Deep learning using one-stop-shop CT scan to predict hemorrhagic transformation in stroke patients undergoing reperfusion therapy: A multicenter study.

Abstract

Rationale and objectives: Hemorrhagic transformation (HT) is one of the most serious complications in patients with acute ischemic stroke (AIS) following reperfusion therapy. The purpose of this study is to develop and validate deep learning (DL) models utilizing multiphase computed tomography angiography (CTA) and computed tomography perfusion (CTP) images for the fully automated prediction of HT.

Materials and methods: In this multicenter retrospective study, a total of 229 AIS patients who underwent reperfusion therapy from June 2019 to May 2022 were reviewed. Data set 1, comprising 183 patients from two hospitals, was utilized for training, tuning, and internal validation. Data set 2, consisting of 46 patients from a third hospital, was employed for external testing. DL models were trained to extract valuable information from multiphase CTA and CTP images. The DenseNet architecture was used to construct the DL models. We developed single-phase, single-parameter models, and combined models to predict HT. The models were evaluated using receiver operating characteristic curves.

Results: Sixty-nine (30.1%) of 229 patients (mean age, 66.9 years ± 10.3; male, 144 [66.9%]) developed HT. Among the single-phase models, the arteriovenous phase model demonstrated the highest performance. For single-parameter models, the time-to-peak model was superior. When considering combined models, the CTA-CTP model provided the highest predictive accuracy.

Conclusions: DL models for predicting HT based on multiphase CTA and CTP images can be established and performed well, providing a reliable tool for clinicians to make treatment decisions.