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Machine Learning Model Guided by TLS Predicts Survival and Immune Features in Gastric Cancer

NCT06979817 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1200

Last updated 2025-05-20

No results posted yet for this study

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

  • Locally Advanced Gastric Cancer
  • Tumor Immune Microenvironment
  • Tertiary Lymphoid Structures (TLS)

Interventions

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.

Sponsors & Collaborators

  • Qun Zhao

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2012-01-01
Primary Completion
2024-01-01
Completion
2024-01-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06979817 on ClinicalTrials.gov