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dc.contributor.authorHodneland, Erlend
dc.contributor.authorDybvik, Julie Andrea
dc.contributor.authorWagner-Larsen, Kari Strøno
dc.contributor.authorSolteszova, Veronika
dc.contributor.authorZanna, Antonella
dc.contributor.authorFasmer, Kristine Eldevik
dc.contributor.authorKrakstad, Camilla
dc.contributor.authorLundervold, Arvid
dc.contributor.authorLundervold, Alexander Selvikvåg
dc.contributor.authorSalvesen, Øyvind
dc.contributor.authorErickson, Bradley J.
dc.contributor.authorHaldorsen, Ingfrid S
dc.date.accessioned2021-08-03T08:47:18Z
dc.date.available2021-08-03T08:47:18Z
dc.date.created2021-01-22T13:47:28Z
dc.date.issued2021
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/11250/2765954
dc.description.abstractPreoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, p=0.06). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, p=0.08, p=0.60, and p=0.05). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.en_US
dc.language.isoengen_US
dc.publisherNatureen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleAutomated segmentation of endometrial cancer on MR images using deep learningen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2021 The Authorsen_US
dc.source.articlenumber179en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1038/s41598-020-80068-9
dc.identifier.cristin1877198
dc.source.journalScientific Reportsen_US
dc.identifier.citationScientific Reports. 2021, 11, 179en_US
dc.source.volume11en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal