Lesion Load in acute ischemic stroke prior to interventional treatment: Mismatch patterns vs. predictive models

Abstract

Objectives To investigate the relationship between imaging features derived from lesion loads and three month clinical assessments in ischemic stroke patients. To support clinically implementable predictive modeling with information from lesion-load features. Methods A retrospective cohort of ischemic stroke patients was studied. The dataset was dichotomized based on revascularization treatment outcome (TICI score). Three lesion delineations were derived from magnetic resoncance imaging in each group: two clinically implementable (threshold based and fully automatic prediction) and 90-day follow-up as final groundtruth. Lesion load imaging features were created through overlay of the lesion delineations on a histological brain atlas, and were correlated with the clinical assessment (NIHSS). Significance of the correlations was assessed by constructing confidence intervals using bootstrap sampling. Results Overall, high correlations between lesion loads and clinical score were observed (up to 0.859). These were mostly significant in the successful revascularized cohort and predominantly not found to be significant in the unsuccessfully revascularized cohort. £Delineations derived from acute imaging yielded on average somewhat lower correlations than delineations derived from 90-day follow-up imaging. Correlations confirmed that both total lesion volume and corticospinal tract lesion load are associated with functional outcome, and in addition lesion load on the primary somatosensory cortex BA3a was strongly correlated with NIHSS at 3 months. Fully automatic prediction was comparable to ADC threshold-based delineation on the successfully treated cohort, and superior to the Tmax threshold-based delineation in the unsuccessfully treated cohort. Conclusions While established predictors for stroke outcome (e.g. corticospinal tract integrity and total lesion volume) were re-confirmed in this analysis, further brain regions and structures were recognized as significant in contribution to three month clinical outcome as assessed by global NIHSS score. Hence, prediction models might observe an increased accuracy when incorporating regional (instead of global) lesion loads.. The results support superiority for the clinical utilization of the automatically predicted volumes from FASTER over the simpler DWI and PWI lesion delineations.