This is quite an innovative study that should lead to a multicentre validation trial. We have developed an FDG-PET/MRI texture-based model for the prediction of lung metastases (LM) in newly diagnosed patients with soft-tissue sarcomas (STSs) using retrospective analysis. In this work, we assess the model performance using a new prospective STS cohort. We also investigate whether incorporating hypoxia and perfusion biomarkers derived from FMISO-PET and DCE-MRI scans can further enhance the predictive power of the model. A total of 66 patients with histologically confirmed STSs were used in this study and divided into two groups: a retrospective cohort of 51 patients (19 LM) used for training the model, and a prospective cohort of 15 patients (two patients with LM, one patient with bone metastases and suspicious lung nodules) for testing the model. In the training phase, a model of four texture features characterising tumour sub-region size and intensity heterogeneities was developed for LM prediction from pre-treatment FDG-PET and MRI scans (T1-weighted, T2-weighted with fat saturation) of the retrospective cohort, using imbalance-adjusted bootstrap statistical resampling and logistic regression multivariable modeling. In the testing phase, this multivariable model was applied to predict the distant metastasis status of the prospective cohort. The predictive power of the obtained model response was assessed using the area under the receiver-operating characteristic curve (AUC). In the exploratory phase of the study, we extracted two heterogeneity metrics from the prospective cohort: the area under the intensity-volume histogram of pre-treatment DCE-MRI volume transfer constant parametric maps and FMISO-PET hypoxia maps (AU-IVH-Ktrans, AU-IVH-FMISO). The impact of the addition of these two individual metrics to the texture-based model response obtained in the testing phase was first investigated using Spearman's correlation (rs), and lastly using logistic regression and leave-one-out cross-validation (LOO-CV) to account for overfitting bias. First, the texture-based model reached an AUC of 0.94, a sensitivity of 1, a specificity of 0.83 and an accuracy of 0.87 when tested in the prospective cohort. In the exploratory phase, the addition of AU-IVH-FMISO did not improve predictive power, yielding a correlation of rs = −0.42 (p = 0.12) with lung metastases, and a relative change in validation AUC of 0% in comparison with the texture-based model response alone in LOO-CV experiments. In contrast, the addition of AU-IVH-Ktrans improved predictive power, yielding a correlation of rs = −0.54 (p = 0.04) with lung metastases, and a change in validation AUC of +10%. Our results demonstrate that texture-based models extracted from pre-treatment FDG-PET and MRI anatomical scans could be successfully used to predict distant metastases in STS cancer. Our results also suggest that the addition of perfusion heterogeneity metrics may contribute to improving model prediction performance