ArticleScientific reports2021
Automated segmentation of endometrial cancer on MR images using deep learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.European radiology · 2025Pooled it
- Robotic assisted versus laparoscopic surgery for deep endometriosis: a meta-analysis of current evidence.Journal of robotic surgery · 2024Pooled it
- Artificial intelligence for evaluation of magnetic resonance imaging-detected extramural vascular invasion in rectal cancer.PLOS digital health · 2026Article
- Toward reproducible metabolic tumor volume quantification in endometrial cancer: optimizing [¹⁸F]FDG PET/CT tumor segmentation methods.EJNMMI research · 2026Article
- Preoperative risk assessment of endometrial cancer using histogram analysis of weighted and quantitative MRI images.Abdominal radiology (New York) · 2026Article
- Development of an AI-based magnetic resonance imaging reading support program (AMP) for deep endometriosis diagnosis.Scientific reports · 2025Article
- A Multi-Modal Pelvic MRI Dataset for Deep Learning-Based Pelvic Organ Segmentation in Endometriosis.Scientific data · 2025Article
- Artificial Intelligence in Obstetric and Gynecological MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.Chinese medical journal · 2025Review
- Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma.Abdominal radiology (New York) · 2025Article
- Preoperative prediction of aggressive endometrial cancer using multiparametric MRI-based deep transfer learning models.Frontiers in oncology · 2025Article
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers.Scientific reports · 2024Article
- Machine Learning Meets Cancer.Cancers · 2024Review
- A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles.PloS one · 2024Article
- An Instance Segmentation Model Based on Deep Learning for Intelligent Diagnosis of Uterine Myomas in MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Advances in Radiomics Research for Endometrial Cancer: A Comprehensive Review.Journal of Cancer · 2023Review
- Diagnostic value of the apparent diffusion coefficient in differentiating malignant from benign endometrial lesions.Frontiers in oncology · 2023Article
- Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study.Journal of personalized medicine · 2022Article
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12 authors.
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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, [Formula: see text]). 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, [Formula: see text], [Formula: see text], and [Formula: see text]). 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.
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