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对于在结肠直肠癌患者的对比增强腹部CT中检测肝转移的深度学习算法:与放射科医生的比较研究.

Riccardo Sartoris1,2, Anita Paisant3,4, Alexandre Bône5

  • 1Department of Radiology, Hôpital Beaujon, 100 boulevard du Général Leclerc, 92110 Clichy, France.

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概括

一个深度学习算法 (DLA) 在检测结直肠癌 (CRC) 患者的肝转移 (LM) 方面表现与放射科医生相当. 这种人工智能工具在各种损伤大小和位置上显示出一致的准确性,有助于瘤诊断.

关键词:
这里是 Abdomen Abdomen 的意思.进行比较研究.深度学习 (Deep Learning) 是一种深度学习.诊断 诊断 诊断 诊断 是一个胃肠道 胃肠道 胃肠道采用成像方式的模式.肝脏 肝脏 肝脏 肝脏在瘤学瘤学.分段化 分段化 分段化 分段化

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 在瘤学瘤学.

背景情况:

  • 大肠直肠癌 (CRC) 经常转移到肝脏,需要精确检测才能有效治疗.
  • 放射科医生对CT扫描中的肝转移 (LM) 的解释可能是具有挑战性的,因为细微的病变和读者之间的变化.
  • 深度学习算法 (DLA) 在医学图像分析中表现有前途,但它们在检测LM的性能在各种临床环境中需要进行彻底的评估.

研究的目的:

  • 评估DLA用于检测结直肠癌 (CRC) 患者肝转移 (LM) 的诊断性能.
  • 为了比较DLA的准确性与高级和正在培训的放射科医生.
  • 评估DLA在不同损伤大小和位置上的一致性.

主要方法:

  • 对181名CRC患者进行了回顾性双中心研究,这些患者接受了对比度增强的腹部CT.
  • 在每个结节和每个患者的水平上评估DLA性能.
  • 与两个失明的放射科医生 (高级和正在培训) 进行了检测和错误检测率的比较.

主要成果:

  • DLA实现了81%的LM检测率,相当于高级 (79%) 和正在培训 (76%) 的放射科医生.
  • 随着病变大小的提高,检测率也提高了 (55%的病变大小为<10mm,91%的病变大小为10-19mm,99%的病变大小为≥20mm).
  • 错误检测率很低,在DLA和放射科医生之间是可比的,发现了常见的模仿者.

结论:

  • 在检测CRC患者的肝转移中,DLA的性能与放射科医生相似.
  • 该算法在不同的损伤大小和位置上显示出强大的性能.
  • DLAs代表了增强放射科医生在肝转移检测能力的宝贵工具.