Kim Gihyeon, Xing Fangxu, Kong Hyoun-Joong, Santarnecchi Emiliano, Shih Helen A, Bortfeld Thomas, El Fakhri Georges, Liu Xiaofeng, Choi Jang-Hwan, Woo Jonghye
Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Ewha Womans University, Seoul, Korea.
Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10981128. Epub 2025 May 12.
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.
准确预测胶质母细胞瘤患者的生存期能够显著有助于个性化治疗方案的制定。虽然术前多模态磁共振成像（MRI）可提供补充信息，但目前的方法受到相对有限的数据限制，并且很大程度上依赖于从分割结果中提取的手工特征。为了解决这些问题，在本研究中，我们提出了一种数据高效的多任务框架，以利用先进的Swin UNETR中的分层分割特征进行生存期预测。通过整合多尺度特征，我们能够捕捉详细的空间信息和全局上下文，同时采用移位窗口机制来保持对三维体积的计算效率和可扩展性。我们通过分割预训练进一步缓解生存数据稀缺问题，同时对特征进行微调以使其与生存预测任务对齐，并通过统计F值进行优化。此外，将年龄信息与提取的特征一起纳入，以提高生存预测性能。通过对BraTS数据集的全面评估，我们证明我们的模型实现了卓越的分割精度和领先的生存预测性能，为胶质母细胞瘤患者的临床预后提供了一个强大的解决方案。
