Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment
NCT ID: NCT05426135
Last Updated: 2022-06-21
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
3000 participants
OBSERVATIONAL
2022-06-01
2026-10-31
Brief Summary
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Detailed Description
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1. 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.
2. 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
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Lung cancer group
Participants with lung cancer/pulmonary nodules
No interventions assigned to this group
Stomach cancer group
Participants with Stomach cancer/Stomach lesion
No interventions assigned to this group
Colorectal cancer group
Participants with Colorectal cancer/Colorectal lesion
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
2. Participants that have signed informed consent.
3. Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information.
4. Healthy participants with no clinical diagnosis of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision.
Exclusion Criteria
2. Participants lost to follow-up.
3. Participants with too poor medical image quality to perform segment and mark ROI accurately
18 Years
75 Years
ALL
Yes
Sponsors
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Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
OTHER
Responsible Party
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Yang Jin
Professor
Locations
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Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, China
Countries
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Central Contacts
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Facility Contacts
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Yang Jin, MD
Role: primary
Other Identifiers
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Jin_cancer risk
Identifier Type: -
Identifier Source: org_study_id
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