OVERALL SURVIVAL PREDICTION OF BRAIN TUMOR PATIENTS WITH MULTIMODAL MRI USING SWIN UNETR

Accurate prediction of glioblastoma patient survival can significantly aid in personalized treatment planning. While pre-operative multimodal magnetic resonance imaging (MRI) offers complementary information, current methods are constrained by relatively limited data and largely rely on hand-crafted features extracted from segmentation results. To address these issues, in this work, we propose a data-efficient multi-task framework to take advantage of hierarchical segmentation features within advanced Swin UNETR for survival prediction. By integrating multi-scale features, we are able to capture detailed spatial information and global context, while employing the shifted window mechanism to maintain computational efficiency and scalability for 3D volumes. We further alleviate survival data scarcity through segmentation pre-training, while the features are fine-tuned to align with the survival prediction task and refined by statistical F-values. In addition, age information is incorporated alongside the extracted features to enhance survival prediction performance. Through comprehensive evaluations on the BraTS dataset, we demonstrate that our model achieves superior segmentation accuracy and state-of-the-art survival prediction performance, offering a robust solution for clinical prognosis in glioblastoma patients.