人工智能准备好的直肠癌MRI成像:用于瘤检测和细分的工作流程
Heather M Selby1, Yewon A Son2, Vipul R Sheth3
1S-SPIRE Center, Department of Surgery, Stanford University School of Medicine, Palo Alto, USA. selbyh@stanford.edu.
BMC medical imaging
|March 15, 2025
概括
这项研究表明,放射科医生和数据科学家的合作可以创建用于直肠癌的高质量的标记MRI数据集,这对于开发用于自动瘤细分和改善临床工作流程的AI工具至关重要.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 结直肠癌的分期依赖于磁共振成像 (MRI),但由于组织重叠和解释主观性,瘤细分具有挑战性.
- 由多名放射科医生进行的手动细分是黄金标准,但成本高且耗时.
- 缺乏高质量的公共MRI成像数据集阻碍了用于直肠癌细分的强大的AI模型的开发.
研究的目的:
- 促进放射科医生和数据科学家之间的合作,以在MRI图像上检测和细分直肠瘤.
- 为人工智能驱动的方法奠定基础,以简化直肠瘤分析和提高工作流程效率.
- 将临床专业知识与数据科学技能相结合,用于增强成像分析.
主要方法:
- 分析了37名直肠癌患者的治疗前T2和扩散权重MR图像.
- 数据科学家接受了放射学家领导的培训,并参加了多学科瘤委员会.
- 数据科学家使用放射科医生注释和临床笔记进行了瘤细分,随后进行放射科医生审查和编辑.
主要成果:
- 数据科学家成功地在所有MRI图像中通过放射科医生的协助识别了直肠瘤.
- 细分显示出与放射科医生编辑的强烈一致,实现了高的子相似系数 (DSC) 和雅卡德指数 (JI) 得分.
- 差异主要是由于周围直肠结构的轻微过度或不足细分.
结论:
- 由放射科医生和数据科学家共同生成高质量的标记MR数据集是可行的.
- 这种方法对于训练人工智能模型进行自动直肠癌检测和细分至关重要.
- 整合专业知识可以提高AI性能,并改变未来的临床工作流程.
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