Abstract：
OBJECTIVE: Hematoma expansion (HE) in intracerebral hemorrhage (ICH) is a critical factor affecting patient outcomes, yet effective clinical tools for predicting HE are currently lacking. We aim to develop a fully automated framework based on deep learning for predicting HE using only clinical non-contrast CT (NCCT) scans.
METHODS: A large retrospective dataset (n = 2484) was collected from 84 centers, while a prospective dataset (n = 500) was obtained from 26 additional centers. Baseline NCCT scans and follow-up NCCT scans were conducted within 6 h and 48 h from symptom onset, respectively. HE was defined as a volume increase of more than 6 mL on the follow-up NCCT. The retrospective dataset was divided into a training set (n = 1876) and a validation set (n = 608) by patient inclusion time. A two-stage framework was trained to predict HE, and its performance was evaluated on both the validation and prospective sets. Receiver operating characteristics area under the curve (AUC), sensitivity, and specificity were leveraged.
RESULTS: Our two-stage framework achieved an AUC of 0.760 (95% CI 0.724-0.799) on the retrospective validation set and 0.806 (95% CI 0.750-0.859) on the prospective set, outperforming the commonly used BAT score, which had AUCs of 0.582 and 0.699, respectively.
CONCLUSIONS: Our framework can automatically and robustly identify ICH patients at high risk of HE using admission head NCCT scans, providing more accurate predictions than the BAT score.
METHODS: A large retrospective dataset (n = 2484) was collected from 84 centers, while a prospective dataset (n = 500) was obtained from 26 additional centers. Baseline NCCT scans and follow-up NCCT scans were conducted within 6 h and 48 h from symptom onset, respectively. HE was defined as a volume increase of more than 6 mL on the follow-up NCCT. The retrospective dataset was divided into a training set (n = 1876) and a validation set (n = 608) by patient inclusion time. A two-stage framework was trained to predict HE, and its performance was evaluated on both the validation and prospective sets. Receiver operating characteristics area under the curve (AUC), sensitivity, and specificity were leveraged.
RESULTS: Our two-stage framework achieved an AUC of 0.760 (95% CI 0.724-0.799) on the retrospective validation set and 0.806 (95% CI 0.750-0.859) on the prospective set, outperforming the commonly used BAT score, which had AUCs of 0.582 and 0.699, respectively.
CONCLUSIONS: Our framework can automatically and robustly identify ICH patients at high risk of HE using admission head NCCT scans, providing more accurate predictions than the BAT score.
摘要：
目的：脑出血（ICH）中血肿扩大（HE）是影响患者预后的关键因素，然而，目前缺乏预测HE的有效临床工具。我们的目标是开发一个基于深度学习的全自动框架，用于仅使用临床非对比CT（NCCT）扫描来预测HE。
方法：从84个中心收集了一个大型回顾性数据集（n=2484），而前瞻性数据集（n=500）来自26个其他中心。基线NCCT扫描和随访NCCT扫描在6小时和48小时内从症状发作，分别。HE定义为随访NCCT时体积增加超过6mL。根据患者纳入时间将回顾性数据集分为训练集(n=1876)和验证集(n=608)。训练了一个两阶段框架来预测HE，并在验证集和预期集上评估了其性能。接收机工作特性曲线下面积(AUC),灵敏度,和特异性被利用。
结果：我们的两阶段框架在回顾性验证集上的AUC为0.760（95％CI0.724-0.799），在前瞻性集上的AUC为0.806（95％CI0.750-0.859），优于常用的BAT得分，其AUC分别为0.582和0.699。
结论：我们的框架可以使用入院头颅NCCT扫描自动且稳健地识别处于HE高风险的ICH患者，提供比BAT分数更准确的预测。
方法：从84个中心收集了一个大型回顾性数据集（n=2484），而前瞻性数据集（n=500）来自26个其他中心。基线NCCT扫描和随访NCCT扫描在6小时和48小时内从症状发作，分别。HE定义为随访NCCT时体积增加超过6mL。根据患者纳入时间将回顾性数据集分为训练集(n=1876)和验证集(n=608)。训练了一个两阶段框架来预测HE，并在验证集和预期集上评估了其性能。接收机工作特性曲线下面积(AUC),灵敏度,和特异性被利用。
结果：我们的两阶段框架在回顾性验证集上的AUC为0.760（95％CI0.724-0.799），在前瞻性集上的AUC为0.806（95％CI0.750-0.859），优于常用的BAT得分，其AUC分别为0.582和0.699。
结论：我们的框架可以使用入院头颅NCCT扫描自动且稳健地识别处于HE高风险的ICH患者，提供比BAT分数更准确的预测。
