Incorporating visceral adipose tissue into dual-region deep learning radiomics on CT improves preoperative lymph node metastasis prediction in gastric cancer: A multicenter study.
Authors
Affiliations (6)
Affiliations (6)
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
- Department of Interventional Medicine, the First Affiliated Hospital, Harbin Medical University, Harbin, China.
- Department of General Practice, the Second Affiliated Hospital, Harbin Medical University, Harbin, China.
- Computer Science School of Computing, Engineering & Intelligent Systems, Ulster University, NI, UK.
- Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China. Electronic address: [email protected].
- Department of PET/CT, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China. Electronic address: [email protected].
Abstract
Visceral adipose tissue (VAT) is associated with tumor progression in gastric cancer (GC), but its added value for predicting lymph node metastasis (LNM) remains unclear. This study aimed to construct a DR-DLR model that incorporates CT-based features from both VAT and tumor, with the goal of predicting LNM in GC patients, while investigating its biological relevance. 855 GC patients were retrospectively collected across three clinical centers, divided into a primary cohort, one internal test cohort, and two independent external validation cohorts. An Attention Residual Transformation Network (ARTNet) extracted imaging representations from CT images. Feature representations and clinical variables were integrated using a multilayer perceptron (MLP) to build the DR-DLR model. Model performance, generalizability, prognostic value, and biological relevance were evaluated using internal/external testing, radiologist comparison, survival analysis, and radiogenomics analysis. Incorporation of VAT-derived features improved predictive performance compared with tumor-only models. The DR-DLR model achieved areas under the receiver operating characteristic curve (AUC) of 0.898 (95 % CI: 0.817-0.946), 0.834 (95 % CI: 0.743-0.897), and 0.848 (95 % CI: 0.775-0.901) in the internal and two external test cohorts, respectively. It outperformed junior and senior radiologists in LNM assessment. Overall survival was shorter in high-risk patients than in low-risk. Radiogenomics analysis revealed that the high-risk phenotype was associated with activation of immune-related pathways and increased infiltration of CD4 + T cells, plasma cells, dendritic cells, and mast cells. The DR-DLR model improved LNM prediction, with its high-risk phenotype linked to immune-related molecular alterations, supporting its value for preoperative risk stratification.