Automated segmentation of endometrial cancer on MR images using deep learning

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 relev...

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Autores principales:	Erlend Hodneland, Julie A. Dybvik, Kari S. Wagner-Larsen, Veronika Šoltészová, Antonella Z. Munthe-Kaas, Kristine E. Fasmer, Camilla Krakstad, Arvid Lundervold, Alexander S. Lundervold, Øyvind Salvesen, Bradley J. Erickson, Ingfrid Haldorsen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c6abe276d154ef9bbcfb07bbc143dfb
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spelling	oai:doaj.org-article:0c6abe276d154ef9bbcfb07bbc143dfb2021-12-02T15:12:55ZAutomated segmentation of endometrial cancer on MR images using deep learning10.1038/s41598-020-80068-92045-2322https://doaj.org/article/0c6abe276d154ef9bbcfb07bbc143dfb2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80068-9https://doaj.org/toc/2045-2322Abstract 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, $$p = 0.06$$ 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 = 0.08 , $$p=0.60$$ p = 0.60 , and $$p=0.05$$ 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.Erlend HodnelandJulie A. DybvikKari S. Wagner-LarsenVeronika ŠoltészováAntonella Z. Munthe-KaasKristine E. FasmerCamilla KrakstadArvid LundervoldAlexander S. LundervoldØyvind SalvesenBradley J. EricksonIngfrid HaldorsenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Erlend Hodneland Julie A. Dybvik Kari S. Wagner-Larsen Veronika Šoltészová Antonella Z. Munthe-Kaas Kristine E. Fasmer Camilla Krakstad Arvid Lundervold Alexander S. Lundervold Øyvind Salvesen Bradley J. Erickson Ingfrid Haldorsen Automated segmentation of endometrial cancer on MR images using deep learning
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, $$p = 0.06$$ 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 = 0.08 , $$p=0.60$$ p = 0.60 , and $$p=0.05$$ 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.
format	article
author	Erlend Hodneland Julie A. Dybvik Kari S. Wagner-Larsen Veronika Šoltészová Antonella Z. Munthe-Kaas Kristine E. Fasmer Camilla Krakstad Arvid Lundervold Alexander S. Lundervold Øyvind Salvesen Bradley J. Erickson Ingfrid Haldorsen
author_facet	Erlend Hodneland Julie A. Dybvik Kari S. Wagner-Larsen Veronika Šoltészová Antonella Z. Munthe-Kaas Kristine E. Fasmer Camilla Krakstad Arvid Lundervold Alexander S. Lundervold Øyvind Salvesen Bradley J. Erickson Ingfrid Haldorsen
author_sort	Erlend Hodneland
title	Automated segmentation of endometrial cancer on MR images using deep learning
title_short	Automated segmentation of endometrial cancer on MR images using deep learning
title_full	Automated segmentation of endometrial cancer on MR images using deep learning
title_fullStr	Automated segmentation of endometrial cancer on MR images using deep learning
title_full_unstemmed	Automated segmentation of endometrial cancer on MR images using deep learning
title_sort	automated segmentation of endometrial cancer on mr images using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0c6abe276d154ef9bbcfb07bbc143dfb
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Automated segmentation of endometrial cancer on MR images using deep learning

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