Publications by authors named "J-C Rueckert"
Article Synopsis
- Surgical resection is the primary treatment for patients with large or symptomatic brain metastases, but there's still a risk of local failure, prompting the development of a prediction tool to identify those at high risk.
- Data from the AURORA study included 253 patients for training and 99 for external testing, utilizing radiomic features from MRI scans to enhance prediction accuracy.
- The elastic net regression model combining radiomic and clinical features showed a significant improvement in predicting local failure, with lower risk groups experiencing only 9% failure at 24 months compared to 74% in high-risk groups, suggesting potential for improved patient follow-up and treatment.
While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists.
View Article and Find Full Text PDFBackground: The effectiveness of endovascular therapy in patients with stroke caused by basilar-artery occlusion has not been well studied.
Methods: We randomly assigned patients within 6 hours after the estimated time of onset of a stroke due to basilar-artery occlusion, in a 1:1 ratio, to receive endovascular therapy or standard medical care. The primary outcome was a favorable functional outcome, defined as a score of 0 to 3 on the modified Rankin scale (range, 0 to 6, with 0 indicating no disability, 3 indicating moderate disability, and 6 indicating death) at 90 days.