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 Dishwasher Door Panel 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 ORG STEEL CUT OATS 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.