OBJECTIVES Triage of patients with basilar artery occlusion for additional imaging diagnostics, therapy planning, and initial outcome prediction requires assessment of early ischemic changes in early hyperacute non-contrast computed tomography (NCCT) scans. However, accuracy of visual evaluation is impaired by inter- and intra-reader variability, artifacts in the posterior fossa and limited sensitivity for subtle density shifts. We propose a machine learning approach for detecting early ischemic changes in pc-ASPECTS regions (Posterior circulation Alberta Stroke Program Early CT Score) based on admission NCCTs. METHODS The retrospective study includes 552 pc-ASPECTS regions (144 with infarctions in follow-up NCCTs) extracted from pre-therapeutic early hyperacute scans of 69 patients with basilar artery occlusion that later underwent successful recanalization. We evaluated 1218 quantitative image features utilizing random forest algorithms with fivefold cross-validation for the ability to detect early ischemic changes in hyperacute images that lead to definitive infarctions in follow-up imaging. Classifier performance was compared to conventional readings of two neuroradiologists. RESULTS Receiver operating characteristic area under the curves for detection of early ischemic changes were 0.70 (95% CI [0.64; 0.75]) for cerebellum to 0.82 (95% CI [0.77; 0.86]) for thalamus. Predictive performance of the classifier was significantly higher compared to visual reading for thalamus, midbrain, and pons (P value < 0.05). CONCLUSIONS Quantitative features of early hyperacute NCCTs can be used to detect early ischemic changes in pc-ASPECTS regions. The classifier performance was higher or equal to results of human raters. The proposed approach could facilitate reproducible analysis in research and may allow standardized assessments for outcome prediction and therapy planning in clinical routine.

中文翻译：

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后循环卒中：基于机器学习的急性非造影CT扫描早期缺血性变化的检测。

目的对基底动脉闭塞患者进行分类以进行其他影像学诊断，治疗计划和初步结果预测，需要评估早期超急性非对比计算机断层扫描（NCCT）扫描中的早期缺血性改变。但是，阅读器之间和阅读器内部的可变性，后颅窝中的伪影以及对细微密度变化的敏感性有限，会损害视觉评估的准确性。我们提出了一种基于入学NCCT的pc-ASPECTS地区（后循环艾伯塔省卒中计划早期CT评分）检测早期缺血性变化的机器学习方法。方法回顾性研究包括从69例基底动脉闭塞患者的治疗前早期超急性扫描中提取的552个pc-ASPECTS区域（在后续NCCT中有144例梗塞），随后对这些患者进行了成功的再通气。我们使用具有五重交叉验证的随机森林算法评估了1218个定量图像特征，以检测超急性图像的早期缺血性变化，从而导致后续成像中明确的梗塞。将分类器的性能与两名神经放射科医生的常规读数进行了比较。结果用于检测早期缺血性变化的曲线下的受体工作特征区域为小脑为0.70（95％CI [0.64; 0.75]），丘脑为0.82（95％CI [0.77; 0.86]）。与丘脑，中脑和脑桥的视觉读数相比，分类器的预测性能明显更高（P值<0.05）。结论早期超急性NCCT的定量特征可用于检测pc-ASPECTS区域中的早期缺血变化。分类器的性能高于或等于人类评估者的结果。所提出的方法可以促进研究中的可重复分析，并且可以在临床常规中对结果预测和治疗计划进行标准化评估。