ArticlePhysics and imaging in radiation oncology2020
Validation of automated magnetic resonance image segmentation for radiation therapy planning in prostate cancer.
Article in Physics and imaging in radiation oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 20 citations in OpenAlex.
- Assessing inter-observer variability in prostate and GTV segmentation on mpMRI: A comparison between radiation oncologists and AI-based method.Journal of applied clinical medical physics · 2026Article
- Step-by-step assessment of MRI-only workflow in pelvic radiotherapy: feasibility and practical implementation.La Radiologia medica · 2026Article
- The dosimetric impacts of ct-based deep learning autocontouring algorithm for prostate cancer radiotherapy planning dosimetric accuracy of DirectORGANS.BMC urology · 2025Article
- Multi-spatial-attention U-Net: a novel framework for automated gallbladder segmentation on CT images.BMC medical imaging · 2025Article
- Multi-scale channel attention U-Net: a novel framework for automated gallbladder segmentation in medical imaging.Frontiers in oncology · 2025Article
- Estimation and Analysis of Slice Propagation Uncertainty in 3D Anatomy Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Optimising the MR-Linac as a standard treatment modality.Journal of medical radiation sciences · 2023Article
- Automatic segmentation of prostate and extracapsular structures in MRI to predict needle deflection in percutaneous prostate intervention.International journal of computer assisted radiology and surgery · 2023Article
- Review
- Anatomical evaluation of deep-learning synthetic computed tomography images generated from male pelvis cone-beam computed tomography.Physics and imaging in radiation oncology · 2023Article
- Optimisation and validation of an integrated magnetic resonance imaging-only radiotherapy planning solution.Physics and imaging in radiation oncology · 2021Article
- Clinical experience and cost evaluation of magnetic resonance imaging -only workflow in radiation therapy planning of prostate cancer.Physics and imaging in radiation oncology · 2021Article
- A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study.Diagnostics (Basel, Switzerland) · 2020Article
- Clinical experience and workflow challenges with magnetic resonance-only radiation therapy simulation and planning for prostate cancer.Physics and imaging in radiation oncology · 2020Article
- Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology.Physics and imaging in radiation oncology · 2020Article
- Clinical Validation and Treatment Plan Evaluation Based on Autodelineation of the Clinical Target Volume for Prostate Cancer Radiotherapy.Technology in cancer research & treatmentArticle
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeMagnetic resonance imaging (MRI) is increasingly used in radiation therapy planning of prostate cancer (PC) to reduce target volume delineation uncertainty. This study aimed to assess and validate the performance of a fully automated segmentation tool (AST) in MRI based radiation therapy planning of PC. MATERIAL AND
methodsPelvic structures of 65 PC patients delineated in an MRI-only workflow according to established guidelines were included in the analysis. Automatic vs manual segmentation by an experienced oncologist was compared with geometrical parameters, such as the dice similarity coefficient (DSC). Fifteen patients had a second MRI within 15 days to assess repeatability of the AST for prostate and seminal vesicles. Furthermore, we investigated whether hormonal therapy or body mass index (BMI) affected the AST results.
resultsThe AST showed high agreement with manual segmentation expressed as DSC (mean, SD) for delineating prostate (0.84, 0.04), bladder (0.92, 0.04) and rectum (0.86, 0.04). For seminal vesicles (0.56, 0.17) and penile bulb (0.69, 0.12) the respective agreement was moderate. Performance of AST was not influenced by neoadjuvant hormonal therapy, although those on treatment had significantly smaller prostates than the hormone-naïve patients (p < 0.0001). In repeat assessment, consistency of prostate delineation resulted in mean DSC of 0.89, (SD 0.03) between the paired MRI scans for AST, while mean DSC of manual delineation was 0.82, (SD 0.05).
conclusionFully automated MRI segmentation tool showed good agreement and repeatability compared with manual segmentation and was found clinically robust in patients with PC. However, manual review and adjustment of some structures in individual cases remain important in clinical use.
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