ArticleCardiovascular and interventional radiology2026
Predicting Antegrade Success in Femoropopliteal Occlusions Using Radiological and Clinical Machine Learning Models.
Article in Cardiovascular and interventional radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Interventionalist Intuition to Algorithm: Can Machine Learning Predict the Uncrossable Lesion in Femoropopliteal Occlusions?Cardiovascular and interventional radiology · 2026Article
- Response to: "Important Considerations for the TIVAD Infection Prediction Model and its Clinical Application".Cardiovascular and interventional radiology · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeTo develop and validate a machine learning model integrating imaging, demographic, and laboratory features to predict the technical success of antegrade endovascular approaches for femoropopliteal artery occlusion. MATERIALS AND
methodsThe retrospective multicenter study included 379 femoropopliteal artery interventions (training set: n = 264; internal test set: n = 66; external test set: n = 49) treated between January 2020 and June 2023. Radiological features-plaque burden, composition, vessel remodeling, and occlusion length-were extracted from non-contrast and contrast-enhanced CT. Clinical features included demographics, comorbidities, and laboratory results. Feature selection was performed using univariate and multivariate analysis. A random forest model was developed with three variations: clinical, radiological, and combined clinical-radiological. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA).
resultsTechnical failure occurred in 136 of 379 interventions (36%). Thirteen key predictors were identified, including hypertension, low-density lipoprotein, aspartate transaminase, occlusion length, Agatston score, and other imaging-based features. The clinical, radiological, and combined clinical-radiological models were developed based on the selected features. The combined model showed the highest performance, with AUC values of 0.81 in the training set, 0.77 in the internal test set, and 0.78 in the external test set. Calibration improved predictive accuracy while maintaining high specificity (> 0.75). DCA confirmed this model's superior net clinical benefit.
conclusionsA machine learning model combining radiological and clinical features provides high accuracy in predicting the technical success of antegrade access in femoropopliteal interventions. The model's high specificity and clinical utility may support preoperative decision-making.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.