Trials / Completed
CompletedNCT06979817
Machine Learning Model Guided by TLS Predicts Survival and Immune Features in Gastric Cancer
TLS-Informed Machine Learning Model Predicts Survival and Immune Landscape in Locally Advanced Gastric Cancer
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 1,200 (actual)
- Sponsor
- Qun Zhao · Academic / Other
- Sex
- All
- Age
- 18 Years – 80 Years
- Healthy volunteers
- —
Summary
This study aims to develop and validate a machine learning model that uses information from tertiary lymphoid structures (TLSs)-specialized immune-related cell clusters found near tumors-to predict survival outcomes and immune characteristics in patients with locally advanced gastric cancer. By analyzing clinical data, pathology, and imaging results, the model may help doctors better understand a patient's prognosis and personalize treatment strategies. The study will also explore how TLS-related immune patterns relate to the effectiveness of certain therapies, potentially offering new insights for immune-based treatment planning.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| OTHER | TLS-Informed Machine Learning Prognostic Model | This intervention involves the development and application of a machine learning-based prognostic model that integrates features derived from tertiary lymphoid structures (TLSs) identified in tumor pathology slides, along with clinical and immunological data, to predict overall survival and immune landscape in patients with locally advanced gastric cancer. The model utilizes digital pathology, image analysis, and advanced computational algorithms to quantify TLS-related characteristics and correlate them with patient outcomes. It is designed to stratify patients into risk groups and provide insight into the tumor immune microenvironment, aiming to support personalized treatment planning. |
Timeline
- Start date
- 2012-01-01
- Primary completion
- 2024-01-01
- Completion
- 2024-01-01
- First posted
- 2025-05-20
- Last updated
- 2025-05-20
Source: ClinicalTrials.gov record NCT06979817. Inclusion in this directory is not an endorsement.