Clinical trial NCT06791473: AI-Driven Cancer Diagnosis and Prediction With EHR
Official title: AI-Based Cancer Diagnosis and Prediction Using Electronic Health Records
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Tumor
Keywords: tumor
Early Disease Prediction
AI-Assisted Diagnosis
Lead sponsor: The Eye Hospital of Wenzhou Medical University
Sponsor class: OTHER
First posted: 2025-01-24
Enrollment: 1000000
Sex: ALL
Minimum age (years): 0
Maximum age (years): 90
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: OTHER
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.

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. As precision medicine becomes increasingly important, the challenge remains to identify cancer at early stages, especially when symptoms are subtle or absent. Recent advancements in artificial intelligence and data analysis techniques have shown great promise in enhancing the diagnostic accuracy and speed of cancer detection. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, imaging, laboratory results, and genetic data. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized treatment options for cancer patients. Ultimately, this system seeks to improve early detection, guide effective treatment strategies, and enhance patient survival rates.

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:

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).

Primary outcomes:
measure: Area Under the Curve (AUC)
timeFrame: 1 year
measure: F1 Score
timeFrame: 1 year
Secondary outcomes:
measure: Sensitivity (True Positive Rate)
timeFrame: 1 year
measure: Specificity (True Negative Rate)
timeFrame: 1 year
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Guangzhou Women and Children's Medical Center
state: Guangdong
city: Guangzhou
country: China
facility: Nanfang Hospital
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
state: Guangdong
city: Guangzhou
country: China
facility: Sun Yat-sen University Cancer Hospital
state: Guangdong
city: Chengdu
country: China
facility: West China Hospital
state: Sichuan
city: Wenzhou
country: China
facility: First Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
city: Wenzhou
country: China
facility: Second Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
Study officials:
References: