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

NCT06979817 | Completed DMC

• 1 other identifier: GC-RAD-AI-2025-03
• Study Type: observational
• Target: 1,200
• Locations: 0 countries
• Sites: N/A

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.

Conditions

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

Outcome Measures

Primary Outcomes (1)

• Overall Survival Predicted by TLS-Informed Machine Learning Model

  Up to 5 Years Post-Surgery

Study Arms (1)

Locally Advanced Gastric Cancer Patients

Other: TLS-Informed Machine Learning Prognostic Model

Interventions

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.

Locally Advanced Gastric Cancer Patients

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

This study will include patients with histologically confirmed locally advanced gastric adenocarcinoma who have undergone curative-intent surgical resection at participating medical centers. The population will consist of both retrospective and prospective cohorts, with all patients having available tumor tissue for TLS analysis and complete clinical, pathological, and follow-up data. The study aims to capture a representative sample of real-world gastric cancer patients, reflecting a diversity of clinical characteristics, treatment modalities, and outcomes.

You may qualify if:

• 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)

You may not qualify if:

• 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

Sponsors & Collaborators

• Qun Zhao: lead

MeSH Terms

Conditions

Tertiary Lymphoid Structures

Condition Hierarchy (Ancestors)

Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: May 12, 2025
• First Posted: May 20, 2025
• Study Start: January 1, 2012
• Primary Completion: January 1, 2024
• Study Completion: January 1, 2024
• Last Updated: May 20, 2025

Record last verified: 2025-05