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OCDA-Net:一种基于3D卷积神经网络的系统,用于分类和分期卵巢癌患者,使用FDG PET/CT检查
Mohammad Hossein Sadeghi1, Sedigheh Sina2,3, Mehrosadat Alavi4
1Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran.
Annals of nuclear medicine
|September 28, 2023
概括
新的3D CNN模型,OCDAc-Net和OCDAs-Net,使用[18F]FDG PET扫描显示了卵巢癌 (OC) 监测的高准确性. 这些人工智能工具有助于诊断复发和分期,可能改善患者的治疗结果.
科学领域:
- 医疗成像中的人工智能
- 核医学和瘤学 核医学和瘤学
- 机器学习用于癌症诊断 癌症诊断
背景情况:
- 卵巢癌 (OC) 复发和治疗后监测需要准确的诊断工具.
- 全身[18F]FDG PET/CT成像是OC管理的一个有价值的模式.
- 目前的方法可以从先进的计算分析中获益,以提高准确性.
研究的目的:
- 开发基于3D卷积神经网络 (CNN) 的系统,用于OC监控.
- 使用全身[18F]FDG PET成像用于复发检测和分期.
- 创建新的深度学习模型,OCDAc-Net和OCDAs-Net,用于OC分类和分期.
主要方法:
- 研究了来自OC患者的1224个全身[18F]FDG PET/CT图像集.
- 开发了深度神经网络算法 (OCDAc-Net用于分类,OCDAs-Net用于分期).
- 使用独立培训 (75%),验证 (10%) 和测试 (15%) 数据子集;应用数据增强.
主要成果:
- OCDAc-Net实现了ROC曲线下的面积 (AUC) 0.990和0.92的整体诊断分类准确度.
- 在OCDAs-Net的分期测试中,AUC达到0.995和0.94的整体精度.
- 模型在一个由170张图像集组成的测试集上表现出高性能.
结论:
- 拟议的3D CNN模型 (OCDAc-Net和OCDAs-Net) 是OC复发/治疗后监测的有希望的工具.
- 这些模型使用PET图像提供了一种新的预后分析方法.
- 这些系统可以帮助诊断分类和分期,即使没有病理发现.
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