ArticleJournal of applied clinical medical physics2022
Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed, 84 citations in OpenAlex.
- Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.Radiology. Artificial intelligence · 2024Trial
- Real-world clinical impact of implementing and updating a deep learning-based automatic contouring system in rectal cancer radiotherapy.Journal of applied clinical medical physics · 2026Article
- Internal feasibility evaluation of SAM-Med3D for cervical cancer clinical target volume segmentation on planning computed tomography.Radiological physics and technology · 2026Article
- Article
- A prospective development and evaluation of a 2D convolutional neural network-based auto-segmentation model for cervical cancer radiotherapy.Journal of the Egyptian National Cancer Institute · 2026Article
- A lightweight deep learning model for automated segmentation of Gynecologic organs and cervical Tumors on T2-weighted magnetic resonance imaging.Physics and imaging in radiation oncology · 2026Article
- HAR-Net: Automatic delineation of clinical target volumes for cervical cancer guided by hierarchical anatomical refinement.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Deep learning-based cervical cancer T-staging using MRI: multi-structure segmentation and classification.BMC medical imaging · 2026Article
- Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Article
- CT-based deep learning auto-segmentation of high-risk clinical target volume in CT-guided cervical cancer brachytherapy: a single-center pragmatic study.Frontiers in oncology · 2026Article
- Performance enhancement method by using the probabilistic estimation for kidney tumor segmentation.Frontiers in oncology · 2026Article
- Clinical validation of AI-assisted contouring in prostate radiation therapy treatment planning: Highlighting automation bias and the need for standardized quality assurance.Journal of applied clinical medical physics · 2026Article
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques.Frontiers in artificial intelligence · 2026Review
- Prior knowledge of anatomical relationships supports automatic delineation of clinical target volume for cervical cancer.Scientific reports · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis.Current oncology (Toronto, Ont.) · 2025Article
- A fully automated machine-learning-based workflow for radiation treatment planning in prostate cancer.Clinical and translational radiation oncology · 2025Article
- Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.BMC musculoskeletal disorders · 2025Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesBecause radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation targets. We evaluated a deep learning (DL)-based auto-segmentation algorithm for automatic contouring of clinical target volumes (CTVs) in cervical cancers.
methodsComputed tomography (CT) datasets from 535 cervical cancers treated with definitive or postoperative radiotherapy were collected. A DL tool based on VB-Net was developed to delineate CTVs of the pelvic lymph drainage area (dCTV1) and parametrial area (dCTV2) in the definitive radiotherapy group. The training/validation/test number is 157/20/23. CTV of the pelvic lymph drainage area (pCTV1) was delineated in the postoperative radiotherapy group. The training/validation/test number is 272/30/33. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) were used to evaluate the contouring accuracy. Contouring times were recorded for efficiency comparison.
resultsThe mean DSC, MSD, and HD values for our DL-based tool were 0.88/1.32 mm/21.60 mm for dCTV1, 0.70/2.42 mm/22.44 mm for dCTV2, and 0.86/1.15 mm/20.78 mm for pCTV1. Only minor modifications were needed for 63.5% of auto-segmentations to meet the clinical requirements. The contouring accuracy of the DL-based tool was comparable to that of senior radiation oncologists and was superior to that of junior/intermediate radiation oncologists. Additionally, DL assistance improved the performance of junior radiation oncologists for dCTV2 and pCTV1 contouring (mean DSC increases: 0.20 for dCTV2, 0.03 for pCTV1; mean contouring time decrease: 9.8 min for dCTV2, 28.9 min for pCTV1).
conclusionsDL-based auto-segmentation improves CTV contouring accuracy, reduces contouring time, and improves clinical efficiency for treating cervical cancer.
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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.