使用深度学习模型的诊断性能F-FDG PET/CT用于评估肺癌患者放射治疗后复发的肺癌患者
Changhwan Sung1, Jungsu S Oh1, Byung Soo Park1
1Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
Annals of nuclear medicine
|April 8, 2024
概括
一个深度学习模型有效地区分放射治疗的变化和肺癌患者的瘤复发,使用18F-FDG PET/CT扫描. 这种AI工具有助于在治疗后进行准确的诊断.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 区分与放射治疗 (RT) 相关的变化和瘤复发对于肺癌患者在RT后至关重要.
- 准确的区分影响治疗决策和患者的结果.
- 当前的成像方法在区分这些条件时可能存在局限性.
研究的目的:
- 开发和评估一个深度学习模型,以区分RT相关的变化和肺癌患者的瘤复发.
- 通过使用18F-FDG PET/CT成像来评估基于二维切片的卷积神经网络 (CNN) 的性能.
主要方法:
- 追溯分析308名肺癌患者在RT 18F-FDG后进行PET/CT扫描.
- 使用3329个图像切片开发一个2D CNN模型.
- 在五个独立测试组的性能评估中,瘤复发通过组织学或临床随访得到证实.
主要成果:
- 该CNN模型实现了高诊断性能,其AUC范围为0.98-0.99.9.
- 报告的灵敏度和特异性分别为95-98%和87-95%.
- 可解释人工智能 (Grad-CAM) 证实该模型识别的区域与实际的复发性瘤相对应.
结论:
- 开发的基于2D切片的CNN模型准确地区分了RT相关的变化和肺癌患者的瘤复发.
- 18F-FDG PET成像与深度学习相结合,有望提高治疗后肺癌监测的诊断准确性.
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