Development of composite clinical-radiological tool to predict functional outcomes after ischemic stroke treatment

Quantitative radiological tools to assess acute ischemic stroke (AIS) survivor's functional outcomes are limited. While conventional qualitative scoring systems exist, they are limited by inter-rater variability due to subtle ischemic changes. Post-intervention estimation of final cerebral infarct volume (CIV) followed by assessment is a known objective radiological determinant of functional outcomes. Hence, this study aims to develop and evaluate a composite radiological tool using radiomic imaging markers to predict long-term functional risk in AIS survivors. The dataset consists of 50 AIS patients' clinical and radiological information. First, Alberta stroke programme early CT score (ASPECTS) and posterior circulation-ASPECTS regions were annotated on scans followed by mapping them with CIV. Multiple volumetric parameters were extracted, including TBV, CIV, and the proportion of CIV to TBV. These raw features were used to compute percentage volumes and perform summation and proportion analyses w.r.to ASPECTS regions. Premorbid and clinical features were also converted to meaningful representations. Principal Component Analysis and Recursive Feature Elimination (RFE) were employed to identify optimal feature sets. Finally, different combinations of composite features were utilized to train classification algorithms. Promising results were achieved with RFE using both feature combination approaches. Support vector machine (SVM) on raw features achieved the highest AUC value (0.94±0.05), while logistic regression (LR) attained AUC value (0.93±0.05). Additional analysis indicated models trained using transformed features perform more consistently and achieve superior overall performance. The study demonstrated the potential of composite tool using radiomic and clinical information to accurately predict AIS patient's risk of long-term functional outcomes.