ArticleRadiation oncology (London, England)2020
Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers.
Article in Radiation oncology (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 55 citations in OpenAlex.
- Discerning clinical target volume of endometrial cancer via a lightweight multi-phase delineation framework.Radiation oncology (London, England) · 2026Article
- Automatic pelvic bone segmentation for radiotherapy using diagnostic imaging workstation.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Article
- Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective.Frontiers in medicine · 2026Article
- Imaging-based transformer model predicts early therapy response in advanced nasopharyngeal carcinoma: a dual-center study.Insights into imaging · 2025Article
- [Endometrial cancer lesion region segmentation based on large kernel convolution and combined attention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Large-scale convolutional neural network for clinical target and multi-organ segmentation in gynecologic brachytherapy via multi-stage learning.Medical physics · 2025Article
- Prior knowledge of anatomical relationships supports automatic delineation of clinical target volume for cervical cancer.Scientific reports · 2025Article
- Article
- Clinical evaluation of atlas-based auto-segmentation for contouring pelvic CTVs in the treatment of anal cancer with FDG-PET-positive lymph node involvement.Frontiers in oncology · 2025Article
- Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation.Journal of applied clinical medical physics · 2025Article
- Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.Scientific reports · 2024Article
- Cervical cancer segmentation based on medical images: a literature review.Quantitative imaging in medicine and surgery · 2024Review
- Multicenter Study of Pelvic Nodal Autosegmentation Algorithm of Siemens Healthineers: Comparison of Male Versus Female Pelvis.Advances in radiation oncology · 2024Article
- Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study.Frontiers in oncology · 2024Article
- Article
- Evaluation of Atlas-based auto-segmentation of liver in MR images for Yttrium-90 selective internal radiation therapy.Journal of applied clinical medical physics · 2023Article
- Review
- A dual deep neural network for auto-delineation in cervical cancer radiotherapy with clinical validation.Radiation oncology (London, England) · 2022Article
- A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.Radiation oncology (London, England) · 2022Article
- Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSince intensity-modulated radiation therapy (IMRT) has become popular for the treatment of gynecologic cancers, the contouring process has become more critical. This study evaluated the feasibility of atlas-based auto-segmentation (ABAS) for contouring in patients with endometrial and cervical cancers.
methodsA total of 75 sets of planning CT images from 75 patients were collected. Contours for the pelvic nodal clinical target volume (CTV), femur, and bladder were carefully generated by two skilled radiation oncologists. Of 75 patients, 60 were randomly registered in three different atlas libraries for ABAS in groups of 20, 40, or 60. ABAS was conducted in 15 patients, followed by manual correction (ABAS
resultsFor ABAS-CTV, the best results were achieved with groups of 60 patients (DC, 0.79; HD, 19.7 mm) and the worst results with groups of 20 patients (DC, 0.75; p = 0.012; HD, 21.3 mm; p = 0.002). ABAS
conclusionsABAS could help physicians to delineate the CTV and organs-at-risk (e.g., femurs) in IMRT planning considering its consistency, efficacy, and accuracy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.