Abstract #3659

Developing a deep learning model to classify normal bone and metastatic bone disease on Whole-Body Diffusion Weighted Imaging

Antonio Candito¹, Matthew D Blackledge¹, Fabio Zugni², Richard Holbrey¹, Sebastian Schäfer³, Matthew R Orton¹, Ana Ribeiro⁴, Matthias Baumhauer³, Nina Tunariu¹, and Dow-Mu Koh¹

¹The Institute of Cancer Research, London, United Kingdom, ²IEO, European Institute of Oncology IRCCS, Milan, Italy, ³Mint Medical, Heidelberg, Germany, ⁴The Royal Marsden NHS Foundation Trust, London, United Kingdom

We employed a deep transfer-learning model to classify whether images from whole-body diffusion-weighted MRI (WBDWI) contain metastatic bone lesions. Our results demonstrate sensitivity/specificity of 0.87/0.89 on 8 test patients, who were not included in the model training. Such a model may accelerate radiological assessment of disease extent from WBDWI, which currently can be cumbersome to interpret due to the large quantity of data (approximately 200-250 images per patient).

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