Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction.
Authors
Affiliations (5)
Affiliations (5)
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, 218 Jixi Road, China.
- Department of The First Clinical Medical College, Anhui Medical University, Hefei, 230022, China.
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, 218 Jixi Road, China. [email protected].
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, 218 Jixi Road, China. [email protected].
- The People's Hospital of Hanshan County, Ma'anshan, 238101, China. [email protected].
Abstract
This study developed a 5-year survival prediction model for gastric cancer patients by combining radiomics and deep learning, focusing on CT-based 2D and 3D features of the iliopsoas and erector spinae muscles. Retrospective data from 705 patients across two centers were analyzed, with clinical variables assessed via Cox regression and radiomic features extracted using deep learning. The 2D model outperformed the 3D approach, leading to feature fusion across five dimensions, optimized via logistic regression. Results showed no significant association between clinical baseline characteristics and survival, but the 2D model demonstrated strong prognostic performance (AUC ~ 0.8), with attention heatmaps emphasizing spinal muscle regions. The 3D model underperformed due to irrelevant data. The final integrated model achieved stable predictive accuracy, confirming the link between muscle mass and survival. This approach advances precision medicine by enabling personalized prognosis and exploring 3D imaging feasibility, offering insights for gastric cancer research.