Subregional Radiomics Analysis on Multiparametric MRI for Evaluating Lymphovascular Invasion and Survival in Gastric Cancer: A Multicenter Study

BACKGROUND: Accurate preoperative assessment of lymphovascular invasion (LVI) remains challenging due to the high heterogeneity of gastric cancer (GC). PURPOSE: To evaluate the feasibility of a subregion-based radiomics model using multiparametric MRI (mpMRI) for preoperative evaluation of LVI and to further assess its prognostic value. STUDY TYPE: Retrospective. SUBJECTS: A total of 878 GC patients from four centers: 313 training, 133 internal test, and 432 external validation cases. FIELD STRENGTH/SEQUENCE: 1.5 T and 3 T/mpMRI including T2-weighted imaging (FSE/TSE), diffusion-weighted imaging (SS-EPI), and contrast-enhanced T1-weighted imaging (FFE/VIBE). ASSESSMENT: The fuzzy c-means clustering was applied to subregion generation after manual segmentation. The subregional radiomics model was established using LVI-related features from a four-step extracted pipeline, with logistic regression, random forest, and support vector machine algorithms. The corresponding intra-tumoral subregion (ITS) index for each patient was obtained from the optimal subregional model. Subsequently, a combined model incorporating the ITS index and independent clinical characteristics was developed. Performance was further validated in test and validation cohorts. Additionally, the prognostic utility for overall survival (OS) and disease-free survival (DFS) was assessed in the follow-up cohort. STATISTICAL TESTS: Model area under the receiver operating characteristic curves (AUCs) was compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Kaplan-Meier survival analyses were conducted for prognostic evaluation. p < 0.05 was considered statistically significant. RESULTS: Pathological LVI-positive was detected in 448 (51.0%) patients. The combined model demonstrated satisfactory discrimination of LVI, achieving AUCs of 0.814 (training), 0.769 (test), and 0.758-0.783 (validation), outperforming the optimal subregional model with positive NRI and IDI across all cohorts. Furthermore, the ITS index maintained a significant association with OS (HR 33.50) and DFS (HR 30.00). DATA CONCLUSION: The combined model, which integrated the ITS index derived from subregional radiomics with clinical factors, demonstrated robust performance in evaluating both LVI and survival outcomes in GC patients. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 3.