Publications by authors named "A G Rockall"
Objectives: In women with high-grade serous ovarian cancer (HGSOC), a CT-based radiomic prognostic vector (RPV) predicted stromal phenotype and survival after primary surgery. The study's purpose was to fully externally validate RPV and its biological correlate.
Materials And Methods: In this retrospective study, ovarian masses on CT scans of HGSOC patients, who underwent primary cytoreductive surgery in an ESGO-certified Center between 2002 and 2017, were segmented for external RPV score calculation and then correlated with overall survival (OS) and progression-free survival (PFS).
Article Synopsis
- Radiology is leading the way in using artificial intelligence (AI) in medicine, which is important for improving how patients are cared for.
- To help with this, three workshops are being organized for experts and industry leaders to share ideas and tackle challenges.
- The first workshop focused on using everyday data to evaluate AI, discussing ethics, data management, sharing methods, and the importance of considering patients' perspectives.
Robustness of deep learning segmentation models is crucial for their safe incorporation into clinical practice. However, these models can falter when faced with distributional changes. This challenge is evident in magnetic resonance imaging (MRI) scans due to the diverse acquisition protocols across various domains, leading to differences in image characteristics such as textural appearances.
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