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Statistical Shape Model to Generate a Planning Library for Cervical Adaptive Radiotherapy.

Bastien Rigaud, Antoine Simon, Maxime Gobeli

    IEEE Transactions on Medical Imaging
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    This study introduces a patient-specific library to improve cervical cancer radiotherapy by accounting for anatomical changes. The new method enhances tumor targeting and organ-at-risk sparing during treatment.

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    Area of Science:

    • Medical Physics
    • Radiation Oncology
    • Medical Imaging

    Background:

    • External beam radiotherapy is standard for cervical cancer, relying on CT scans for dose planning.
    • Anatomical variations during treatment can lead to underdosing tumors and overdosing organs-at-risk (OARs).
    • Previous methods using three planning CTs did not fully capture all anatomical variations.

    Purpose of the Study:

    • To develop and validate a patient-specific, population-based library for anticipating anatomical variations in cervical cancer radiotherapy.
    • To improve the accuracy of dose delivery and reduce OAR toxicity.

    Main Methods:

    • Deformable mesh registration and an iterative point-matching algorithm were used to create cervix and bladder meshes from 314 images of 20 patients.
    • A principal component analysis (PCA) model was generated to capture dominant deformation modes.
    • Patient-specific libraries were built using PCA models and applied via leave-one-patient-out cross-validation.

    Main Results:

    • The patient-specific library successfully generated representative anatomies beyond the standard planning CT.
    • Comparison with a three-CT library showed significant improvements in target coverage.
    • The new method also demonstrated enhanced sparing of organs-at-risk.

    Conclusions:

    • A patient-specific population-based library is a superior approach for managing anatomical variations in cervical cancer radiotherapy.
    • This method offers better tumor coverage and OAR protection compared to existing techniques.
    • The developed library can enhance treatment planning and delivery precision.