Deep Learning-Derived CT Body Composition Enhances Survival Risk Stratification Beyond the TNM System in Locally Advanced Gastric Cancer: A Multi-Omics Cohort Study

Background and study aims:

Gastric carcinoma remains the fifth most common malignancy and the second leading cause of cancer-related mortality worldwide. For patients with locally advanced disease, standard treatment includes radical gastrectomy with D2 lymphadenectomy followed by (neo)adjuvant chemotherapy and, more recently, immune checkpoint inhibitors (ICIs). Despite these therapeutic advances, outcomes for stage III gastric cancer remain poor, with 5-year overall survival rates below 40%. Considerable variability in prognosis persists even within the same the American Joint Committee on Cancer (AJCC) substage, highlighting the importance of host-related factors such as nutritional status, systemic inflammation, and immune competence in shaping survival.

Computed tomography-based body composition (CTBC) analysis offers an objective means to quantify skeletal muscle, subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT), capturing key dimensions of patient physiology that are not accounted for in traditional staging systems. However, its prognostic value in gastric cancer has been inconsistently reported.

Manual CTBC segmentation is time-consuming and prone to inter-observer variation, limiting its routinely clinical applicability. Advances in deep learning, particularly U-Net-based architectures, enable rapid, automated body composition analysis with high concordance to expert annotations. This innovation greatly supports scalable investigations into the prognostic relevance of adipose distribution in cancer.

Here, the investigators prepare to apply automated CTBC analysis to a homogeneous cohort of 300 patients with AJCC8 stage III gastric cancer to determine whether visceral adiposity-related metrics improve survival risk stratification beyond TNM staging. To clarify the underlying mechanisms, the investigators further integrated plasma metabolomic profiling and tumor immune-metabolic phenotyping. This comprehensive approach aims to delineate the systemic and tumor-intrinsic consequences of fat distribution and identify potential metabolic or immunologic vulnerabilities relevant to patient stratification and therapy.