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Related Experiment Video

Updated: Jul 8, 2025

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
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ASTRA: Atomic Surface Transformations for Radiotherapy Quality Assurance.

Amith Kamath, Robert Poel, Jonas Willmann

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    A new deep learning method, atomic surface transformations for radiotherapy quality assurance (ASTRA), helps radiation oncologists improve glioblastoma treatment planning. ASTRA identifies how segmentation errors impact radiation dose, leading to more accurate and efficient radiotherapy.

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    Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

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    Area of Science:

    • Medical Physics
    • Artificial Intelligence in Medicine
    • Radiation Oncology

    Background:

    • Glioblastoma radiotherapy planning faces challenges from human errors and variability in tumor segmentation.
    • Current segmentation review processes are time-intensive and lack dose-distribution feedback, hindering efficiency and optimal outcomes.
    • Sub-optimal segmentations directly lead to inaccurate radiation dose distributions, compromising treatment efficacy.

    Purpose of the Study:

    • To introduce an automated deep learning method, atomic surface transformations for radiotherapy quality assurance (ASTRA), for predicting the impact of segmentation variations on radiation dose.
    • To provide clinicians with a dose-aware sensitivity map for reviewing and correcting segmentations in radiotherapy planning.
    • To enhance the quality assurance workflow in radiation therapy planning for glioblastoma.

    Main Methods:

    • Development of a deep learning-based method (ASTRA) to predict dose changes resulting from local segmentation variations.
    • Application of ASTRA on a dataset of 100 glioblastoma patients.
    • Visualization of organs-at-risk (OARs) susceptibility to dose alterations based on segmentation changes.

    Main Results:

    • ASTRA effectively predicts the potential impact of segmentation variations on radiotherapy dose predictions.
    • The method enables visualization of OARs most susceptible to dose changes, offering a dose-informed review mechanism.
    • Demonstrated ability to assess and visualize segmentation variation impacts on dose distributions for glioblastoma patients.

    Conclusions:

    • ASTRA shows significant potential as an automated tool for radiotherapy quality assurance.
    • The method can improve the efficiency and accuracy of segmentation review and correction in radiation therapy planning.
    • ASTRA facilitates a dose-informed approach, optimizing clinical outcomes for glioblastoma patients.