CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study
Translational Stroke Research, ISSN: 1868-601X, Vol: 15, Issue: 4, Page: 784-794
2024
- 6Citations
- 4Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations6
- Citation Indexes6
- CrossRef6
- Captures4
- Readers4
- Mentions1
- News Mentions1
- 1
Most Recent News
Studies from Beijing Hospital in the Area of Arteriovenous Malformations Described (Ct Angiography Radiomics Combining Traditional Risk Factors To Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study)
2023 JUL 07 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Cardiovascular Daily -- Investigators discuss new findings in Cardiovascular Diseases and Conditions
Article Description
This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients with unruptured bAVMs from 2010 to 2020. All patients were grouped into the hemorrhage (n = 368) and non-hemorrhage (n = 218) groups. The bAVM nidus were segmented on CT angiography images using Slicer software, and radiomic features were extracted using Pyradiomics. The dataset included a training set and an independent testing set. The machine learning model was developed on the training set and validated on the testing set by merging numerous base estimators and a final estimator based on the stacking method. The area under the receiver operating characteristic (ROC) curve, precision, and the f1 score were evaluated to determine the performance of the model. A total of 1790 radiomics features and 8 traditional risk factors were contained in the original dataset, and 241 features remained for model training after L1 regularization filtering. The base estimator of the ensemble model was Logistic Regression, whereas the final estimator was Random Forest. In the training set, the area under the ROC curve of the model was 0.982 (0.967–0.996) and 0.893 (0.826–0.960) in the testing set. This study indicated that radiomics features are a valuable addition to traditional risk factors for predicting bAVM rupture. In the meantime, ensemble learning can effectively improve the performance of a prediction model.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85163069330&origin=inward; http://dx.doi.org/10.1007/s12975-023-01166-0; http://www.ncbi.nlm.nih.gov/pubmed/37311939; https://link.springer.com/10.1007/s12975-023-01166-0; https://dx.doi.org/10.1007/s12975-023-01166-0; https://link.springer.com/article/10.1007/s12975-023-01166-0
Springer Science and Business Media LLC
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