ArticleBMC medical imaging2022
Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 19 citations in OpenAlex.
- Internal feasibility evaluation of SAM-Med3D for cervical cancer clinical target volume segmentation on planning computed tomography.Radiological physics and technology · 2026Article
- 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
- Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.International journal of radiation oncology, biology, physics · 2026Article
- Discerning clinical target volume of endometrial cancer via a lightweight multi-phase delineation framework.Radiation oncology (London, England) · 2026Article
- 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
- 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 segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.Cancer medicine · 2024Article
- Cervical cancer segmentation based on medical images: a literature review.Quantitative imaging in medicine and surgery · 2024Review
- A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.Journal of applied clinical medical physics · 2024Article
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.Advances in radiation oncology · 2024Review
- Semi-supervised auto-segmentation method for pelvic organ-at-risk in magnetic resonance images based on deep-learning.Journal of applied clinical medical physics · 2024Article
- Multicenter Study of Pelvic Nodal Autosegmentation Algorithm of Siemens Healthineers: Comparison of Male Versus Female Pelvis.Advances in radiation oncology · 2024Article
- A dual deep neural network for auto-delineation in cervical cancer radiotherapy with clinical validation.Radiation oncology (London, England) · 2022Article
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11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAccurate contouring of the clinical target volume (CTV) is a key element of radiotherapy in cervical cancer. We validated a novel deep learning (DL)-based auto-segmentation algorithm for CTVs in cervical cancer called the three-channel adaptive auto-segmentation network (TCAS).
methodsA total of 107 cases were collected and contoured by senior radiation oncologists (ROs). Each case consisted of the following: (1) contrast-enhanced CT scan for positioning, (2) the related CTV, (3) multiple plain CT scans during treatment and (4) the related CTV. After registration between (1) and (3) for the same patient, the aligned image and CTV were generated. Method 1 is rigid registration, method 2 is deformable registration, and the aligned CTV is seen as the result. Method 3 is rigid registration and TCAS, method 4 is deformable registration and TCAS, and the result is generated by a DL-based method.
resultsFrom the 107 cases, 15 pairs were selected as the test set. The dice similarity coefficient (DSC) of method 1 was 0.8155 ± 0.0368; the DSC of method 2 was 0.8277 ± 0.0315; the DSCs of method 3 and 4 were 0.8914 ± 0.0294 and 0.8921 ± 0.0231, respectively. The mean surface distance and Hausdorff distance of methods 3 and 4 were markedly better than those of method 1 and 2.
conclusionsThe TCAS achieved comparable accuracy to the manual delineation performed by senior ROs and was significantly better than direct registration.
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