Fractal-Based Quantitative Collateral Assessment for Thrombectomy Candidate Selection in Acute Ischemic Stroke: A Preliminary Study

Background: Acute ischemic stroke (AIS) remains a leading cause of mortality and disability worldwide. Accurate evaluation of collateral circulation is essential for predicting outcomes following endovascular thrombectomy (EVT). However, conventional visual collateral scoring (vCS) based on multiphase CT angiography (mCTA) is limited by subjectivity and inter-observer variability. This preliminary study introduces the multiphase quantitative collateral score (mqCS), a novel imaging biomarker designed to provide an objective and reproducible assessment of both the morphological extent and temporal dynamics of collateral flow. Methods: In this exploratory study, 54 AIS patients treated with EVT were retrospectively analyzed. Collateral status was evaluated using both vCS (graded by two blinded neuroradiologists) and mqCS, derived from mCTA-based fractal dimension (FD) and delay indicator (DI) metrics. Logistic regression and receiver operating characteristic (ROC) analyses were performed to assess the predictive value of each scoring system for favorable 90-day functional outcomes (modified Rankin scale, mRS ≤ 2). Results: The mqCS was significantly associated with favorable outcomes. Patients with mqCS ≥ 0.8674 had significantly higher odds of achieving favorable outcomes (adjusted OR = 5.98, 95% CI: 1.38–25.93, p = 0.017; AUC = 0.80). In comparison, the visual collateral score (vCS) showed a lower adjusted predictive value (adjusted OR = 2.84, 95% CI: 1.17–6.89, p = 0.02; AUC = 0.79). Patients in the highest mqCS quartiles (Q3–Q4) exhibited significantly better recovery rates (69%, p < 0.01). Conclusions: This proof-of-concept study suggests that mqCS provides a potentially more objective and robust alternative to visual scoring for collateral assessment in AIS. By integrating structural and temporal characteristics, mqCS enhances outcome prediction and may inform EVT decision-making, particularly in borderline cases. These preliminary findings warrant validation in larger, prospective cohorts and support its potential integration into automated imaging platforms.