Inflammatory marker-driven deep learning model for postoperative gastric cancer prognosis

Prognostic prediction following gastric cancer surgery plays a pivotal role in postoperative management, helping to optimize therapeutic strategies and improve patient survival. Standard clinicopathological indicators, including tumor differentiation and lymph node metastasis, continue to serve as the basis for outcome evaluation; however, they do not adequately represent the host's systemic inflammatory response and immunonutritional status, both of which significantly affect tumor progression and postoperative recovery. Systemic inflammatory markers, such as the Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR), have emerged as reliable, noninvasive prognostic indicators. However, the complex and nonlinear interactions among inflammatory, clinical, and demographic variables pose a limitation for traditional statistical methods.

This study proposes a novel deep learning framework that integrates three major components: Gradient-Boosted Decision Tree, Tree-Driven Encoder (TDE), and one-dimensional Convolutional Neural Network (1D-CNN) for postoperative prognostic prediction in gastric cancer. The GBDT module captures intricate dependencies among clinical and inflammatory variables, the TDE transforms tree-based structures into unified binary embeddings, and the 1D-CNN component learns high-level feature representations from these embeddings to predict postoperative prognosis. The model's performance was evaluated using cross-validation and compared with various traditional machine learning algorithms and advanced deep learning architectures for tabular data.

Experimental findings demonstrate that the proposed hybrid framework consistently outperforms both traditional and general deep learning models in predicting postoperative prognosis. By combining tree-based feature structuring with deep representation learning, the model effectively captures nonlinear and hierarchical relationships among systemic inflammatory markers and clinicopathological features. This approach achieves high predictive accuracy, robustness, and generalization capability, particularly in identifying high-risk patients characterized by elevated inflammatory activity. Moreover, the model exhibited stable performance across multiple random seeds and data partitions, confirming its reproducibility and reliability under different experimental conditions.

This study presents a data-driven and interpretable deep learning framework for postoperative prognostic prediction in gastric cancer. By integrating the strengths of gradient-boosted tree modeling and deep neural representation learning, the proposed model provides a more comprehensive understanding of the interplay among inflammation, nutrition, and tumor biology, supporting personalized treatment planning and evidence-based clinical decision-making. Future research will focus on external validation using independent cohorts, real-time clinical application, and enhancing model explainability to facilitate clinical adoption.