Abstract #2432

Characterisation of disease heterogeneity in malignant pleural mesothelioma using mixture modelling of ADC and R2

Lin Cheng¹, Matthew D. Blackledge¹, David J. Collins¹, Nina Tunariu^1,2, Neil P. Jerome¹, Matthew R. Orton¹, Veronica A. Morgan³, Martion O. Leach¹, and Dow-Mu Koh^1,2

¹Division of Radiotherapy and Imaging, Cancer Research UK Cancer Imaging Centre, Institute of Cancer Research, London, United Kingdom, ²Radiology, Royal Marsden Hospital, London, United Kingdom, ³Clinical MRI Unit, Royal Marsden Hospital, London, United Kingdom

Disease heterogeneity in patients with malignant pleural mesothelioma (MPM) makes it challenging to characterise solid disease and assess response following treatment. Computed Diffusion-Weighted MRI (cDWI) provides improved contrast between disease and background tissues, and facilitates total disease segmentation. A mixture modelling of ADC and R2 with semi-automatic segmentation on the cDWI is proposed to assess disease heterogeneity in MPM, with demonstration of its utility on a paired pre/post-treatment dataset. The mixture modelling methodology successfully characterised disease heterogeneity for two MPM patients, and can provide additional quantitative functional disease response characterisation compared with using only a single parameter.

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