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Background and purpose: In this work, we validate a texture-based model computed from positron emission tomography (PET) and magnetic resonance imaging (MRI) for the prediction of lung metastases in soft-tissue sarcomas (STS). We explore functional imaging at different treatment time points and evaluate the feasibility of radiotherapy dose painting as a potential treatment strategy for patients with higher metastatic risk. Materials and methods: We acquired fluorodeoxyglucose (FDG)-PET, fluoromisonidazole (FMISO)-PET, diffusion weighting (DW)-MRI and dynamic contrast enhanced (DCE)-MRI data for 18 patients with extremity STS before, during, and after pre-operative radiotherapy. We tested the lung metastases prediction model using pre-treatment images. We evaluated the feasibility of dose painting using volumetric arc therapy (VMAT) via treatment re-planning with a prescription of 50 Gy to the planning target volume (PTV50Gy) and boost doses of 60 Gy to the FDG hypermetabolic gross tumour volume (GTV60Gy) and 65 Gy to the low-perfusion DCE-MRI hypoxic GTV contained within the GTV60Gy (GTV65Gy). Results: The texture-based model for lung metastases prediction reached an area under the curve (AUC), sensitivity, specificity and accuracy of 0.71, 0.75, 0.85 and 0.82, respectively. Dose painting resulted in adequate coverage and homogeneity in the re-planned treatments: D95% to the PTV50Gy, GTV60Gy and GTV65Gy were 50.0 Gy, 60.3 Gy and 65.4 Gy, respectively. Conclusions: Textural biomarkers extracted from FDG-PET and MRI could be useful to identify STS patients that might benefit from dose escalation. The feasibility of treatment planning with double boost levels to intratumoural GTV functional sub-volumes was established. Keywords: Soft-tissue sarcoma, FDG-PET, FMISO-PET, DW-MRI, DCE-MRI, Texture analysis, Dose painting