juampablo/BTReport-BraTS23

BTReport-BraTS23数据集是BTReport框架的配套数据集，旨在推动神经肿瘤学放射学报告生成（RRG）的研究。它通过添加结构化、临床相关的特征和合成的放射学报告，扩展了BraTS 2023影像数据集。BTReport提供了一种结构化的脑肿瘤报告方法，通过提取定量神经影像特征，并使用大型语言模型（LLMs）将其合成为专业的放射学报告。框架提取了标准化的VASARI特征（如增强、坏死、水肿）、3D中线移位（使用深度学习配准进行定量估计）以及空间元数据（病变大小、坐标和解剖学受累情况），然后生成放射学报告。该数据集适用于训练和微调用于医学报告生成的LLMs，评估LLMs在确定性神经影像特征上的基础性，以及开发用于神经肿瘤学的自动化临床文档工具。但不应在没有放射科医生监督的情况下用于初级临床诊断。数据集源自BraTS 2023挑战赛，不包含任何个人身份信息（PHI）。数据集由KurtLab、华盛顿大学和Microsoft Health AI的团队策划，采用MIT许可证。

BTReport-BraTS23 is a companion dataset to the BTReport framework, designed to advance research in neuro-oncology radiology report generation (RRG). It augments the BraTS 2023 imaging dataset with structured, clinically relevant features and synthetic radiology reports. BTReport provides a structured approach to brain tumor reporting by extracting quantitative neuroimaging features and synthesizing them into professional radiology reports using Large Language Models (LLMs). The framework extracts standardized VASARI features (e.g., enhancement, necrosis, edema), 3D Midline Shift (quantitative estimation using deep learning registration), and spatial metadata (lesion size, coordinates, and anatomical involvement), then generates radiology reports. The dataset is intended for training and fine-tuning LLMs for medical report generation, evaluating the grounding of LLMs on deterministic neuroimaging features, and developing automated clinical documentation tools for neuro-oncology. However, it should not be used for primary clinical diagnosis without radiologist supervision. The dataset is derived from the BraTS 2023 challenge and contains no personally identifiable information (PHI). The dataset is curated by a team from KurtLab, University of Washington, and Microsoft Health AI, and is licensed under MIT.