Gleichgerrcht Ezequiel, Kaestner Erik, Hassanzadeh Reihaneh, Roth Rebecca W, Parashos Alexandra, Davis Kathryn A, Bagić Anto, Keller Simon S, Rüber Theodor, Stoub Travis, Pardoe Heath R, Dugan Patricia, Drane Daniel L, Abrol Anees, Calhoun Vince, Kuzniecky Ruben I, McDonald Carrie R, Bonilha Leonardo
Department of Neurology, Emory University, Atlanta, GA 30329, USA.
Department of Radiation Medicine & Applied Sciences, University of California San Diego, San Diego, CA 92093, USA.
Brain. 2025 Jun 3;148(6):2189-2200. doi: 10.1093/brain/awaf020.
Despite decades of advancements in diagnostic MRI, 30%-50% of temporal lobe epilepsy (TLE) patients remain categorized as 'non-lesional' (i.e. MRI negative) based on visual assessment by human experts. MRI-negative patients face diagnostic uncertainty and significant delays in treatment planning. Quantitative MRI studies have demonstrated that MRI-negative patients often exhibit a TLE-specific pattern of temporal and limbic atrophy that might be too subtle for the human eye to detect. This signature pattern could be translated successfully into clinical use via advances in artificial intelligence in computer-aided MRI interpretation, thereby improving the detection of brain 'lesional' patterns associated with TLE. Here, we tested this hypothesis by using a three-dimensional convolutional neural network applied to a dataset of 1178 scans from 12 different centres, which was able to differentiate TLE from healthy controls with high accuracy (85.9% ± 2.8%), significantly outperforming support vector machines based on hippocampal (74.4% ± 2.6%) and whole-brain (78.3% ± 3.3%) volumes. Our analysis focused subsequently on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI-negative patients from this cohort were accurately identified as TLE 82.7% ± 0.9% of the time, an encouraging finding given that clinically these were all patients considered to be MRI negative (i.e. not radiographically different from controls). The saliency maps from the convolutional neural network revealed that limbic structures, particularly medial temporal, cingulate and orbitofrontal areas, were most influential in classification, confirming the importance of the well-established TLE signature atrophy pattern for diagnosis. Indeed, the saliency maps were similar in MRI-positive and MRI-negative TLE groups, suggesting that even when humans cannot distinguish more subtle levels of atrophy, these MRI-negative patients are on the same continuum common across all TLE patients. As such, artificial intelligence can identify TLE lesional patterns, and artificial intelligence-aided diagnosis has the potential to enhance the neuroimaging diagnosis of TLE greatly and to redefine the concept of 'lesional' TLE.
尽管诊断性磁共振成像（MRI）在过去几十年取得了进展，但基于人类专家的视觉评估，仍有30%-50%的颞叶癫痫（TLE）患者被归类为“无病变”（即MRI阴性）。MRI阴性的患者面临诊断不确定性以及治疗计划的显著延迟。定量MRI研究表明，MRI阴性的患者通常表现出特定于TLE的颞叶和边缘叶萎缩模式，这种模式可能过于细微，人眼难以察觉。通过计算机辅助MRI解释中的人工智能进展，这种特征模式可以成功转化为临床应用，从而改善与TLE相关的脑“病变”模式的检测。在此，我们通过将三维卷积神经网络应用于来自12个不同中心的1178次扫描数据集来验证这一假设，该网络能够以高精度（85.9%±2.8%）区分TLE与健康对照，显著优于基于海马体体积（74.4%±2.6%）和全脑体积（78.3%±3.3%）的支持向量机。我们随后的分析集中于术后实现持续无癫痫发作的患者子集，将其作为确认TLE的金标准。重要的是，该队列中MRI阴性的患者在82.7%±0.9%的时间内被准确识别为TLE，鉴于临床上这些患者均被认为是MRI阴性（即影像学上与对照无差异），这一发现令人鼓舞。卷积神经网络的显著性图显示，边缘结构，特别是颞叶内侧、扣带回和眶额区域，在分类中最具影响力，证实了既定的TLE特征萎缩模式对诊断的重要性。实际上，MRI阳性和MRI阴性的TLE组中的显著性图相似，这表明即使人类无法区分更细微的萎缩程度，这些MRI阴性的患者与所有TLE患者处于相同的连续统一体中。因此，人工智能可以识别TLE病变模式，并且人工智能辅助诊断有可能极大地增强TLE的神经影像学诊断，并重新定义“病变性”TLE的概念。
