ArticleFrontiers in oncology2022
Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- Nasopharyngeal cancer adaptive radiotherapy with CBCT-derived synthetic CT: deep learning-based auto-segmentation precision and dose calculation consistency on a C-Arm linac.Radiation oncology (London, England) · 2025Article
- Investigating the necessity of adaptive radiotherapy in tomotherapy of head and neck cancer patients.Radiation oncology (London, England) · 2025Article
- Current status and upcoming developments for online adaptive proton therapy enabling a closed feedback loop for near real-time adaptation.Frontiers in oncology · 2025Review
- Adaptive assessment based on fractional CBCT images for cervical cancer.Journal of applied clinical medical physics · 2024Article
- Automated tracking of morphologic changes in weekly magnetic resonance imaging during head and neck radiotherapy.Journal of applied clinical medical physics · 2023Article
- A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS).Scientific reports · 2023Article
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Authors and funding
6 authors.
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Abstract
Purpose: This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients. Methods: We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients' data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameter (DP) trends against user-specified thresholds to proactively trigger adaptive re-planning up to four fractions ahead. The DPs evaluated for ART were based on treatment planning dose constraints. Warning (V Results: RTapp was able to address the needs of treatment adaptation. Specifically, we identified 18/27 studies (67%) for violating PTV coverage or parotid D Conclusion: Integrated in an ART clinical workflow, RTapp aids in predicting whether specific treatment would require adaptation up to four fractions ahead of time.
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