Department of Imaging and Nuclear Medicine, First Affiliated Hospital of Harbin Medical University, Harbin, China.
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Eur J Neurosci. 2021 May;53(9):3231-3241. doi: 10.1111/ejn.15185. Epub 2021 Mar 26.
We aimed to develop an efficient and objective pre-evaluation method to identify the precise location of a focal cortical dysplasia lesion before surgical resection to reduce medication use and decrease the post-operative frequency of seizure attacks. We developed a novel machine learning-based approach using cortical surface-based features by integrating MRI and metabolic PET to identify focal cortical dysplasia lesions. Significant surface-based features of 22 patients with histopathologically proven FCD IIb lesions were extracted from PET and MRI images using FreeSurfer. We modified significant parameters, trained and tested the XGBoost model using these surface-based features, and made predictions. We detected lesions in all 20 patients using the XGBoost model, with an accuracy of 91%. We used one-way chi-squared test to test the null hypothesis that the population proportion was 50% (p = 0.0001), indicating that our classification of the algorithm was statistically significant. The sensitivity, specificity, and false-positive rates were 93%, 91%, and 9%, respectively. We developed an objective, quantitative XGBoost classifier that combined MRI and PET imaging features to locate focal cortical dysplasia. This automated method yielded better outcomes than conventional visual analysis and single modality quantitative analysis for surgical pre-evaluation, especially in subtle or visually unidentifiable FCD lesions. This time-efficient method would also help doctors identify otherwise overlooked details.
我们旨在开发一种高效且客观的术前评估方法，以确定皮质发育不良病变的确切位置，从而减少药物使用并降低术后癫痫发作频率。我们开发了一种基于机器学习的新方法，使用皮质表面特征，整合 MRI 和代谢 PET 来识别皮质发育不良病变。使用 FreeSurfer 从 22 名经组织病理学证实为 FCD IIb 病变的患者的 PET 和 MRI 图像中提取出具有显著皮质表面特征的参数。我们修改了显著参数，使用这些表面特征训练和测试 XGBoost 模型，并进行预测。我们使用 XGBoost 模型在所有 20 名患者中检测到了病变，准确率为 91%。我们使用单向卡方检验来检验假设总体比例为 50%的零假设（p=0.0001），这表明我们的算法分类具有统计学意义。敏感性、特异性和假阳性率分别为 93%、91%和 9%。我们开发了一种客观的、定量的 XGBoost 分类器，结合了 MRI 和 PET 成像特征来定位皮质发育不良病变。与传统的视觉分析和单一模式定量分析相比，这种自动化方法在手术前评估中产生了更好的结果，特别是在细微或视觉上无法识别的 FCD 病变中。这种高效的方法还可以帮助医生发现其他可能被忽视的细节。
