ArticleScientific reports2025
A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Deep learning auto-contouring of target and organs-at-risk on post-catheter implant CT images for prostate HDR brachytherapy.Journal of applied clinical medical physics · 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
- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- Radiomics and artificial intelligence in precision radiotherapy for cervical cancer: a narrative review.Frontiers in oncology · 2026Review
- Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer.Oncology reviews · 2026Review
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Authors and funding
9 authors.
Funding
Abstract
This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs) in cervical cancer brachytherapy. Using 694 CT scans from 179 cervical cancer patients, the performance of five models (AM-UNet, UNet, DeepLab V3, UNETR and nnU-Net) was compared. The models were assessed using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and dose-volume index (DVI). AM-UNet achieved mean DSCs of 0.862, 0.937, 0.823, and 0.725 for HRCTV, bladder, rectum, and sigmoid, respectively. Subjective evaluations showed 93.07% of AM-UNet predicted HRCTV were rated as clinically acceptable or needing minor adjustments, with no unacceptable cases. Dosimetric differences between AM-UNet-generated and manually delineated contours were within 1%, highlighting its potential for improving clinical workflows in brachytherapy.
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Registered trials
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.