Artificial IntelligenceNCT06653478
Development and Demonstration of Intelligent Assessment Based on Multi-modal Information Fusion for Tumor Risk and Diagnosis and Treatment
An observational study of Artificial Intelligence and Deep Learning, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 1 site in China.
Trial summary
To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data… Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Deep Learning, Lung Cancer
- How many people
- 3,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2022to 1 Oct 2026
- 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 to 75 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision
Who may be able to join
- Participants with the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision
- Participants that have signed informed consent.
- Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information.
- Healthy participants with no clinical diagnosis of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision.
Who cannot join
- Participants with primary clinical and pathological data missing.
- Participants lost to follow-up.
- Participants with too poor medical image quality to perform segment and mark ROI accurately
Trial locations and contacts
1 site in 1 country.
Yang Jin contact
China1 site, 1 recruiting
- Recruiting
Wuhan Union Hospital
Wuhan, Hubei, 430000
Juanjuan Xu
About this study
To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data fusion-based technology system using artificial intelligence technology based on database.
Read the detailed description
The main aims are as follows:
- To establish a data platform for multi-modal information of common tumors (lung cancer/pulmonary nodules, stomach and colorectal cancers) : electronic medical records (including routine clinical detection, treatment, outcome), pathological image data, medical imaging (CT, MRI, ultrasound, nuclear medicine, etc.), multiple omics data (genome, transcriptome, and metabolome, proteomics) omics data, etiology and carcinogenic exposure information.
- We will make use of artificial intelligence technology to create the multi-modal medical big data cross-analysis technology and the above disease individualized accurate diagnosis and curative effect prediction models. In order to solve the three key problems of multi-modal data fusion mining, such as unbalanced, small sample size, and poor interpretability, we will establish an artificial intelligence recognition algorithm for image images and pathological images, and use image processing and deep learning technologies to mine multi-level depth visual features of image data and pathological data. In addition, we will use bioinformatics analysis algorithms to conduct molecular network mining and functional analysis of molecular markers at the level of multiple omics technologies (pathologic, genomic, transcriptome, metabolome, proteome, etc.).
- Conditions
- Artificial Intelligence, Deep Learning, Lung Cancer, Lung; Node, Stomach Cancer, Colon Cancer
- Being tested
- No Intervention
- Medical subject headings
- Lung Neoplasms, Stomach Neoplasms, Colonic Neoplasms, Neoplasms, Second Primary
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No InterventionOther
No Intervention
| Study group | Role | What they receive |
|---|---|---|
| Lung cancer group | Participants with lung cancer/pulmonary nodules Receives: Other: No Intervention | |
| Stomach cancer group | Participants with Stomach cancer/Stomach lesion Receives: Other: No Intervention | |
| Colorectal cancer group | Participants with Colorectal cancer/Colorectal lesion Receives: Other: No Intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule)
The outcome of clinical diagnosis of patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).
① Benign nodule
② Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.
Measured: 2022-2026
The outcome of clinical diagnosis of suspected patients with colorectal cancer or lesion (Benign/Malignant).
① Benign
② Malignant
Measured: 2022-2026
Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD).
The treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer follows The Response Evaluation Criteria In Solid Tumors (RECIST version 1.1) from the World Health Organization (WHO). The evaluation index is as follows.
CR (complete response): Disappearance of all target lesions and reduction in the short axis measurement of all pathologic lymph nodes to ≤10 mm.
PR (partial response): 30% decrease in the sum of the longest diameter of the target lesions compared with baseline.
PD (progressive disease):≥20% increase of at least 5 mm in the sum of the longest diameter of the target lesions compared with the smallest sum of the longest diameter recorded OR The appearance of new lesions, including those detected by FDG-PET (fludeoxyglucose positron emission tomography).
SD (stable disease): Neither PR nor PD.
