Zhao Xuemin, Hu Xu, Guo Zhihao, Hu Wenhan, Zhang Chao, Mo Jiajie, Zhang Kai
Department of Neurophysiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100071, China.
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China.
J Clin Med. 2024 Jan 24;13(3):680. doi: 10.3390/jcm13030680.
The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower in epileptogenic tubers defined via presurgical evaluation ( = 0.001). The CNNs showed high performance for localizing tubers, with an accuracy between 0.992 and 0.994 across the five datasets. The automated segmentations were highly correlated with clinician-based features. The neuroimaging characteristics for epileptogenic tubers were demonstrated, increasing surgical confidence in clinical practice. The validated deep learning detection algorithm yielded a high performance in determining tubers with an excellent agreement with reference clinician-based segmentation. Collectively, when coupled with our investigation of minimal input requirements, the approach outlined in this study represents a clinically invaluable tool for the management of TSC.
本研究提出了一种新方法，用于识别结节性硬化症（TSC）患者的致痫性结节，并使用三维卷积神经网络（3D CNN）实现结节分割的自动化。该研究回顾性纳入了31例TSC患者，其病变已从多参数神经影像数据中进行了手动标注。通过术前评估和立体脑电图记录来确定致痫性结节。提取了神经影像指标，并在致痫性和非致痫性结节之间进行比较。此外，使用五个具有不同预处理策略的数据集来构建和训练用于结节自动分割的3D CNN。通过术前评估定义的致痫性结节中，标准化正电子发射断层扫描（PET）代谢值显著更低（ = 0.001）。这些CNN在定位结节方面表现出高性能，在五个数据集中的准确率介于0.992和0.994之间。自动分割与基于临床医生的特征高度相关。研究显示了致痫性结节的神经影像特征，提高了临床实践中的手术信心。经过验证的深度学习检测算法在确定结节方面表现出高性能，与基于临床医生的参考分割具有极佳的一致性。总体而言，结合我们对最小输入要求的研究，本研究中概述的方法是TSC管理中具有临床价值的工具。
