This research aims to evaluate how deep learning-based non-contrast CT markers can predict core volume after patient transfer.
Analyzed total and severely hypodense DLNCCT volumes as predictors.
Utilized imaging data to assess core volume prediction improvements.
Compared DLNCCT markers with CTP-based core volume predictions.
Identified DLNCCT volumes as independent predictors of core volume post-transfer.
Demonstrated that these markers significantly improve prediction accuracy.
Abstract
Total and severely hypodense DLNCCT volumes are independent predictors for post-transfer core volume. These DLNCCT markers improved CTP-based post-transfer core volume prediction.