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Rank #136 · 7 unique linked submissions.
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Accurate delineation of the high-risk clinical target volume (HR-CTV) and organs at risk (OARs) is critical for cervical cancer brachytherapy. However, treatment planning is time-consuming, and prolonged waiting can lead to organ displacement and patient disc...
To develop and validate an MRI–based fusion model (Rad–SRad–SwinT) integrating conventional radiomics (Rad), subregional radiomics (SRad), and Transformer–derived deep learning features (Swin Transformer, SwinT) to predict post–radiotherapy radiation–induced...
Conventional 3D CT-CBCT registration is typically performed independently at a single respiratory phase, limiting the ability to characterize 4D respiratory motion and temporal continuity between planning and treatment. This work develops a phase-aware, modal...
Patient pose variations during radiotherapy can degrade setup accuracy and compromise treatment safety. Existing monitoring approaches often suffer from limited real-time capability and complex system configurations. To address these limitations, this study p...
Respiratory motion increases setup uncertainty in thoracic and abdominal tumor radiotherapy and elevates radiation exposure to organs at risk (OARs), such as the heart and lungs. Deep inspiration breath-hold (DIBH) can effectively reduce OAR dose; however, it...
Radiotherapy is one of the primary treatment modalities for cervical cancer, and accurate ROI delineation is crucial. Previous studies have predominantly utilized feature reproducibility to construct patient prognosis models, with limited exploration of voxel...
Biological in vivo three-dimensional dose distribution verification, introduced as a new concept in radiotherapy, was proposed to enable evaluation of the impact of respiratory motion on dose-delivery accuracy and to allow the inherent limitations of conventi...