AI-Driven Cancer Diagnosis and Prediction With EHR

NCT06791473 | Recruiting

• 1 other identifier: Cancer
• Study Type: observational
• Target: 1,000,000
• Locations: 1 country
• Sites: 7

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.

Conditions

Tumor

Keywords

tumor; Early Disease Prediction; AI-Assisted Diagnosis

Outcome Measures

Primary Outcomes (2)

• Area Under the Curve (AUC)

  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).

  1 year

• F1 Score

  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.

  1 year

Secondary Outcomes (2)

• Sensitivity (True Positive Rate)

  1 year

• Specificity (True Negative Rate)

  1 year

Study Arms (2)

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.

Diagnostic Test: AI-Based Diagnostic and Prognostic Model

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.

Diagnostic Test: AI-Based Diagnostic and Prognostic Model

Interventions

AI-Based Diagnostic and Prognostic Model DIAGNOSTIC_TEST

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.

Healthy Cohort; Tumor Cohort

Eligibility Criteria

• Age: 0 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of individuals aged 0 to 90 years who have received care at participating study centers. Participants must have comprehensive electronic health records (EHRs) available, including medical history, laboratory test results, imaging data, and genetic information (if available). Both individuals diagnosed with cancer (including pediatric and adult cancers) and healthy individuals with no history of cancer will be included in the study to evaluate the AI-assisted model's diagnostic and predictive capabilities. The study will focus on patients with complete and documented care records from the participating centers, ensuring a diverse cohort for analysis across different age groups and cancer types.

You may qualify if:

• 、Patients with comprehensive electronic health records (EHRs), including medical history, laboratory test results, imaging data, and genetic data (if available).
• \. Individuals without severe cognitive impairments or conditions that would prevent them from providing informed consent or participating in the study.
• \. Parents or guardians must provide informed consent for minors, while adult participants must provide informed consent for themselves.

You may not qualify if:

• 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).

Sponsors & Collaborators

• The Eye Hospital of Wenzhou Medical University: lead

Study Sites (7)

Guangzhou Women and Children's Medical Center

Guangzhou, Guangdong, China

RECRUITING

Nanfang Hospital

Guangzhou, Guangdong, China

RECRUITING

Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University

Guangzhou, Guangdong, China

RECRUITING

Sun Yat-sen University Cancer Hospital

Guangzhou, Guangdong, China

RECRUITING

West China Hospital

Chengdu, Sichuan, China

RECRUITING

First Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

Second Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

MeSH Terms

Conditions

Neoplasms

Central Study Contacts

Fei Liu, MD

CONTACT

+86 13810512704; liufei_2359@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Scientist

Study Record Dates

• First Submitted: January 19, 2025
• First Posted: January 24, 2025
• Study Start: January 19, 2025
• Primary Completion: October 1, 2025
• Study Completion: October 1, 2025
• Last Updated: July 30, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (7)