Using radiomics to summarize 657 tumor features such as shape, size, intensity and texture collected from CT scans of 364 epithelial ovarian cancer patients, researchers tapped machine learning to derive a new diagnostic tool, the Radiomic Prognostic Vector (RPV).
The tool’s developers demonstrate that patients with high RPV have a significantly higher risk of failing standard treatment strategies, indicating a need for alternative therapeutic approaches for those patients. By serving as a cost-effective prognostic marker to guide personalized treatment, RPV “convincingly fulfills an unmet need” in epithelial ovarian cancer care.
HealthMyne helps clinicians easily extract radiomics in the standard clinical workflow and can alert clinicians when a signature like RPV is matched. To read the full article, click here. To learn more about HealthMyne, schedule your personalized demo here.