Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, USA.
University of Essen School of Medicine, Essen, Germany.
Sci Data. 2024 Feb 29;11(1):254. doi: 10.1038/s41597-024-03021-9.
Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.
切除和全脑放疗（WBRT）是脑转移瘤（BM）的标准治疗方法，但与认知副作用有关。立体定向放射外科（SRS）采用靶向方法，副作用比 WBRT 少。SRS 需要精确识别和描绘 BM。虽然已经开发出用于此目的的人工智能（AI）算法，但由于在临床环境中模型性能不佳，其临床采用受到限制。算法的局限性通常是由于用于训练 AI 网络的数据集中存在的质量问题。本研究的目的是创建一个大型、异质、标注的 BM 数据集，用于训练和验证 AI 模型。我们提出了一个包含 200 名患者的 BM 数据集，这些患者具有预处理 T1、T1 对比后、T2 和 FLAIR MR 图像。该数据集包括 T1 对比后增强和坏死的 3D 分割，以及 FLAIR 上的肿瘤周围水肿的 3D 分割。我们的数据集包含 975 个增强病变，其中许多是亚厘米大小的，同时还包含临床和影像学信息。我们通过与 PACS 集成的分割工作流程，采用简化的方法来构建数据库。
