Routine monitoring of response to therapy in patients with glioma greatly benefits from using volumetrics quantified from lesion segmentation. Yet, the vast majority of deep learning models developed for this task have been trained using data from treatment-naïve, newly-diagnosed patients, whose T2-lesions have different appearance on imaging. We found that increasing the proportion of treated patients in training, incorporating a cross-entropy loss term that takes into account the spatial distance from surgical resection cavity and leading tumor edge, and transfer learning from newly-diagnosed to post-treatment imaging domains were effective strategies to improve the generalizability of segmentation of the T2-lesion post-treatment.
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