Oura Daisuke, Takamiya Soichiro, Ihara Riku, Niiya Yoshimasa, Sugimori Hiroyuki
Department of Radiology, Otaru General Hospital, Otaru 047-0152, Japan.
Graduate School of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan.
Diagnostics (Basel). 2023 Jun 21;13(13):2138. doi: 10.3390/diagnostics13132138.
Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data. The threshold ranged from 620 × 10 mm/s to 480 × 10 mm/s with a 20 × 10 mm/s step. The mean, standard deviation, and pixel number of the region of interest were obtained according to the threshold. Simulation data were created by mean measurement value of patients with a modified Rankin score of 3-4. The time limit was simulated from the cross point of the prediction score according to the time to perform reperfusion from imaging. The extra tree classifier accurately predicted the outcome (AUC: 0.833. Accuracy: 0.933). In simulation data, the prediction score to obtain a good outcome decreased according to increasing time to reperfusion, and the time limit was longer among younger patients. ML methods using detailed ADC analysis accurately predicted patient outcomes in AIS and simulated tolerance time for MT.
对于急性缺血性卒中（AIS）患者，预测机械取栓（MT）后的预后仍然具有挑战性。本研究旨在探讨使用详细表观扩散系数（ADC）分析的机器学习（ML）方法在预测AIS患者预后及模拟MT时间限制方面的实用性。共纳入75例在MT中实现完全再灌注的连续AIS患者；20%被分离作为测试数据。阈值范围为620×10⁻⁶mm²/s至480×10⁻⁶mm²/s，步长为20×10⁻⁶mm²/s。根据阈值获取感兴趣区域的平均值、标准差和像素数。模拟数据由改良Rankin评分3 - 4分患者的平均测量值创建。根据成像中进行再灌注的时间，从预测评分的交叉点模拟时间限制。极端随机树分类器准确预测了预后（曲线下面积：0.833，准确率：0.933）。在模拟数据中，随着再灌注时间增加，获得良好预后的预测评分降低，且年轻患者的时间限制更长。使用详细ADC分析的ML方法准确预测了AIS患者的预后，并模拟了MT的耐受时间。
