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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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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
1,200 participants in 1 patient group
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Data sourced from clinicaltrials.gov
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