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
1000000 participants
OBSERVATIONAL
2025-01-19
2025-10-01
Brief Summary
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Detailed Description
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To build the foundation for our work, first phase of the project was initiated in 2023, conducting a large-scale retrospective study. This foundational phase involved analyzing comprehensive, multimodal data from approximately 1 million cancer patients. The goal was to identify key patterns and build robust preliminary models.
As precision medicine becomes increasingly important, the challenge remains to identify cancer at early stages, especially when symptoms are subtle or absent. Building on the insights from our initial analysis, the project's second phase was launched in February 2025: a prospective study. This current study aims to develop and validate an AI-assisted decision-making system by integrating multimodal data from electronic health records, imaging, laboratory results, and genetic data in a real-world clinical setting. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized treatment options for cancer patients. Ultimately, through this comprehensive, two-phase approach, this system seeks to improve early detection, guide effective treatment strategies, and enhance patient survival rates.
Conditions
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Study Design
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COHORT
OTHER
Study Groups
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Healthy Cohort
This group consists of individuals without any diagnosed cancer. Participants in this cohort will serve as the control group for comparison to the experimental group. No interventions or treatments will be administered to this cohort, as they represent a baseline of healthy individuals.
AI-Based Diagnostic and Prognostic Model
This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, imaging data, and genetic information, to predict the risk of cancer. The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of cancer risks. By analyzing historical health data, the model aims to predict potential cancer developments, improving early detection and treatment outcomes.
Tumor Cohort
This group consists of individuals diagnosed with cancer, including various types. Participants in this cohort will serve as the experimental group for evaluating the effectiveness of the early prediction model in identifying cancer risks and improving diagnostic accuracy.
AI-Based Diagnostic and Prognostic Model
This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, imaging data, and genetic information, to predict the risk of cancer. The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of cancer risks. By analyzing historical health data, the model aims to predict potential cancer developments, improving early detection and treatment outcomes.
Interventions
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AI-Based Diagnostic and Prognostic Model
This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, imaging data, and genetic information, to predict the risk of cancer. The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of cancer risks. By analyzing historical health data, the model aims to predict potential cancer developments, improving early detection and treatment outcomes.
Eligibility Criteria
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Inclusion Criteria
2\. Individuals without severe cognitive impairments or conditions that would prevent them from providing informed consent or participating in the study.
3\. Parents or guardians must provide informed consent for minors, while adult participants must provide informed consent for themselves.
Exclusion Criteria
2. Individuals with severe cognitive disorders or other terminal illnesses that would prevent meaningful participation.
3. Pregnant women (although pediatric cancers are being considered, pregnant women would be excluded for safety reasons).
0 Years
90 Years
ALL
Yes
Sponsors
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The Eye Hospital of Wenzhou Medical University
OTHER
Responsible Party
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Kang Zhang
Chief Scientist
Locations
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Guangzhou Women and Children's Medical Center
Guangzhou, Guangdong, China
Nanfang Hospital
Guangzhou, Guangdong, China
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
Guangzhou, Guangdong, China
Sun Yat-sen University Cancer Hospital
Guangzhou, Guangdong, China
West China Hospital
Chengdu, Sichuan, China
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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Cancer
Identifier Type: -
Identifier Source: org_study_id
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