Publications by authors named "Natasha Allgood"
Article Synopsis
- - The article discusses an automated system called AWARE that tracks anatomical changes in head and neck cancer patients during radiotherapy by analyzing weekly MRI scans to improve treatment outcomes.
- - AWARE processes MRI data to monitor changes in the size of relevant structures, such as tumor volumes and parotid glands, and incorporates expert manual input for accuracy.
- - Results from 91 patients showed significant shrinking of tumor volumes and parotid glands over time, with early changes potentially predicting treatment responses.
Background And Purpose: Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased.
Materials And Methods: Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO.
During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT).
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