dc.contributor.author | Høivik, Erling Andre | |
dc.contributor.author | Hodneland, Erlend | |
dc.contributor.author | Dybvik, Julie Andrea | |
dc.contributor.author | Wagner-Larsen, Kari Strøno | |
dc.contributor.author | Fasmer, Kristine Eldevik | |
dc.contributor.author | Berg, Hege Fredriksen | |
dc.contributor.author | Halle, Mari Kyllesø | |
dc.contributor.author | Haldorsen, Ingfrid S. | |
dc.contributor.author | Krakstad, Camilla | |
dc.date.accessioned | 2021-12-15T13:17:40Z | |
dc.date.available | 2021-12-15T13:17:40Z | |
dc.date.created | 2021-12-10T20:55:01Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 2399-3642 | |
dc.identifier.uri | https://hdl.handle.net/11250/2834480 | |
dc.description.abstract | 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Nature | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | A radiogenomics application for prognostic profiling of endometrial cancer | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | Copyright 2021 The Author(s) | en_US |
dc.source.articlenumber | 1363 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | https://doi.org/10.1038/s42003-021-02894-5 | |
dc.identifier.cristin | 1967289 | |
dc.source.journal | Communications Biology | en_US |
dc.identifier.citation | Communications Biology. 2021, 4, 1363. | en_US |
dc.source.volume | 4 | en_US |