Measured: 2022-2026
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Oct 2024
- Study start1 Jun 2022
- Primary completion1 Oct 2025 (estimated)
- Study completion1 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyThe 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
- 3,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yang Jin, Principal investigator, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06653478
- Sponsor's protocol ID
- Jin-BT&IT
- Status last verified
- Sep 2024
- First submitted
- 20 Oct 2024
- Last updated
- 22 Oct 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Demonstration of Intelligent Assessment Based on Multi-modal Information Fusion for Tumor Risk and Diagnosis and Treatment
- ClinicalTrials.gov ID
- NCT06653478
- Acronym / sponsor ID
- Jin-BT&IT
- Conditions
- Artificial Intelligence, Deep Learning, Lung Cancer, Lung; Node, Stomach Cancer, Colon Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyUniversity, hospital or other
- Study start
- 1 Jun 2022 (actual)
- Primary completion
- 1 Oct 2025 (estimated)
- Study completion
- 1 Oct 2026 (estimated)
- First posted
- 22 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.
To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data fusion-based technology system using artificial intelligence technology based on database.
Detailed description
The main aims are as follows:
- To establish a data platform for multi-modal information of common tumors (lung cancer/pulmonary nodules, stomach and colorectal cancers) : electronic medical records (including routine clinical detection, treatment, outcome), pathological image data, medical imaging (CT, MRI, ultrasound, nuclear medicine, etc.), multiple omics data (genome, transcriptome, and metabolome, proteomics) omics data, etiology and carcinogenic exposure information.
- We will make use of artificial intelligence technology to create the multi-modal medical big data cross-analysis technology and the above disease individualized accurate diagnosis and curative effect prediction models. In order to solve the three key problems of multi-modal data fusion mining, such as unbalanced, small sample size, and poor interpretability, we will establish an artificial intelligence recognition algorithm for image images and pathological images, and use image processing and deep learning technologies to mine multi-level depth visual features of image data and pathological data. In addition, we will use bioinformatics analysis algorithms to conduct molecular network mining and functional analysis of molecular markers at the level of multiple omics technologies (pathologic, genomic, transcriptome, metabolome, proteome, etc.).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Lung cancer group | – | Other: No InterventionParticipants with lung cancer/pulmonary nodules |
| Stomach cancer group | – | Other: No InterventionParticipants with Stomach cancer/Stomach lesion |
| Colorectal cancer group | – | Other: No InterventionParticipants with Colorectal cancer/Colorectal lesion |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision
Key inclusion criteria (4 listed)
- Participants with the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision
- Participants that have signed informed consent.
- Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information.
- Healthy participants with no clinical diagnosis of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision.
Key exclusion criteria (3 listed)
- Participants with primary clinical and pathological data missing.
- Participants lost to follow-up.
- Participants with too poor medical image quality to perform segment and mark ROI accurately
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 |
|---|---|---|
| No InterventionNo Intervention | Other | Colorectal cancer group, Lung cancer group, Stomach cancer group |
Primary outcome
The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule)
Time frame: 2022-2026
The outcome of clinical diagnosis of patients with lung cancer/pulmonary nodular (Benign/Malignant nodule). ① Benign nodule ② Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.
The outcome of clinical diagnosis of suspected patients with colorectal cancer or lesion (Benign/Malignant).
Time frame: 2022-2026
① Benign ② Malignant
Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD).
Time frame: 2022-2026
The treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer follows The Response Evaluation Criteria In Solid Tumors (RECIST version 1.1) from the World Health Organization (WHO). The evaluation index is as follows. CR (complete response): Disappearance of all target lesions and reduction in the short axis measurement of all pathologic lymph nodes to ≤10 mm. PR (partial response): 30% decrease in the sum of the longest diameter of the target lesions compared with baseline. PD (progressive disease):≥20% increase of at least 5 mm in the sum of the longest diameter of the target lesions compared with the smallest sum of the longest diameter recorded OR The appearance of new lesions, including those detected by FDG-PET (fludeoxyglucose positron emission tomography). SD (stable disease): Neither PR nor PD.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Oct 2024.
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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.
Yang Jin contact
Yang Jin principal investigator
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
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 Artificial Intelligence and Deep Learning, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology 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 4 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 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Juanjuan Xu (+86 15827125538, whuhjy@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule) (time frame: 2022-2026). 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?
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 Oct 2024.