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Abstract #0268

The impact of radiomic feature reproducibility on a head and neck cancer radiotherapy response model: a comparison of two common analysis packages

James C Korte1,2, Carlos E Cardenas3, Tomas Kron1,4, Nicholas Hardcastle1,5, Jihong Wang3, Houda Bahig6, Baher Elgohari7, Laurence E Court3, Clifton D Fuller7, and Sweet Ping Ng7,8
1Department of Physical Science, Peter MacCallum Cancer Centre, Melbourne, Australia, 2Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia, 3Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 4Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia, 5Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia, 6Radiation Oncology Department, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada, 7Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States, 8Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomic features is a known issue that is being addressed by standardisation initiatives, but it remains a challenge to interpret previously published radiomic signatures. We investigate the reproducibility of radiomic features calculated with two common software packages and explore the impact of including non-reproducible diffusion features in a head and neck cancer (HNC) radiotherapy response model. Our results demonstrate that equivalent models can be generated from either software, but only when restricting the model to reproducible features identified with a correlation threshold method.

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