detection can improve treatment outcomes and survival rates. In this study, we will conduct a large-scale, prospective, multi-center cohort study to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening.
The study aims to enroll 1 million asymptomatic participants undergoing routine health examinations, using an AI imaging model based on non-contrast CT to detect seven cancers such as lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancers. Positive cases will be required to be referred to Shanghai Changhai Hospital for further imaging and care based on National Comprehensive Cancer Network (NCCN) and American College of Radiology (ACR) guidelines. The goal is to assess the AI model's diagnostic performance for seven cancer types, especially for early-stage, resectable tumors.
Detailed description
Cancer has become a major public health issue in China, seriously affecting population health, the economy, and social development. In 2022, there were an estimated 4.82 million new cancer cases and 2.57 million cancer-related deaths. Lung cancer, liver cancer, gastric cancer, colorectal cancer, esophageal cancer, pancreatic cancer, and breast cancer are the seven leading causes of cancer-related mortality. A successful earlier detection strategy would allow patients to receive timely interventions, improve treatment outcomes, enhance overall survival, and reduce the complexity and cost of treatment.
In this study, we will conduct a large-scale, prospective, multi-center cohort study, aiming to evaluate the utility of AI-assisted non-contrast CT for multi-cancer screening. The population consists of individuals who have undergone non-contrast abdominal or chest CT scans at Meinian Onehealth Health Examination Center or Shanghai Changhai Health Examination Center, with an expected enrollment of 1 million participants. A multi-cancer screening model via non-contrast CT, developed by Alibaba DAMO Academy, will be integrated into the PACS system of health examination centers. The imaging AI model will be used to automatically detect various cancerous lesions, including lung cancer, liver cancer, gastric cancer, colorectal cancer, esophageal cancer, pancreatic cancer, and breast cancer. Subjects identified with positive lesions by the AI model will be required to be referred to Shanghai Changhai Hospital for further imaging examinations (such as contrast-enhanced CT, MRI, Endoscopy, etc.) to confirm the final disease status and formulate a treatment plan. Additionally, the medical team should follow care pathways developed based on guidelines from NCCN and ACR, and if necessary, patients will be directed to the multidisciplinary team (MDT) clinic for specific cancer types to determine the diagnostic procedures. The ultimate goal of this study is to comprehensively assess the diagnostic performance metrics of the AI model for each of the seven cancer types individually. These metrics include, but are not limited to, sensitivity, specificity, and positive/negative predictive value. Particular emphasis will be placed on evaluating the model's efficacy in detecting early-stage, resectable tumors. The overarching aim is to determine whether the implementation of this AI-assisted screening approach could potentially lead to improved overall survival rates through earlier detection and intervention.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Health Examination Cohort | Experimental | Diagnostic Test: AI-Assisted Non-Contrast CT for Multi-Cancer ScreeningAsymptomatic participants in routine health examinations receive abdominal or chest non-contrast CT scans, categorized as follows: 1. Meinian cohort 2. Changhai cohort |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Subject is able and willing to provide informed consent and sign an informed consent form.
- Subject has undergone an abdominal or chest non-contrast CT scan.
Key exclusion criteria (3 listed)
- Subject has been diagnosed with one of the following cancers within the last five years: lung, liver, stomach, colon, esophageal, pancreatic, or breast cancer;
- Subject has any medical condition that contraindicates high-resolution MRI/CT/Endoscopy;
- Subject cannot be followed up or is participating in other clinical trials.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Assisted Non-Contrast CT for Multi-Cancer ScreeningAlso: AI-MCScreenParticipants identified by the AI model as having potential cancerous lesions, including those suspected of lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer, will be required to undergo blood tests (for tumor markers) and additional imaging studies (such as contrast-enhanced CT, MRI, Endoscopy, etc.) to confirm the diagnosis of cancerous lesions. | Diagnostic test | Health Examination Cohort |
Primary outcome
Diagnostic yield
Time frame: 3 years
Determine the diagnostic performance metrics of the multi-cancer screening model for each of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) independently. The metrics will encompass sensitivity, specificity, positive/negative predictive values, and overall accuracy.
Incidence
Time frame: 3 years
Determine the incidence of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer) among the health examination cohort.
Resectable rate
Time frame: 3 years
Determine the proportion of resectable tumor among detected cases for each of the seven cancer types (lung, liver, gastric, colorectal, esophageal, pancreatic, and breast cancer).
1 secondary outcome
Survival time
Time frame: 3 years
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 9 Oct 2024.
