Locally Advanced Gastric Cancer, Tertiary Lymphoid Structures (TLS), Tumor Immune Microenvironment
Conditions
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.
Interventions
OTHERTLS-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
Qun Zhao
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
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
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Overall Survival Predicted by TLS-Informed Machine Learning Model | Up to 5 Years Post-Surgery |
Outcome results
None listed