From the Radiology Informatics Lab, Department of Radiology, Mayo Clinic, Rochester, Minnesota.
Department of Radiology, Mayo Clinic, Rochester, Minnesota.
AJNR Am J Neuroradiol. 2024 Apr 8;45(4):439-443. doi: 10.3174/ajnr.A8173.
Spontaneous intracranial hypotension is an increasingly recognized condition. Spontaneous intracranial hypotension is caused by a CSF leak, which is commonly related to a CSF-venous fistula. In patients with spontaneous intracranial hypotension, multiple intracranial abnormalities can be observed on brain MR imaging, including dural enhancement, "brain sag," and pituitary engorgement. This study seeks to create a deep learning model for the accurate diagnosis of CSF-venous fistulas via brain MR imaging.
A review of patients with clinically suspected spontaneous intracranial hypotension who underwent digital subtraction myelogram imaging preceded by brain MR imaging was performed. The patients were categorized as having a definite CSF-venous fistula, no fistula, or indeterminate findings on a digital subtraction myelogram. The data set was split into 5 folds at the patient level and stratified by label. A 5-fold cross-validation was then used to evaluate the reliability of the model. The predictive value of the model to identify patients with a CSF leak was assessed by using the area under the receiver operating characteristic curve for each validation fold.
There were 129 patients were included in this study. The median age was 54 years, and 66 (51.2%) had a CSF-venous fistula. In discriminating between positive and negative cases for CSF-venous fistulas, the classifier demonstrated an average area under the receiver operating characteristic curve of 0.8668 with a standard deviation of 0.0254 across the folds.
This study developed a deep learning model that can predict the presence of a spinal CSF-venous fistula based on brain MR imaging in patients with suspected spontaneous intracranial hypotension. However, further model refinement and external validation are necessary before clinical adoption. This research highlights the substantial potential of deep learning in diagnosing CSF-venous fistulas by using brain MR imaging.
自发性颅内低血压是一种日益被认识的病症。自发性颅内低血压是由脑脊液（CSF）漏引起的，通常与 CSF-静脉瘘有关。在自发性颅内低血压患者中，脑磁共振成像（MRI）上可观察到多种颅内异常，包括硬脑膜增强、“脑下垂”和垂体充血。本研究旨在创建一种基于脑 MRI 对 CSF-静脉瘘进行准确诊断的深度学习模型。
对经脑 MRI 检查后行数字减影脊髓造影（DS）检查的临床疑似自发性颅内低血压患者进行了回顾性研究。将患者分为明确的 CSF-静脉瘘、无瘘或 DS 不确定。数据集按患者水平分为 5 折，并按标签分层。然后使用 5 折交叉验证来评估模型的可靠性。使用每个验证折的受试者工作特征曲线下面积来评估模型识别有 CSF 漏的患者的预测价值。
本研究共纳入 129 例患者，中位年龄为 54 岁，66 例（51.2%）存在 CSF-静脉瘘。在区分 CSF-静脉瘘阳性和阴性病例方面，分类器在 5 个验证折中的平均受试者工作特征曲线下面积为 0.8668，标准差为 0.0254。
本研究开发了一种深度学习模型，可基于疑似自发性颅内低血压患者的脑 MRI 预测存在脊髓 CSF-静脉瘘。但在临床应用前，还需要进一步对模型进行改进和外部验证。本研究强调了深度学习在使用脑 MRI 诊断 CSF-静脉瘘方面的巨大潜力。
