Multi-modal CT Perfusion-based Deep Learning for Predicting Stroke Lesion Outcomes in Complete and No Recanalization Scenarios.

Background and purpose: Predicting the final location and volume of lesions in acute ischemic stroke (AIS) is crucial for clinical management. While CT perfusion (CTP) imaging is routinely used for estimating lesion outcomes, conventional threshold-based methods have limitations. We developed specialized outcome prediction deep learning models that predict infarct core in successful reperfusion cases and the combined core-penumbra region in unsuccessful reperfusion cases.

Materials and methods: We developed single-modal and multi-modal deep learning models using CTP parameter maps to predict the final infarct lesion on follow-up diffusion-weighted imaging (DWI). Using a multi-center dataset from multiple sites, deep learning models were developed and evaluated separately for patients with complete recanalization (CR, successful reperfusion, n=350) and no recanalization (NR, unsuccessful reperfusion, n=138) after treatment. The CR model was designed to predict the infarct core region, while the NR model predicted the expanded hypoperfused tissue encompassing both core and penumbra regions. Five-fold cross-validation was performed for robust evaluation.

Results: The multi-modal 3D nnU-Net model demonstrated superior performance, achieving mean Dice scores of 35.36% in CR patients and 50.22% in NR patients. This significantly outperformed the current clinical used method, providing more accurate outcome estimates than the conventional single-modality threshold-based measures which yielded dice scores of 15.73% and 39.71% for CR and NR groups respectively.

Conclusions: Our approach offered both successful reperfusion and unsuccessful reperfusion estimations for potential treatment outcomes, enabling clinicians to better evaluate treatment eligibility for reperfusion therapies and assess potential treatment benefits. This advancement facilitates more personalized treatment recommendations and has the potential to significantly enhance clinical decision-making in AIS management by providing more accurate tissue outcome predictions than conventional single-modality threshold-based approaches.

Abbreviations: AIS=acute ischemic stroke; CR=complete recanalization; NR=no recanalization; DT=delay time; IQR=interquartile range; GT=ground truth; HD95=95% Hausdorff distance; ASSD=average symmetric surface distance; MLV=mismatch lesion volume.