Statistical shape model to generate a planning library for cervical adaptive radiotherapy

Abstract : External beam radiotherapy is extensively used to treat cervical carcinomas. A single planning CT scan enables the calculation of the dose distribution. The treatment is delivered over 5 weeks. Large per-treatment anatomical variations may hamper the dose delivery, with the potential of an organs at risk (OAR) overdose and a tumor underdose. To anticipate these deformations, a recent approach proposed three planning CTs with variable bladder volumes, which had the limitation of not covering all per-treatment anatomical variations. An original patient-specific population-based library has been proposed. It consisted of generating two representative anatomies, in addition to the standard planning CT anatomy. First, the cervix and bladder meshes of a population of 20 patients (314 images) were registered to an anatomical template, using a deformable mesh registration. An iterative point-matching algorithm was developed based on local shape context (histogram of polar or cylindrical coordinates and geodesic distance to the base) and on a topology constraint filter. Second, a standard principal component analysis (PCA) model of the cervix and bladder was generated to extract the dominant deformation modes. Finally, specific deformations were obtained using posterior PCA models, with a constraint representing the top of the uterus deformation. For a new patient, the cervix-uterus and bladder were registered to the template, and the patient’s modeled planning library was built according to the model deformations. This method was applied following a leave-one-patient-out cross-validation. The performances of the modeled library were compared to those of the three-CT-based library and showing an improvement in both target coverage and OAR sparing.
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Bastien Rigaud, Antoine Simon, Maxime Gobeli, Julie Leseur, Loig Duverge, et al.. Statistical shape model to generate a planning library for cervical adaptive radiotherapy. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2019, 38 (2), pp.406-416. ⟨10.1109/TMI.2018.2865547⟩. ⟨hal-01988100⟩

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