Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT,European Radiology

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Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT
European Radiology ( IF 4.7 ) Pub Date : 2023-11-11 , DOI: 10.1007/s00330-023-10432-6
JoonNyung Heo _{1,

2} , Youngno Yoon ₃ , Hyun Jin Han ₄ , Jung-Jae Kim ₄ , Keun Young Park ₄ , Byung Moon Kim ₁ , Dong Joon Kim ₁ , Young Dae Kim ₂ , Hyo Suk Nam ₂ , Seung-Koo Lee ₁ , Beomseok Sohn _{1,

5}

Affiliation

Objectives

To develop and validate a deep learning model for predicting hemorrhagic transformation after endovascular thrombectomy using dual-energy computed tomography (CT).

Materials and methods

This was a retrospective study from a prospective registry of acute ischemic stroke. Patients admitted between May 2019 and February 2023 who underwent endovascular thrombectomy for acute anterior circulation occlusions were enrolled. Hemorrhagic transformation was defined using follow-up magnetic resonance imaging or CT. The deep learning model was developed using post-thrombectomy dual-energy CT to predict hemorrhagic transformation within 72 h. Temporal validation was performed with patients who were admitted after July 2022. The deep learning model’s performance was compared with a logistic regression model developed from clinical variables using the area under the receiver operating characteristic curve (AUC).

Results

Total of 202 patients (mean age 71.4 years ± 14.5 [standard deviation], 92 men) were included, with 109 (54.0%) patients having hemorrhagic transformation. The deep learning model performed consistently well, showing an average AUC of 0.867 (95% confidence interval [CI], 0.815–0.902) upon five-fold cross validation and AUC of 0.911 (95% CI, 0.774–1.000) with the test dataset. The clinical variable model showed an AUC of 0.775 (95% CI, 0.709–0.842) on the training dataset (p < 0.01) and AUC of 0.634 (95% CI, 0.385–0.883) on the test dataset (p = 0.06).

Conclusion

A deep learning model was developed and validated for prediction of hemorrhagic transformation after endovascular thrombectomy in patients with acute stroke using dual-energy computed tomography.

Clinical relevance statement

This study demonstrates that a convolutional neural network (CNN) can be utilized on dual-energy computed tomography (DECT) for the accurate prediction of hemorrhagic transformation after thrombectomy. The CNN achieves high performance without the need for region of interest drawing.

Key Points

• Iodine leakage on dual-energy CT after thrombectomy may be from blood-brain barrier disruption.

• A convolutional neural network on post-thrombectomy dual-energy CT enables individualized prediction of hemorrhagic transformation.

• Iodine leakage is an important predictor of hemorrhagic transformation following thrombectomy for ischemic stroke.

中文翻译：

双能CT深度学习预测血栓切除术后脑出血转化

目标

开发并验证使用双能计算机断层扫描 (CT) 预测血管内血栓切除术后出血转化的深度学习模型。

材料和方法

这是一项针对急性缺血性中风前瞻性登记的回顾性研究。纳入2019年5月至2023年2月期间因急性前循环闭塞而接受血管内血栓切除术的患者。使用后续磁共振成像或 CT 来定义出血转化。利用血栓切除后双能 CT 开发深度学习模型来预测 72 小时内的出血转化。对 2022 年 7 月后入院的患者进行了时间验证。将深度学习模型的性能与使用受试者工作特征曲线 (AUC) 下面积的临床变量开发的逻辑回归模型进行了比较。

结果

总共纳入 202 名患者（平均年龄 71.4 岁 ± 14.5 [标准差]，92 名男性），其中 109 名 (54.0%) 患者出现出血性转化。深度学习模型始终表现良好，五倍交叉验证后的平均 AUC 为 0.867（95% 置信区间 [CI]，0.815–0.902），测试数据集的 AUC 为 0.911（95% CI，0.774–1.000）。临床变量模型显示，训练数据集上的 AUC 为 0.775（95% CI，0.709–0.842）（ p < 0.01），测试数据集上的 AUC 为 0.634（95% CI，0.385–0.883）（ p = 0.06）。