RADCURE:一个开源的头癌CT数据集,用于临床辐射治疗的洞察力
Mattea L Welch1,2, Sejin Kim1,2,3, Andrew J Hope4,5
1Princess Margaret Cancer Centre, Toronto, ON, Canada.
Medical physics
|February 16, 2024
概括
RADCURE数据集为研究提供了广泛的头癌 (HNC) 成像数据. 这一公共资源有助于开发人工智能和机器学习模型,以改善癌症治疗和治疗结果.
科学领域:
- 医疗成像医学成像
- 无线电学 (Radiomics) 是一种无线电学.
- 瘤学研究研究
背景情况:
- 头癌 (HNC) 研究需要大型,全面的成像数据集.
- 现有的公共数据集往往缺乏高级分析所需的规模和细节.
- 临床放射治疗 (RT) 规划产生了有价值的成像和轮数据.
研究的目的:
- 为了展示RADCURE,一个大规模的,公开可访问的头癌成像数据集.
- 为研究用途提供临床RT治疗规划的回顾性重建数据.
- 促进放射学研究,AI/ML模型开发和HNC中的生物标志物发现.
主要方法:
- 数据集包括来自3346名HNC患者的CT RT模拟图像和轮.
- 数据包括人口,临床,治疗 (TNM分期) 和结果信息.
- 图像和轮分别以DICOM CT和RT-STRUCT格式提供;链接CSV用于临床数据.
主要成果:
- 该RADCURE数据集包括3346名患有瘤和风险器官标准化轮的患者.
- 患者的中位年龄为63岁,其中80%为男性;口腔癌最常见 (50%).
- 随访数据延伸到5年的中位数,生存率为60%.
结论:
- RADCURE是放射学,人工智能和头癌机器学习研究的宝贵资源.
- 该数据集支持开发新的非侵入性生物标志物和预后模型.
- 通过人工智能驱动的HNC管理见解,使临床程序的进步成为可能.
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