AI-Based Cancer Diagnosis and Prediction Using Electronic Health Records
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 1,000,000
- 试验地点
- 7
- 主要终点
- Area Under the Curve (AUC)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 0 Years 至 90 Years（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •1、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.
排除标准
- •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).
结局指标
主要结局
Area Under the Curve (AUC)
时间窗: 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
时间窗: 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.
次要结局
- Sensitivity (True Positive Rate)(1 year)
- Specificity (True Negative Rate)(1 year)
研究者
Kang Zhang
Chief Scientist
Wenzhou Medical University