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Abstract #0034

Test-retest repeatability of data-driven radiomic features derived from a deep-learning model: Diffusion-weighted MRI of soft-tissue sarcoma

Timothy Sum Hon Mun1,2, Imogen Thrussell1,2, Jessica Winfield1,2, Amani Arthur3, David J Collins1, Dow-Mu Koh1, Paul Huang3, Simon J Doran1, Christina Messiou1, and Matthew D Blackledge1
1Division of Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom, 2Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom, 3Institute of Cancer Research, London, United Kingdom


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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