Radiology:机器学习帮你找到并计算急性缺血性脑梗死灶

2020-05-11 shaosai MedSci原创

背景

背景

在发病时能够识别急性梗死灶及其程度对急性缺血性脑卒中(AIS)患者的治疗是非常重要的。大面积梗死的患者从溶栓或机械取栓术中获益的可能性较小。

目的

本研究 旨在利用非强化CT检查建立能够评价AIS患者急性梗死灶及其体积的自动计算方法。

 

材料与方法

本研究收集了AIS患者(发病到CT检查<6小时)的非增强CT图像,所有患者均在AIS发病后1小时内行扩散加权(DW)MRI检查。以人工勾画缺血性脑梗死灶体积作为参考标准。建立包含机器学习(ML)的自动分割方法来识别脑梗死灶。随机选择157例经人工勾画DW MRI图像梗死病灶的非增强图像进行训练和验证ML模型,剩余100例患者作为独立组进行测试。利用Bland-Altman plots和pearson相关性分析定量比较ML算法与参考标准(DW MRI)的差异。

结果

在100例测试组,基线时非增强CT检查时间为发病后中位数48分钟;基线MRI检查时间为CT检查后38分钟。算法检查病变体积与参考标准人工勾画病变体积具有相关性。在算法分割体积与DW MRI显示病变体积的平均差异为11ml。

结论

利用机器学习算法自动分割非增强CT图像脑梗死体积与扩散加权MRI检查中脑梗死体积具有良好的一致性。

原始出处:

Qiu W, Kuang H, Teleg E. et al. Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT. DOI:10.1148/radiol.2020191193

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    2020-05-13 karmond
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