多模组合深度学习在头癌HPV亚型化中的多模组合深度学习
Manob Jyoti Saikia1, Shiba Kuanar2, Dwarikanath Mahapatra3
1Electrical Engineering, University of North Florida, Jacksonville, FL 32224, USA.
Bioengineering (Basel, Switzerland)
|January 22, 2024
概括
这项研究开发了一种深度学习模型,用于使用CT和PET扫描检测口腔平状细胞癌 (OPSCC) 中的人类乳头瘤病毒 (HPV) 状态. 人工智能精确地将HPV阳性与阴性病例区分开来,帮助治疗决策.
科学领域:
- 放射学和瘤学 放射学和瘤学
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
背景情况:
- 口腔状细胞癌 (OPSCC) 呈现出显著的异质性,人类乳头瘤病毒 (HPV) 感染是关键的危险因素.
- 在OPSCC中准确地确定HPV状态对于指导治疗策略和预测患者结果至关重要.
- 目前的诊断方法可能需要侵入性手术,突出了对非侵入性替代品的需求.
研究的目的:
- 开发和评估一种基于深度学习的方法,用于在OPSCC中自动检测HPV状态.
- 通过使用计算机断层扫描 (CT) 和正子发射断层扫描 (PET) 图像来评估多模式特征融合架构的准确性.
- 将拟议模型的性能与现有的HPV状态分类方法进行比较.
主要方法:
- 基于3D卷积神经网络 (CNN) 的多模式特征融合架构被设计用于HPV状态预测.
- 该模型整合了与瘤区域的强度,对比度,形状,纹理异质性 (来自CT) 和代谢评估 (来自PET) 相关的特征.
- 采用软投票的CNN网络组合,将多模式特征合并为最终分类.
主要成果:
- 多模组合模型与单模PET/CT方法相比,表现优越.
- 拟议的方法在TCGA和MAASTRO数据集的组合上实现了0.76的曲线下面面积 (AUC) 和0.746的F1得分.
- 在MAASTRO数据集上,该模型获得了主要瘤体积 (VOI) 的AUC得分为0.74.
结论:
- 对CT和PET图像的基于深度学习的分析可以准确地预测OPSCC中的HPV状态.
- 多模式特征融合方法为OPSCC诊断提供了一个有希望的非侵入性工具.
- 建议在更大的队列中进行进一步验证,以提高诊断准确性并支持活检前的评估.
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