Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction
Morphology-aware multi-source fusion-based intracranial aneurysms rupture prediction
复制标题
基于形态学感知的多源融合颅内动脉瘤破裂预测
DOI：
10.1007/s00330-022-08608-7
复制
发表时间：
2022-02-18
影响因子：
5.9
通讯作者：
Heng, Pheng-Ann
中科院分区：
文献类型：
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作者：
Ou, Chubin;Li, Caizi;Heng, Pheng-Ann
目的我们提出了一种新的方法来训练用于动脉瘤破裂预测的深度学习模型，该模型仅使用有限数量的标记数据。方法以分割的动脉瘤掩模为输入，采用自监督方法预训练主干模型，从947例未标记的血管造影图像中学习动脉瘤形态的深度嵌入。随后，使用120个具有已知破裂状态的标记病例对骨干模型进行微调。将临床信息与深度嵌入相结合，以进一步提高预测性能。将该模型与放射组学模型和传统形态学模型进行了比较。基于该模型开发了一个辅助诊断系统，并与五名神经外科医生进行了测试。结果我们的方法实现了0.823的受试者工作特征曲线下面积（AUC），优于从头开始训练的深度学习模型（0.787）。通过结合临床信息，所提出的模型的性能进一步提高到AUC = 0.853，使得结果显著优于基于放射组学的模型（AUC = 0.805，p = 0.007）或基于常规形态学参数的模型（AUC = 0.766，p = 0.001）。我们的模型还实现了最高的灵敏度，PPV，NPV和准确性。使用辅助诊断系统，神经外科医生的预测性能从AUC=0.877提高到0.945（p = 0.037）。结论我们提出的方法可以开发出具有竞争力的深度学习模型，用于仅使用有限数量的数据进行破裂预测。该辅助诊断系统可用于神经外科医生预测破裂。
Objectives We proposed a new approach to train deep learning model for aneurysm rupture prediction which only uses a limited amount of labeled data. Method Using segmented aneurysm mask as input, a backbone model was pretrained using a self-supervised method to learn deep embeddings of aneurysm morphology from 947 unlabeled cases of angiographic images. Subsequently, the backbone model was finetuned using 120 labeled cases with known rupture status. Clinical information was integrated with deep embeddings to further improve prediction performance. The proposed model was compared with radiomics and conventional morphology models in prediction performance. An assistive diagnosis system was also developed based on the model and was tested with five neurosurgeons. Result Our method achieved an area under the receiver operating characteristic curve (AUC) of 0.823, outperforming deep learning model trained from scratch (0.787). By integrating with clinical information, the proposed model's performance was further improved to AUC = 0.853, making the results significantly better than model based on radiomics (AUC = 0.805, p = 0.007) or model based on conventional morphology parameters (AUC = 0.766, p = 0.001). Our model also achieved the highest sensitivity, PPV, NPV, and accuracy among the others. Neurosurgeons' prediction performance was improved from AUC=0.877 to 0.945 (p = 0.037) with the assistive diagnosis system. Conclusion Our proposed method could develop competitive deep learning model for rupture prediction using only a limited amount of data. The assistive diagnosis system could be useful for neurosurgeons to predict rupture.