Dataset for: Individualized Quantification of the Benefit from Reperfusion Therapy using Stroke Predictive Models

Purpose: Recent imaging developments have shown the potential of voxel-based models in assessing infarct growth after stroke. Many models have been proposed but their relevance in predicting the benefit of a reperfusion therapy remains unclear. We searched for a predictive model whose volumetric predictions would identify stroke patients who are to benefit from tissue Plasminogen Activator (t-PA)-induced reperfusion. Material and Methods: Forty-five cases were used to study retrospectively stroke progression from admission to end of follow-up. Predictive approaches based on various statistical models, predictive variables, and spatial filtering methods were compared. The optimal approach was chosen according to the area under the precision-recall curve (AUPRC). The final lesion volume was then predicted assuming that the patient would or would not reperfuse. Patients with an acute lesion of ≤50 mL and a predicted reduction in presence of reperfusion >6 mL, and >25% of the acute lesion were classified as responders. Results: The optimal model was a logistic regression using the voxel distance to the acute lesion, the volume of the acute lesion, and Gaussian-filtered MRI contrast parameters as predictive variables. The predictions gave a median AUPRC of 0.655, a median AUC of 0.976, and a median volumetric error of 8.29mL. Nineteen patients matched the responder profile. A non-significant trend of improved reduction in NIHSS score (-42.8%, p=0.09) and in lesion volume (-78.1%, p=0.21) following reperfusion was observed for responder patients. Conclusion: Despite limited volumetric accuracy, predictive stroke models can be used to quantify the benefit of reperfusion therapies.