用Vision Transformer直接分析MRI预测胶质母细胞瘤生存期，无需肿瘤分割。
Glioblastoma Overall Survival Prediction With Vision Transformers
- 基于视觉变换器从MRI图像中直接提取特征，跳过传统分割步骤。
- 在BRATS数据集上测试准确率达62.5%，各项指标优于现有最佳模型。
- 适合追求高效、免分割的医学影像AI研究者与临床辅助决策场景。
胶质母细胞瘤是最具侵袭性且常见的脑肿瘤之一，中位生存期为10-15个月。预测总生存期（OS）对个性化治疗策略制定和临床决策至关重要。本研究提出一种新型人工智能方法，利用磁共振成像（MRI）图像进行OS预测，采用视觉变换器（ViT）直接从MRI中提取隐藏特征，无需肿瘤分割。相比传统方法，该方案简化流程并降低计算资源需求。模型在BRATS数据集上测试准确率达到62.5%，与顶尖方法相当。同时，在精确率、召回率和F1分数上均表现更优。受限于数据集规模，ViT的泛化能力较卷积神经网络弱，此局限在所有相关研究中普遍存在。本工作验证了ViT在降采样医学影像任务中的适用性，为无需分割、计算高效的生存期预测模型奠定基础。
原文摘要 · Abstract (English)
Glioblastoma is one of the most aggressive and common brain tumors, with a median survival of 10-15 months. Predicting Overall Survival (OS) is critical for personalizing treatment strategies and aligning clinical decisions with patient outcomes. In this study, we propose a novel Artificial Intelligence (AI) approach for OS prediction using Magnetic Resonance Imaging (MRI) images, exploiting Vision Transformers (ViTs) to extract hidden features directly from MRI images, eliminating the need of tumor segmentation. Unlike traditional approaches, our method simplifies the workflow and reduces computational resource requirements. The proposed model was evaluated on the BRATS dataset, reaching an accuracy of 62.5% on the test set, comparable to the top-performing methods. Additionally, it demonstrated balanced performance across precision, recall, and F1 score, overcoming the best model in these metrics. The dataset size limits the generalization of the ViT which typically requires larger datasets compared to convolutional neural networks. This limitation in generalization is observed across all the cited studies. This work highlights the applicability of ViTs for downsampled medical imaging tasks and establishes a foundation for OS prediction models that are computationally efficient and do not rely on segmentation.
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