Machine Learning Model Guided by TLS Predicts Survival and Immune Features in Gastric Cancer
NCT ID: NCT06979817
Last Updated: 2025-05-20
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
1200 participants
OBSERVATIONAL
2012-01-01
2024-01-01
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Locally Advanced Gastric Cancer Patients
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
Underwent curative-intent gastrectomy (with or without neoadjuvant therapy)
Availability of adequate tumor tissue specimens for TLS assessment via digital pathology
Complete baseline clinical, pathological, and follow-up data
Age ≥ 18 years
Written informed consent provided (if prospective study component is included)
Exclusion Criteria
Prior history of other malignancies within the past 5 years, except for adequately treated in situ carcinoma or non-melanoma skin cancer
Incomplete or missing essential clinical, pathological, or survival data
Poor-quality tissue samples not suitable for TLS quantification or digital analysis
Participation in another clinical trial that may interfere with the study outcomes
18 Years
80 Years
ALL
No
Sponsors
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Qun Zhao
OTHER
Responsible Party
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Qun Zhao
Professor
Other Identifiers
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GC-RAD-AI-2025-03
Identifier Type: -
Identifier Source: org_study_id
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