AI-Driven Cancer Diagnosis and Prediction With EHR

Официальное название

AI-Based Cancer Diagnosis and Prediction Using Electronic Health Records

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест 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.

Первичные конечные точки

• Area Under the Curve (AUC) [Срок оценки: 1 year]
• F1 Score [Срок оценки: 1 year]

Вторичные конечные точки (2)

• Sensitivity (True Positive Rate) [Срок оценки: 1 year]
• Specificity (True Negative Rate) [Срок оценки: 1 year]

Критерии участия

Критерии включения

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.

Критерии исключения

• 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).

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 7 центров

• Guangzhou Women and Children's Medical Center — Гуанчжоу
• Nanfang Hospital — Гуанчжоу
• Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University — Гуанчжоу
• Sun Yat-sen University Cancer Hospital — Гуанчжоу
• West China Hospital — Чэнду
• First Affiliated Hospital of Wenzhou Medical University — Wenzhou
• Second Affiliated Hospital of Wenzhou Medical University — Wenzhou

Идентификаторы

NCT: NCT06791473 · Cancer

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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