ArticleRadiation oncology (London, England)2025
Nasopharyngeal cancer adaptive radiotherapy with CBCT-derived synthetic CT: deep learning-based auto-segmentation precision and dose calculation consistency on a C-Arm linac.
Article in Radiation oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Improving deep learning organ segmentation accuracy in cone-beam CT-guided radiotherapy using a robust scatter suppression method.Physics and imaging in radiation oncology · 2026Article
- Synthetic CT-enabled weekly adaptive radiotherapy for nasopharyngeal carcinoma: Optimizing plan adaptation triggers through volumetric-dosimetric monitoring.Journal of applied clinical medical physics · 2026Article
- CycleGan-based kV-to-MV image translation for potential in vivo dosimetry application: concept proposal and cross-institutional validation.BMC medical imaging · 2025Article
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Authors and funding
7 authors.
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Abstract
backgroundTo evaluate the precision of automated segmentation facilitated by deep learning (DL) and dose calculation in adaptive radiotherapy (ART) for nasopharyngeal cancer (NPC), leveraging synthetic CT (sCT) images derived from cone-beam CT (CBCT) scans on a conventional C-arm linac. MATERIALS AND
methodsSixteen NPC patients undergoing a two-phase offline ART were analyzed retrospectively. The initial (pCT
resultsMost anatomical structures demonstrated high pCT-sCT agreement, with mean values of DSC > 0.85 and HD
conclusionThe proposed method exhibited minor variations in volumetric and dosimetric parameters compared to prior treatment data, suggesting potential efficiency improvements for ART in NPC through reduced human dependency.
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