RT Journal Article
SR Electronic
T1 Can CTA-based Machine Learning Identify Patients for Whom Successful Endovascular Stroke Therapy is Insufficient?
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP ajnr.A8885
DO 10.3174/ajnr.A8885
A1 Jeevarajan, Jerome A
A1 Dong, Yingjun
A1 Ballekere, Anjan
A1 Marioni, Sergio Salazar
A1 Niktabe, Arash
A1 Abdelkhaleq, Rania
A1 Sheth, Sunil A
A1 Giancardo, Luca
YR 2025
UL http://www.ajnr.org/content/early/2025/06/18/ajnr.A8885.abstract
AB BACKGROUND AND PURPOSE: Despite advances in endovascular stroke therapy (EST) devices and techniques, many patients are left with substantial disability, even if the final infarct volumes (FIVs) remain small. Here, we evaluate the performance of a machine learning (ML) approach using pre-treatment CT angiography (CTA) to identify this cohort of patients that may benefit from additional interventions.MATERIALS AND METHODS: We identified consecutive large vessel occlusion (LVO) acute ischemic stroke (AIS) subjects who underwent EST with successful reperfusion in a multicenter prospective registry cohort. We included only subjects with FIV<30mL and recorded 90-day outcome (modified Rankin scale, mRS). A deep learning model was pre-trained and then fine-tuned to predict 90-day mRS 0-2 using pre-treatment CTA images (DSN-CTA model). The primary outcome was the predictive performance of the DSNCTA model compared to a logistic regression model with clinical variables, measured by the area under the receiver operating characteristic curve (AUROC).RESULTS: The DSN-CTA model was pre-trained on 1,542 subjects and then fine-tuned and cross-validated with 48 subjects, all of whom underwent EST with TICI 2b-3 reperfusion. Of this cohort, 56.2% of subjects had 90-day mRS 3-6 despite successful EST and FIV<30mL. The DSN-CTA model showed significantly better performance than a model with clinical variables alone when predicting good 90-day mRS (AUROC 0.81 vs 0.492, p=0.006).CONCLUSIONS: The CTA-based machine learning model was able to more reliably predict unexpected poor functional outcome after successful EST and small FIV for patients with LVO AIS compared to standard clinical variables. ML models may identify a priori patients in whom EST-based LVO reperfusion alone is insufficient to improve clinical outcomes.ABBREVIATIONS: AIS＝ acute ischemic stroke; AUROC＝ area under the receiver operating characteristic curve; DSN-CTA＝ DeepSymNet-v3 model; EST＝ endovascular stroke therapy; FIV＝ final infarct volume; LVO＝ large vessel occlusion; ML＝ machine learning.