dc.contributor.author | Hodneland, Erlend | |
dc.contributor.author | Dybvik, Julie Andrea | |
dc.contributor.author | Wagner-Larsen, Kari Strøno | |
dc.contributor.author | Solteszova, Veronika | |
dc.contributor.author | Zanna, Antonella | |
dc.contributor.author | Fasmer, Kristine Eldevik | |
dc.contributor.author | Krakstad, Camilla | |
dc.contributor.author | Lundervold, Arvid | |
dc.contributor.author | Lundervold, Alexander Selvikvåg | |
dc.contributor.author | Salvesen, Øyvind | |
dc.contributor.author | Erickson, Bradley J. | |
dc.contributor.author | Haldorsen, Ingfrid S | |
dc.date.accessioned | 2021-03-10T14:13:53Z | |
dc.date.available | 2021-03-10T14:13:53Z | |
dc.date.created | 2021-01-22T13:47:28Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | https://hdl.handle.net/11250/2732681 | |
dc.description.abstract | Preoperative 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.iso | eng | en_US |
dc.publisher | Nature Research | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Automated segmentation of endometrial cancer on MR images using deep learning | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.volume | 11 | en_US |
dc.source.journal | Scientific Reports | en_US |
dc.identifier.doi | 10.1038/s41598-020-80068-9 | |
dc.identifier.cristin | 1877198 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |