Lancet February 2026 End-to-end integrative segmentation and radiomics prognostic models for risk stratification of high-grade serous ovarian cancer: a retrospective multicohort study

Researchers have developed an innovative artificial intelligence pipelinedesigned to automate the analysis of medical imaging for patients with high-grade ovarian cancer. By utilizing automated segmentation and radiomics, the model extracts complex data from routine CT scans to predict patient outcomes more accurately than traditional clinical markers. This end-to-end framework successfully identifies high-risk individuals by linking digital imaging patterns to specific biological pathways and invasive phenotypes. Ultimately, this technology offers a non-invasive and scalable way to personalize treatment plans and improve the precision of prognostic assessments in oncology.