Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care.

翻译：胶质瘤是成人中最常见的恶性原发性脑肿瘤，也是致死率最高的癌症类型之一。由于其遗传多样性以及在形态、结构、组织学和治疗反应方面的高度内在异质性，治疗与监测面临诸多挑战。治疗方案包括手术、放疗和全身性治疗，其中磁共振成像在治疗规划及治疗后纵向评估中发挥着关键作用。2024年脑肿瘤分割挑战赛聚焦于治疗后胶质瘤MRI分析，将基于规模最大的专家标注治疗后胶质瘤MRI数据集，为前沿自动分割模型建立社区标准与基准。参赛者需开发自动分割模型以预测四个不同的肿瘤子区域：强化组织、周围非强化T2/液体衰减反转恢复高信号区、非强化肿瘤核心以及切除腔。模型将在独立的验证集和测试集上，采用BraTS 2024系列挑战赛统一的标准性能指标进行评估，包括病灶级别的戴斯相似性系数与豪斯多夫距离。本挑战赛所开发的模型将推动MRI自动分割领域的发展，促进其向临床实践的转化，最终提升患者诊疗水平。