NornirNet: A Deep Learning Framework to Distinguish Benign From Malignant Type II Endoleaks After Endovascular Aortic Aneurysm Repair Using Preoperative Imaging

Abstract Background Type II endoleak (T2EL) is the most common complication after endovascular aortic aneurysm repair (EVAR). While pre-emptive embolization of side branches may reduce T2EL and reintervention rates, its clinical benefit remains unconfirmed. Current guidelines recommend considering pre-emptive embolization only in selected cases. Aims This study proposes a deep learning framework for preoperative prediction of T2EL occurrence and severity using volumetric computed tomography angiography (CTA) data. Methods A retrospective analysis was conducted on 277 patients who underwent standard EVAR (2010–2024). Preoperative CTA scans were processed for volumetric normalization and fed into a 3D convolutional neural network (CNN), which was trained to classify patients into three categories: no T2EL, benign T2EL, or malignant T2EL. The model was trained on 175 cases, validated on 72, and tested on an independent cohort of 30 patients. The CNN’s performance was evaluated by comparing its predictions with follow-up CTA data. Performance metrics included accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results Median follow-up time was 55.5 months 28.03-91.6. During this time a total of 82 (29.6%) T2EL were recorded: 38 (46.3%) displayed significant sac enlargement. The CNN achieved an overall accuracy of 76.7% (95% CI: 0.63–0.90), macro-averaged F1-score of 0.77, and AUC of 0.93. Class-specific AUCs were 0.93 for no T2EL, 0.91 for "benign", and 0.96 for "malignant" cases, confirming high discriminative capacity across outcomes. Most misclassifications occurred between adjacent categories. Conclusion This study introduces the first end-to-end 3D CNN capable of predicting both presence and severity of T2EL directly from preoperative CTA, without manual segmentation or handcrafted features. These findings suggest that preoperative imaging encodes latent structural information predictive of endoleak-driven sac reperfusion, potentially enabling personalized pre-emptive embolization strategies and tailored surveillance after EVAR.