- Background: PubMed 39036382Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, Li L, Wei W, He J. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent. 2024 Feb 2;4(1):47-53. doi: 10.1016/j.jncc.2024.01.006. eCollection 2024 Mar.
- Background: PubMed 38230766Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024 Jan-Feb;74(1):12-49. doi: 10.3322/caac.21820. Epub 2024 Jan 17.
- Background: PubMed 37985692Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20.
- Background: PubMed 37805216Schrag D, Beer TM, McDonnell CH 3rd, Nadauld L, Dilaveri CA, Reid R, Marinac CR, Chung KC, Lopatin M, Fung ET, Klein EA. Blood-based tests for multicancer early detection (PATHFINDER): a prospective cohort study. Lancet. 2023 Oct 7;402(10409):1251-1260. doi: 10.1016/S0140-6736(23)01700-2.
- Background: PubMed 36849097Gao Q, Lin YP, Li BS, Wang GQ, Dong LQ, Shen BY, Lou WH, Wu WC, Ge D, Zhu QL, Xu Y, Xu JM, Chang WJ, Lan P, Zhou PH, He MJ, Qiao GB, Chuai SK, Zang RY, Shi TY, Tan LJ, Yin J, Zeng Q, Su XF, Wang ZD, Zhao XQ, Nian WQ, Zhang S, Zhou J, Cai SL, Zhang ZH, Fan J. Unintrusive multi-cancer detection by circulating cell-free DNA methylation sequencing (THUNDER): development and independent validation studies. Ann Oncol. 2023 May;34(5):486-495. doi: 10.1016/j.annonc.2023.02.010. Epub 2023 Feb 26.
- Background: PubMed 34176681Klein EA, Richards D, Cohn A, Tummala M, Lapham R, Cosgrove D, Chung G, Clement J, Gao J, Hunkapiller N, Jamshidi A, Kurtzman KN, Seiden MV, Swanton C, Liu MC. Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set. Ann Oncol. 2021 Sep;32(9):1167-1177. doi: 10.1016/j.annonc.2021.05.806. Epub 2021 Jun 24.
- Background: PubMed 35120599Hackshaw A, Clarke CA, Hartman AR. New genomic technologies for multi-cancer early detection: Rethinking the scope of cancer screening. Cancer Cell. 2022 Feb 14;40(2):109-113. doi: 10.1016/j.ccell.2022.01.012. Epub 2022 Feb 3.
- Individual participant dataSharing planned: No
Related trials for Lung Cancers
Recruiting
MRD-Guided Postoperative Surveillance and Adaptive Treatment for Resectable Lung Cancer
Lung Neoplasms, Neoadjuvant Therapy
Not yet recruitingPhase 3
Intraarterial Therapies Plus Tislelizumab Plus Lenvatinib Versus Tislelizumab Plus Gemcitabine-Cisplatin in Unresectable Intrahepatic Cholangiocarcinoma
Intrahepatic Cholangiocarcinoma (Icc)
Active, not recruitingPhase 3
A Study to Compare the Efficacy, Safety, Pharmacokinetics, and Immunogenicity Between SB27 and Keytruda in Subjects With Metastatic Non-squamous Non-small Cell Lung Cancer
Non-small Cell Lung Cancer Stage IV
RecruitingObservational study
Label-free Femtosecond Laser Imaging Combined With the Fast Lung Artificial Intelligence Model for Rapid Intraoperative Diagnosis of Lung Surgical Specimens: A Multicentre, Prospective, Parallel-workflow, Non-inferiority Study.
Lung Cancer
RecruitingPhase 3
A Study of Calderasib (MK-1084) Plus Pembrolizumab (MK-3475) in Participants With KRAS G12C Mutant Non-small Cell Lung Cancer (NSCLC) With Programmed Cell Death Ligand 1 (PD-L1) Tumor Proportion Score (TPS) ≥50% (MK-1084-004/KANDLELIT-004)
Non-small Cell Lung Cancer
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.
Jin Gang, M.D. study chair
Department of general surgery, Changhai Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-Assisted Non-Contrast CT for Multi-Cancer Screening in people with Lung Cancers and Liver Cancer, run by Guo ShiWei 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 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: looking for participants now, as of the sponsor's last update on 9 Oct 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?
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?
Contact Wang Beilei, M.D. (86-13774238083, lilly_wang@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic yield (time frame: 3 years). 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 NCT06632886, which also lists 7 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 9 Oct 2024.