Fusion Learning from Non-contrast CT Scans for the Detection of Hemorrhagic Transformation in Stroke Patients

Hemorrhagic transformation (HT) is a potentially catastrophic complication after acute ischemic stroke. Prevention of HT risk is crucial because it worsens prognosis and increases mortality. This study aimed at developing and validating a computer-aided diagnosis system using pretreatment non-contrast computed tomography (CT) scans for HT prediction in stroke patients undergoing revascularization. This retrospective study included all acute ischemic stroke patients with non-contrast CT before reperfusion therapy who also underwent follow-up MRI from January 2018 to December 2022. Among the 188 evaluated patients, any degree of HT at follow-up imaging was observed in 103 patients. HT diagnosis via MRI was defined as the reference standard for neuroradiologists. Using a database of 2076 serial non-contrast CT images of the brain, pretrained deep learning architectures such as convolutional neural networks and vision transformers (ViTs) were used for feature extraction. The performance of the predictive HT risk model was evaluated via tenfold cross-validation in machine learning classifiers. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were evaluated. Using an individual deep learning architecture, DenseNet201 features achieved the highest accuracy of 87% and an AUC of 0.8863 in the classifier of the subspace ensemble k-nearest neighbor. By combining the DenseNet201 and ViT features, the accuracy and AUC can be improved to 88% and 0.8987, respectively, which are significantly better than those of using ViT alone. Detecting HT in stroke patients is a meaningful but challenging issue. On the basis of the model approach, HT diagnosis would be more automatic, efficient, and consistent, which would be helpful in clinic use.