Brain Tumor Recurrence Prediction after Gamma Knife Radiotherapy from MRI and Related DICOM-RT: An Open Annotated Dataset and Baseline Algorithm (Brain-TR-GammaKnife)

Here we release a brain cancer MRI dataset with the companion Gamma Knife treatment planning and follow-up data for the purpose of tumor recurrence prediction. The dataset consisted of 47 subjects. A total of 244 lesions were collected with annotations. The dataset contains original patient MRI images (in DICOM format), radiation therapy structure data (in DICOM and NRRD format), code, and clinical information. First, dose MRI images were resampled to original MRI spacing via a linear transformation. Second, each region in each patient's MRI was extracted and cropped out; note that one patient may have multiple lesions and or multiple imaging sessions. Third, the corresponding radiation dose information was cropped out to the resampled aligned lesion mask. In this way, each lesion MRI is paired with its radiation dose MRI. The release of this dataset is expected to contribute to the development of automated brain tumor recurrence prediction algorithms.

本研究公开了一套用于脑肿瘤复发预测的脑癌MRI数据集，配套包含伽玛刀（Gamma Knife）治疗计划与随访数据。该数据集共纳入47名受试者，累计收集244处带标注的病灶。数据集涵盖患者原始MRI图像（DICOM格式）、放射治疗结构数据（DICOM与NRRD格式）、代码文件及临床信息。首先，通过线性变换将剂量MRI图像重采样至原始MRI的空间间距；其次，提取并裁剪每位患者MRI中的病灶区域，需注意单名患者可存在多处病灶或多次成像检查；第三，将对应放射剂量信息裁剪至重采样对齐后的病灶掩码区域，由此实现每处病灶MRI与其剂量MRI的一一配对。本数据集的公开有望为自动化脑肿瘤复发预测算法的研发提供助力。