使用深度学习对直肠癌中淋巴结转移的术前评估:研究各种MRI序列的实用性
Jiayue Zhao1,2, Peng Zheng3, Teng Xu1,2
1Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
Annals of surgical oncology
|June 24, 2025
概括
使用多参数MRI (mpMRI) 的深度学习模型可以在术前检测直肠癌 (RC) 中的淋巴结转移 (LNM). 这种人工智能工具在临床实践中显示出对自动化LNM评估的前景.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 结直肠癌 (RC) 的分期需要对淋巴结转移 (LNM) 的准确评估.
- 在手术前的LNM状态显著影响治疗决策和RC患者的结果.
- 目前的成像方法在可靠检测LNM方面存在局限性.
研究的目的:
- 开发一种深度学习 (DL) 模型,用于RC中使用3D多参数MRI (mpMRI) 的手术前LNM评估.
- 为了评估DL模型的诊断性能.
- 为了确定不同MRI序列对LNM检测的贡献.
主要方法:
- 对613名接受了手术前mpMRI的RC患者进行了回顾性研究.
- 开发一个多参数多尺度的EfficientNet (MMENet) DL模型.
- 与单参数模型和放射科医生使用AUC,精度,灵敏度和特异性的MMENet性能进行比较.
主要成果:
- 使用T2WI,DWI和DCE序列的MMENet模型在内部和外部测试队列中实现了高性能.
- 在内部和外部测试队列中,分别报告了0.808和0.782的AUC.
- 在LNM评估中,MMENet模型的表现优于单参数模型和人类放射科医生.
结论:
- 开发的MMENet DL模型准确地评估了RC中的手术前LNM.
- 结合T2WI,DWI和DCE序列,可以提高模型的预测能力.
- 这种人工智能驱动的方法显示出在临床环境中自动化LNM评估的巨大潜力.
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