Puzio Tomasz, Matera Katarzyna, Wiśniewski Karol, Grobelna Milena, Wanibuchi Sora, Jaskólski Dariusz J, Bobeff Ernest J
Department of Diagnostic Imaging, Polish Mothers' Memorial Hospital Research Institute, Łódź, Poland.
Department of Neurosurgery and Neuro-Oncology, Barlicki University Hospital, Medical University of Lodz, Łódź, Poland.
Front Neurosci. 2024 Feb 19;18:1341734. doi: 10.3389/fnins.2024.1341734. eCollection 2024.
Intracranial space is divided into three compartments by the falx cerebri and tentorium cerebelli. We assessed whether cerebrospinal fluid (CSF) distribution evaluated by a specifically developed deep-learning neural network (DLNN) could assist in quantifying mass effect.
Head trauma CT scans from a high-volume emergency department between 2018 and 2020 were retrospectively analyzed. Manual segmentations of intracranial compartments and CSF served as the ground truth to develop a DLNN model to automate the segmentation process. Dice Similarity Coefficient (DSC) was used to evaluate the segmentation performance. Supratentorial CSF Ratio was calculated by dividing the volume of CSF on the side with reduced CSF reserve by the volume of CSF on the opposite side.
Two hundred and seventy-four patients (mean age, 61 years ± 18.6) after traumatic brain injury (TBI) who had an emergency head CT scan were included. The average DSC for training and validation datasets were respectively: 0.782 and 0.765. Lower DSC were observed in the segmentation of CSF, respectively 0.589, 0.615, and 0.572 for the right supratentorial, left supratentorial, and infratentorial CSF regions in the training dataset, and slightly lower values in the validation dataset, respectively 0.567, 0.574, and 0.556. Twenty-two patients (8%) had midline shift exceeding 5 mm, and 24 (8.8%) presented with high/mixed density lesion exceeding >25 ml. Fifty-five patients (20.1%) exhibited mass effect requiring neurosurgical treatment. They had lower supratentorial CSF volume and lower Supratentorial CSF Ratio (both < 0.001). A Supratentorial CSF Ratio below 60% had a sensitivity of 74.5% and specificity of 87.7% (AUC 0.88, 95%CI 0.82-0.94) in identifying patients that require neurosurgical treatment for mass effect. On the other hand, patients with CSF constituting 10-20% of the intracranial space, with 80-90% of CSF specifically in the supratentorial compartment, and whose Supratentorial CSF Ratio exceeded 80% had minimal risk.
CSF distribution may be presented as quantifiable ratios that help to predict surgery in patients after TBI. Automated segmentation of intracranial compartments using the DLNN model demonstrates a potential of artificial intelligence in quantifying mass effect. Further validation of the described method is necessary to confirm its efficacy in triaging patients and identifying those who require neurosurgical treatment.
颅内空间被大脑镰和小脑幕分为三个腔室。我们评估了通过专门开发的深度学习神经网络（DLNN）评估的脑脊液（CSF）分布是否有助于量化占位效应。
回顾性分析了2018年至2020年期间一家大容量急诊科的头部创伤CT扫描。颅内腔室和脑脊液的手动分割作为开发DLNN模型以自动分割过程的基础真值。使用骰子相似系数（DSC）评估分割性能。幕上脑脊液比率通过将脑脊液储备减少一侧的脑脊液体积除以对侧的脑脊液体积来计算。
纳入了274例创伤性脑损伤（TBI）后接受急诊头部CT扫描的患者（平均年龄，61岁±18.6）。训练和验证数据集的平均DSC分别为：0.782和0.765。在脑脊液分割中观察到较低的DSC，训练数据集中右侧幕上、左侧幕上和幕下脑脊液区域的DSC分别为0.589、0.615和0.572，验证数据集中的值略低，分别为0.567、0.574和0.556。22例患者（8%）中线移位超过5mm，24例患者（8.8%）出现高密度/混合密度病变超过25ml。55例患者（20.1%）表现出需要神经外科治疗的占位效应。他们的幕上脑脊液体积和幕上脑脊液比率较低（均<0.001）。幕上脑脊液比率低于60%在识别因占位效应需要神经外科治疗的患者时，敏感性为74.5%，特异性为87.7%（AUC 0.88，95%CI 0.82 - 0.94）。另一方面，脑脊液占颅内空间10 - 20%、其中脑脊液80 - 90%专门位于幕上腔室且幕上脑脊液比率超过80%的患者风险最小。
脑脊液分布可以呈现为可量化的比率，有助于预测TBI后患者的手术需求。使用DLNN模型对颅内腔室进行自动分割证明了人工智能在量化占位效应方面的潜力。需要对所描述的方法进行进一步验证，以确认其在对患者进行分诊和识别需要神经外科治疗的患者方面的有效性。
