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

Completed

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

• Registration Number: NCT06979817
• Lead Sponsor: Qun Zhao

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2012-01-01
• Primary Completion: 2024-01-01
• Study Completion: 2024-01-01
• Status Verified: 2025-05
• Study First Submitted: 2025-05-12
• Study First Submitted QC: 2025-05-12
• Last Update Submitted: 2025-05-12
• Study First Posted: 2025-05-20
• Last Update Posted: 2025-05-20

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1200

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Overall Survival Predicted by TLS-Informed Machine Learning Model	Up to 5 Years Post-Surgery