Multi-parametric MRI enables non-invasive response assessment in heterogeneous soft-tissue sarcomas, but evaluation of post-treatment changes in MRI parameters requires segmentation of cellular tumour-tissue, which might be expected to respond to treatment, from necrotic/cystic regions and fat. Six supervised Machine-Learning methods were explored using a randomized cross-validation approach, from which a candidate method (automatic Kernel Density Estimation) was selected owing to its high validation accuracy and automatic selection of hyper-parameters. The automatic-KDE method enabled evaluation of post-radiotherapy changes in volumes and ADCs of each tumour component, and provided visual depiction of heterogeneous changes in multi-parametric MR-images.
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