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视觉变压器在F-FDG PET/CT中区分良性和恶性切片
Daiki Nishigaki1, Yuki Suzuki1, Tadashi Watabe2
1Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Scientific reports
|April 9, 2024
概括
视觉转换器 (ViT) AI在F-FDG PET/CT扫描中准确地分类癌症,表现优于CNN. 这有助于改善诊断,特别是低吸收癌症,减少瘤学虚假阴性.
科学领域:
- 医学成像人工智能 医学成像人工智能
- 瘤学 诊断 诊断 瘤学
- 机器学习在医学中的应用
背景情况:
- F-FDG PET/CT对于癌症诊断至关重要,但面临着生理吸收和瘤吸收不良的挑战.
- 扫描解读的困难可能导致恶性病变的错误负诊断.
研究的目的:
- 评估视觉变压器 (ViT) 的有效性,用于将F-FDG PET/CT切片自动分类为良性或恶性.
- 在瘤成像中,将ViT的性能与卷积神经网络 (CNN) 模型进行比较.
主要方法:
- 一项回顾性研究使用了207名患者 (143名恶性,64名良性) 的F-FDG PET/CT数据.
- 视觉变压器 (ViT) 和基线CNN模型 (EfficientNet,DenseNet) 在数据集上进行了训练和测试.
- 使用接收器操作特征曲线 (AUC) 下面的面积来评估性能.
主要成果:
- ViT的AUC值高于0.90 (95%CI为0.89,0.91),超过了EfficientNet (0.87) 和DenseNet (0.87) 的表现.
- 即使在FDG吸收率低的情况下,ViT也表现出强的表现,AUC为0.81,明显优于CNN (DenseNet:0.65).
- 敏感分析强调了ViT在识别具有挑战性的,容易错过的瘤病例中的价值.
结论:
- 视觉变压器 (ViT) 为瘤学中自动化PET/CT解释提供了一个有前途的AI解决方案.
- 维特的卓越性能,特别是在低吸收场景中,可以提高诊断准确度并减少假阴性.
- 这种人工智能方法具有显著的潜力,可以帮助临床决策,并改善癌症诊断中的患者结果.
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