Tumor
Conditions
Keywords
tumor, Early Disease Prediction, AI-Assisted Diagnosis
Brief summary
This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing cancer, leveraging multimodal health data.
Detailed description
Cancer diagnosis and early detection are crucial for improving patient outcomes and survival rates. Early identification of cancers and appropriate intervention can significantly impact treatment success and prognosis. In clinical practice, oncologists often need to integrate a variety of patient data-including medical history, laboratory test results, imaging data such as CT scans and MRIs, and genetic markers-to make an accurate diagnosis and develop a personalized treatment plan. 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.
Interventions
DIAGNOSTIC_TESTAI-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.
Sponsors
The Eye Hospital of Wenzhou Medical University
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
0 Years to 90 Years
Healthy volunteers
Yes
Inclusion criteria
1、Patients with comprehensive electronic health records (EHRs), including medical history, laboratory test results, imaging data, and genetic data (if available). 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
1. Patients with incomplete or missing key electronic health record data or insufficient follow-up data. 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).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Curve (AUC) | 1 year | AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1). |
| F1 Score | 1 year | The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity (True Positive Rate) | 1 year | Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders. |
| Specificity (True Negative Rate) | 1 year | Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative. |
Countries
China
Contacts
Primary ContactFei Liu, MD
Outcome results
None listed