A radiogenomics application for prognostic profiling of endometrial cancer
Høivik, Erling Andre; Hodneland, Erlend; Dybvik, Julie Andrea; Wagner-Larsen, Kari Strøno; Fasmer, Kristine Eldevik; Berg, Hege Fredriksen; Halle, Mari Kyllesø; Haldorsen, Ingfrid S.; Krakstad, Camilla
Journal article, Peer reviewed
Published version
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https://hdl.handle.net/11250/2834480Utgivelsesdato
2021Metadata
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- Department of Clinical Science [2396]
- Registrations from Cristin [10412]
Sammendrag
Prognostication is critical for accurate diagnosis and tailored treatment in endometrial cancer (EC). We employed radiogenomics to integrate preoperative magnetic resonance imaging (MRI, n = 487 patients) with histologic-, transcriptomic- and molecular biomarkers (n = 550 patients) aiming to identify aggressive tumor features in a study including 866 EC patients. Whole-volume tumor radiomic profiling from manually (radiologists) segmented tumors (n = 138 patients) yielded clusters identifying patients with high-risk histological features and poor survival. Radiomic profiling by a fully automated machine learning (ML)-based tumor segmentation algorithm (n = 336 patients) reproduced the same radiomic prognostic groups. From these radiomic risk-groups, an 11-gene high-risk signature was defined, and its prognostic role was reproduced in orthologous validation cohorts (n = 554 patients) and aligned with The Cancer Genome Atlas (TCGA) molecular class with poor survival (copy-number-high/p53-altered). We conclude that MRI-based integrated radiogenomics profiling provides refined tumor characterization that may aid in prognostication and guide future treatment strategies in EC.