What does this research mean for the field?

End-to-end integrative segmentation and radiomics models can effectively stratify risk in patients with high-grade serous ovarian cancer. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to develop models for accurately predicting risks in high-grade serous ovarian cancer using imaging data.

February 27, 2026Open Access

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

Key Points

This research aims to develop models for accurately predicting risks in high-grade serous ovarian cancer using imaging data.
Retrospective analysis of multiple patient cohorts
Application of integrative segmentation techniques
Utilization of radiomics for feature extraction
Statistical evaluation of prognostic models
Developed models show improved risk stratification accuracy
Prognostic models identify subgroups with different outcomes
Findings support the use of AI in cancer prognosis

Abstract

The UK Medical Research Council, AstraZeneca, Cancer Research UK, National Institute for Health and Care Research, UK Research and Innovation, the National Natural Science Foundation of China, University of Hong Kong, Material Innovation Institute for Life Sciences and Energy, and the North West London Pathology Research & Education Board.

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

Key Points

Abstract

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