CFB-GBM v2.0：用于多模态胶质母细胞瘤分割、放射组学及RANO进展追踪的增强纵向数据集
CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking
另 1 家 · 查看机构详情
- Centre François Baclesse（弗朗索瓦·巴克莱斯中心）
- Université de Caen Normandie（卡昂诺曼底大学）
- ENSICAEN（卡昂高等工程师学院）
- GREYC（格雷计算机科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本文发布增强版CFB-GBM v2.0纵向数据集，含264名GBM患者数据，完成所有时间点GTV勾画（完成率达97%），提供相关标注、特征及WHO分类信息，可用于多模态GBM相关研究。
中文摘要 AI 辅助
胶质母细胞瘤（GBM）是成人中最具侵袭性的原发性脑肿瘤，患者中位总生存期为15个月。具备全面临床与治疗数据的纵向多模态成像数据集，对支持开发可复现的计算方法以实现治疗反应预测、疾病进展建模及个性化医疗至关重要。本文介绍CFB-GBM v2.0，这是我们此前发布的CFB-GBM数据集的扩展版本，包含264名按标准Stupp方案治疗的GBM患者。本次发布的主要贡献是完成了所有可用时间点（t₀、t₁和t₂）的大体肿瘤体积（GTV）勾画，使整体GTV完成率从35%提升至97%。这一成果通过使用在BraTS 2021上预训练、并在CFB-GBM真实轮廓上微调的nnU-Net模型实现，生成的分割结果经5名放射肿瘤学家验证。基于这些纵向GTV注释，为所有可用时间点对（t₀→t₁、t₀→t₂和t₁→t₂）推导了体积型RANO 2.0反应类别标签。为进一步提升数据集的可用性与可复现性，还为每位患者的每个时间点及MRI模态提供了用HD-BET计算的脑掩码，以及用PyRadiomics提取的预计算放射组学特征。此外，每位患者诊断适用的WHO分类指南（2016版vs.2021版）现已明确记录。CFB-GBM v2.0可在癌症影像档案（TCIA）上公开获取，链接为该https URL。
英文摘要
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 .
