The 3‑mm peritumoral margin provides optimal prognostic information in soft tissue sarcoma radiomics: a retrospective multi‑sequence MRI study

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Abstract Background To construct an integrated prognostic model combining intra‑ and peritumoral radiomics with MRI features for predicting progression‑free survival (PFS) in soft tissue sarcomas (STS). Methods This retrospective study included 305 patients with trunk or extremity STS. Radiomic features were extracted from intratumoral and peritumoral regions at expansion distances of 3 mm, 5 mm, and 10 mm on both contrast‑enhanced T1‑weighted (CE‑T1WI) and fat‑suppressed T2‑weighted (FS‑T2WI) sequences. Features were selected via ICC, univariate Cox, and LASSO Cox regression. Prognostic models were constructed by integrating Rad‑scores with significant MRI features, and evaluated using C‑index, calibration curves, and decision curve analysis. Subgroup analyses were performed stratified by imaging phenotype (cellular vs. non‑cellular). Correlation analyses between peritumoral Rad‑scores and semantic MRI features were also conducted. Results The 3‑mm peritumoral expansion yielded the highest predictive performance among the three distances. Integrated intratumoral–peritumoral (3 mm) models from both sequences outperformed single‑region models. The 3‑mm margin consistently demonstrated superior performance in both cellular and non‑cellular phenotypes. The final multimodal model, combining dual‑sequence Rad‑scores with peritumoral enhancement, achieved a C‑index of 0.888 in the validation set. Correlation analyses reveal that peritumoral Rad‑scores are most strongly associated with peritumoral T2 hyperintensity, with differential correlation patterns between CE‑T1WI and FS‑T2WI supporting their complementary roles. Conclusions We developed a robust multimodal radiomics model integrating intra‑ and peritumoral (3 mm) features with key MRI characteristics. The optimality of the 3‑mm margin was validated across major STS phenotypes. This non‑invasive tool facilitates personalized risk stratification and may inform adjuvant treatment decisions, supporting clinical translation in STS management.