Multimodal CT Perfusion-Based Deep Learning for Predicting Stroke Lesion Outcomes in Complete and No Recanalization Scenarios

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Multimodal model: 35.36% (CR), 50.22% (NR) vs conventional: 15.73% (CR), 39.71% (NR)

• Primary Outcome: Dice similarity coefficient for lesion prediction accuracy

• Nnt Or Sensitivity: Sensitivity and specificity not reported; only Dice coefficients available

• Confidence Interval: Not reported

Clinical Application

Requires specialized deep learning infrastructure and CTP imaging capabilities, limiting immediate widespread implementation Relevant to Australian stroke units with CTP capabilities. Would need TGA approval for clinical use. Could inform PBS-funded reperfusion therapy decisions if validated Acute ischemic stroke patients being considered for reperfusion therapy

Abstract

BACKGROUND AND PURPOSE: Predicting the final location and volume of lesions in acute ischemic stroke is crucial for clinical management. While CTP 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 multimodal deep learning models using CTP parameter maps to predict the final infarct lesion on follow-up DWI. Using a multicenter data set from multiple sites, we developed deep learning models and evaluated them separately for patients with complete recanalization (successful reperfusion [CR], n = 350) and no recanalization (unsuccessful reperfusion [NR], 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 the core and penumbra regions. Five-fold cross-validation was performed for robust evaluation. RESULTS: The multimodal 3D nnU-Net model demonstrated superior performance, achieving mean Dice scores of 35.36% in patients with CR and 50.22% in those with NR. This model substantially outperformed the current clinically used method, providing more accurate outcome estimates than the conventional single-technique 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 substantially enhance clinical decision-making in acute ischemic stroke management by providing more accurate tissue outcome predictions than conventional single-technique threshold-based approaches.

References

1. 1.Yang, H., George, Y., Mehta, D., Lin, L., Chen, C., Yang, D., Sun, J., Lau, K. F., Bain, C., Yang, Q., Parsons, M. W., & Ge, Z. (2026). Multimodal CT perfusion-based deep learning for predicting stroke lesion outcomes in complete and no recanalization scenarios. AJNR. American Journal of Neuroradiology. https://doi.org/10.1371/journal.pone.0228113