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

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.

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Key information

Conditions

Locally Advanced Gastric Cancer Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Tertiary Lymphoid Structures Tertiary Lymphoid Structures (TLS) Tumor Immune Microenvironment

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Histologically confirmed locally advanced gastric adenocarcinoma (clinical stage cT2-T4 and/or N+)

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

Distant metastases at the time of diagnosis or surgery (M1 stage)

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

Treatment and study plan

TLS-Informed Machine Learning Prognostic Model

Other

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.

Primary outcomes

Overall Survival Predicted by TLS-Informed Machine Learning Model

Time frame: Up to 5 Years Post-Surgery

Sponsors and collaborators

Lead sponsor

Qun Zhao

Other

Registry information

Official study title

TLS-Informed Machine Learning Model Predicts Survival and Immune Landscape in Locally Advanced Gastric Cancer

Important dates

Study start: 2012
Primary completion: 2024
Study completion: 2024
First posted: May 20, 2025
Registry last updated: May 20, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed