AI-Driven Cancer Diagnosis and Prediction With EHR
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-Based Diagnostic and Prognostic Model.
- Who it may be relevant to
- Registry conditions: Tumor. Basic parameters: 0 years — 90 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
AI-Based Cancer Diagnosis and Prediction Using Electronic Health Records
Overview
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 test 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.
Primary outcome measures
- Area Under the Curve (AUC) [Time frame: 1 year]
- F1 Score [Time frame: 1 year]
Secondary outcome measures (2)
- Sensitivity (True Positive Rate) [Time frame: 1 year]
- Specificity (True Negative Rate) [Time frame: 1 year]
Eligibility criteria
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
- Patients with incomplete or missing key electronic health record data or insufficient follow-up data.
- Individuals with severe cognitive disorders or other terminal illnesses that would prevent meaningful participation.
- Pregnant women (although pediatric cancers are being considered, pregnant women would be excluded for safety reasons).
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
China · 7 centers
- Guangzhou Women and Children's Medical Center — Guangzhou
- Nanfang Hospital — Guangzhou
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University — Guangzhou
- Sun Yat-sen University Cancer Hospital — Guangzhou
- West China Hospital — Chengdu
- First Affiliated Hospital of Wenzhou Medical University — Wenzhou
- Second Affiliated Hospital of Wenzhou Medical University — Wenzhou
Identifiers
NCT: NCT06791473 · Cancer