扩充脑胶质瘤影像数据集，支持治疗反应与进展预测研究
CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking
- 用预训练模型自动标注肿瘤体积，完成所有时间点勾画
- 实现97%的肿瘤体积标注率，生成符合RANO标准的疗效标签
- 提供脑部掩码、影像组学特征，适合医学影像分析与个性化治疗研究
胶质母细胞瘤（GBM）是成人中最致命的原发性脑肿瘤，中位生存期为15个月。纵向多模态影像数据集结合全面临床与治疗信息，对开发可重复的计算方法以预测治疗反应、建模疾病进展和推动个性化医疗至关重要。本文发布CFB-GBM v2.0，为先前发布的数据集扩展，包含264名接受标准Stupp方案治疗的GBM患者。本版本核心贡献是完成所有可用时间点（t₀、t₁、t₂）的肿瘤体积（GTV）勾画，使整体完成率从35%提升至97%。该标注通过在BraTS 2021上预训练的nnU-Net模型，结合CFB-GBM真实轮廓进行微调，并经五名放疗科医生验证。基于这些纵向GTV标注，为所有时间对（t₀→t₁、t₀→t₂、t₁→t₂）生成了符合RANO 2.0标准的疗效分类标签。为提升数据可用性与可复现性，每个患者时间点和MRI模态均提供由HD-BET生成的脑部掩码，以及使用PyRadiomics提取的预计算影像组学特征。此外，每位患者的诊断适用的WHO分类（2016版或2021版）已明确标注。CFB-GBM v2.0已公开发布于The Cancer Imaging Archive（TCIA）：https://www.cancerimagingarchive.net/collection/cfb-gbm。
原文摘要 · Abstract (English)
Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median overall survival of 15 months. Longitudinal, multi-modal imaging datasets with comprehensive clinical and treatment data are essential to support the development of reproducible computational methods for treatment response prediction, disease progression modelling, and personalized medicine. We present CFB-GBM v2.0, an extension of our previously released CFB-GBM dataset comprising 264 GBM patients treated according to the standard Stupp protocol. The primary contribution of this release is the completion of Gross Tumour Volume (GTV) delineations across all available timepoints ($t_0$, $t_1$ and $t_2$), increasing the overall GTV completion rate from 35% to 97%. This was achieved using a nnU-Net model pre-trained on BraTS 2021 and fine-tuned on CFB-GBM ground-truth contours, with the generated segmentations validated by five radiation oncologists. From these longitudinal GTV annotations, volumetric RANO 2.0 response category labels were derived for all available temporality pairs ($t_0 \rightarrow t_1$, $t_0 \rightarrow t_2$ and $t_1 \rightarrow t_2$). To further ease dataset usability and reproducibility, brain masks computed with HD-BET and pre-computed radiomic features extracted with PyRadiomics are provided for each patient timepoint and MRI modality. Additionally, the WHO classification guideline (2016 vs. 2021) applicable to each patient's diagnosis is now explicitly documented. CFB-GBM v2.0 is publicly available on The Cancer Imaging Archive (TCIA) at https://www.cancerimagingarchive.net/collection/cfb-gbm .
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。