Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
Can a proposed double branch multimodality-contribution-aware TripNet improve the prediction performance of the microvascular invasion of hepatocellular carcinoma based on small samples?
复制标题
提出的双分支多模态贡献感知 TripNet 能否提高基于小样本的肝细胞癌微血管侵犯的预测性能？
DOI：
10.3389/fonc.2022.1035775
复制
发表时间：
2022
影响因子：
4.7
通讯作者：
中科院分区：
文献类型：
--
作者：
目的评估基于小样本的双分支多模式贡献感知TripNet （MCAT）在肝细胞癌（HCC）微血管侵袭（MVI）预测中的潜在改进性能。方法回顾性分析103例连续患者的121例hcc，其中MVI阳性44例，MVI阴性77例。提出了一种旨在提高深度神经网络精度和缓解小样本量负面影响的MCAT模型，并将MCAT模型与其他常用的深度神经网络，包括2DCNN（二维卷积神经网络）、ResNet（残差神经网络）和SENet（挤压激励网络）进行了比较，验证了MCAT模型的改进。结果经验证，MCAT的AUC值在CT、MRI及两种影像学上均显著高于2DCNN（均P < 0.001）。基于小样本的单分支预训练模型的AUC值显著高于CT分支和双分支端到端训练模型（分别为0.62 vs 0.69, p=0.016, 0.65 vs 0.83, p=0.010）。CT和MRI双支MCAT AUC值（0.83）均显著高于CT支MCAT AUC值（0.69）和MRI支MCAT AUC值（0.73）（P < 0.001, P = 0.03），也显著高于常用的ReNet模型（0.67）和SENet模型（0.70）（P < 0.001, P = 0.005）。结论与其他深度神经网络或单分支MCAT模型相比，基于小样本的双分支MCAT模型可以提高有效性，为小样本深度学习和多成像模式融合等场景提供了潜在的解决方案。
Objectives To evaluate the potential improvement of prediction performance of a proposed double branch multimodality-contribution-aware TripNet (MCAT) in microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on a small sample. Methods In this retrospective study, 121 HCCs from 103 consecutive patients were included, with 44 MVI positive and 77 MVI negative, respectively. A MCAT model aiming to improve the accuracy of deep neural network and alleviate the negative effect of small sample size was proposed and the improvement of MCAT model was verified among comparisons between MCAT and other used deep neural networks including 2DCNN (two-dimentional convolutional neural network), ResNet (residual neural network) and SENet (squeeze-and-excitation network), respectively. Results Through validation, the AUC value of MCAT is significantly higher than 2DCNN based on CT, MRI, and both imaging (P < 0.001 for all). The AUC value of model with single branch pretraining based on small samples is significantly higher than model with end-to-end training in CT branch and double branch (0.62 vs 0.69, p=0.016, 0.65 vs 0.83, p=0.010, respectively). The AUC value of the double branch MCAT based on both CT and MRI imaging (0.83) was significantly higher than that of the CT branch MCAT (0.69) and MRI branch MCAT (0.73) (P < 0.001, P = 0.03, respectively), which was also significantly higher than common-used ReNet (0.67) and SENet (0.70) model (P < 0.001, P = 0.005, respectively). Conclusion A proposed Double branch MCAT model based on a small sample can improve the effectiveness in comparison to other deep neural networks or single branch MCAT model, providing a potential solution for scenarios such as small-sample deep learning and fusion of multiple imaging modalities.
登录
查看更多内容
影响因子：
29.4
作者：
El-Serag HB
通讯作者：
El-Serag HB
影响因子：
3.7
作者：
Cucchetti, Alessandro;Piscaglia, Fabio;Pinna, Antonio Daniele
通讯作者：
Pinna, Antonio Daniele
影响因子：
3.4
作者：
Reginelli, Alfonso;Vanzulli, Angelo;Cappabianca, Salvatore
通讯作者：
Cappabianca, Salvatore
影响因子：
5.9
作者：
Chong HH;Yang L;Sheng RF;Yu YL;Wu DJ;Rao SX;Yang C;Zeng MS
通讯作者：
Zeng MS
影响因子：
5.9
作者：
Ma, Xiaohong;Wei, Jingwei;Tian, Jie
通讯作者：
Tian, Jie