Automated segmentation of endometrial cancer on MR images using deep learning

Autor: 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
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-020-80068-9
Popis: 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.
Databáze: Directory of Open Access Journals
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