Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke.
Quantitative Serial CT Imaging-Derived Features Improve Prediction of Malignant Cerebral Edema after Ischemic Stroke.
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DOI：
10.1007/s12028-020-01056-5
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发表时间：
2020-12
影响因子：
3.5
通讯作者：
Dhar R
中科院分区：
文献类型：
--
作者：
Foroushani HM;Hamzehloo A;Kumar A;Chen Y;Heitsch L;Slowik A;Strbian D;Lee JM;Marcus DS;Dhar R
一小部分半球性卒中患者会发生恶性脑水肿，如果不及时进行减压性半颅骨切除术（DHC），会导致病情恶化和死亡。仅根据临床数据来预测哪些中风患者会发生恶性水肿是不准确的。头部计算机断层扫描（CT）成像通常在基线和24小时进行。我们确定了结合系列CT的成像衍生特征以增强恶性水肿预测的增量价值。我们在三个研究中心确定了具有基线以及24小时临床和CT成像数据的NIHSS ≥ 7的半球卒中患者。我们从基线和随访CT中提取定量成像特征，包括CSF体积、颅内储备（CSF/颅体积）以及中线移位（MLS）和梗死相关低密度体积。潜在致死性恶性水肿定义为需要DHC或死于MLS超过5 mm。我们首先使用基线数据，然后添加24小时数据（包括CSF体积减少（ΔCSF）），使用logistic回归建立机器学习模型。使用召回（敏感性），精确度（预测值）以及受试者操作特征和精确度召回曲线下的面积（AUROC，AUPRC）的指标，通过交叉验证评估模型性能。361名患者中有20名（6%）死亡或接受DHC。仅基线临床变量的回忆率为60%，精确度较低（7%），AUROC 0.59，AUPRC 0.15。增加基线颅内储备使回忆率提高到80%，AUROC提高到0.82，但精确度仅为16%（AUPRC 0.28）。校正ΔCSF使AUPRC提高到0.53（AUROC 0.91），而所有成像特征进一步提高了预测（召回率90%，准确率38%，AUROC 0.96，AUPRC 0.66）。从基线和24小时CT中量化基于CT的成像特征，可增强对需要DHC的恶性水肿患者的识别。需要对这种基于成像的机器学习模型进行进一步的改进和外部验证。
Malignant cerebral edema develops in a small subset of patients with hemispheric strokes, precipitating deterioration and death if decompressive hemicraniectomy (DHC) is not performed in a timely manner. Predicting which stroke patients will develop malignant edema is imprecise based on clinical data alone. Head computed tomography (CT) imaging is often performed at baseline and 24-hours. We determined the incremental value of incorporating imaging-derived features from serial CTs to enhance prediction of malignant edema. We identified hemispheric stroke patients at three sites with NIHSS ≥ 7 who had baseline as well as 24-hour clinical and CT-imaging data. We extracted quantitative imaging features from baseline and follow-up CTs, including CSF volume, intracranial reserve (CSF/cranial volume), as well as midline shift (MLS) and infarct-related hypodensity volume. Potentially lethal malignant edema was defined as requiring DHC or dying with MLS over 5-mm. We built machine-learning models using logistic regression first with baseline data and then adding 24-hour data including reduction in CSF volume (ΔCSF). Model performance was evaluated with cross-validation using metrics of recall (sensitivity), precision (predictive value), as well as area under receiver-operating-characteristic and precision-recall curves (AUROC, AUPRC). Twenty of 361 patients (6%) died or underwent DHC. Baseline clinical variables alone had recall of 60% with low precision (7%), AUROC 0.59, AUPRC 0.15. Adding baseline intracranial reserve improved recall to 80% and AUROC to 0.82 but precision remained only 16% (AUPRC 0.28). Incorporating ΔCSF improved AUPRC to 0.53 (AUROC 0.91) while all imaging features further improved prediction (recall 90%, precision 38%, AUROC 0.96, AUPRC 0.66). Incorporating quantitative CT-based imaging features from baseline and 24-hour CT enhances identification of patients with malignant edema needing DHC. Further refinements and external validation of such imaging-based machine learning models are required.
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