Physics in medicine and biology
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Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. ⋯ The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of [Formula: see text] in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters ([Formula: see text]), with an average AUC of [Formula: see text]. Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.