Duke Lung Nodule Dataset 2024

Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification. Methods: Screening chest CT scans done between January 1, 2015 and June 30, 2021 at Duke University Health System were considered for this study. Efficient nodule annotation was performed semi-automatically by using a publicly available deep learning nodule detection algorithm trained on the LUNA16 dataset to identify initial candidates, which were then accepted based on nodule location in the radiology text report or manually annotated by a medical student and a fellowship-trained cardiothoracic radiologist. Results: The dataset contains 1613 CT volumes with 2487 annotated nodules. Radiologist spot-checking confirmed the semi-automated annotation had an accuracy rate of >90%. Conclusions: The Duke Lung Nodule Dataset is the first large dataset for CT screening for lung cancer reflecting the use of current CT technology. This represents a useful resource of lung cancer risk classification research, and the efficient annotation methods described for its creation may be used to generate similar databases for research in the future.研究背景：随着低剂量胸部CT筛查方案成为肺癌高危患者的标准诊疗规范，肺癌风险分类已成为愈发重要的研究方向。当前用于肺结节分类算法训练与测试的大规模标注公共数据库资源相对匮乏。 研究方法：本研究纳入2015年1月1日至2021年6月30日期间于杜克大学健康系统完成的胸部CT筛查扫描图像。我们采用基于LUNA16数据集训练的公开深度学习肺结节检测算法，以半自动流程开展高效结节标注：先由算法识别初始结节候选区域，再结合放射科文本报告中的结节位置信息完成候选区域确认，或由医学生与接受过胸心放射科专科fellowship培训的放射科医师进行手动标注。 研究结果：本数据集共包含1613个CT容积数据，对应2487个标注肺结节。经放射科医师抽查证实，半自动标注的准确率超过90%。 研究结论：杜克肺结节数据集（Duke Lung Nodule Dataset）是首个反映当前CT技术应用情况的肺癌CT筛查大规模数据集。该数据集可为肺癌风险分类研究提供极具价值的资源，其构建过程中采用的高效标注方法，未来亦可用于构建同类研究所需的数据库。