Monitoring treatment response of soft-tissue sarcomas (STS) following radiotherapy is challenging due to the inherent intratumoral heterogeneity of the disease. Radiomics and deep-learning provide opportunities for the discovery of potent biomarkers of treatment response. Successful response biomarkers must demonstrate good baseline repeatability if they are to be used for personalized treatment. We explore the stability of radiomic features derived from a deep-learning pipeline by determining the pairwise correlation of derived features, and measuring the baseline repeatability of features derived from the Apparent Diffusion-Coefficient maps. We demonstrate that 81/512 features are both independent and stable at repeat baseline measurement.
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