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.

Feasibility of cone beam CT-guided library of plans strategy in pre-operative gastric cancer radiotherapy

“…Hence, to make full use of a LoP during the entire course of treatment, a patient-specific model-based LoP would be beneficial. A population analysis was performed in several studies to identify primary deformations and thereby predict the most likely anatomical deformations per patient [21,22]. From this, a LoP can be created, as has been done for cervical cancer [22].…”

“…A population analysis was performed in several studies to identify primary deformations and thereby predict the most likely anatomical deformations per patient [21,22]. From this, a LoP can be created, as has been done for cervical cancer [22]. Thus, to further improve gastric ART, the potential of a model-based LoP for this patient group will be investigated.…”

Implementación y uso clínico de la radioterapia adaptativa. Informe del grupo de trabajo de radioterapia adaptativa de la Sociedad Española de Física Médica (SEFM)

“…149 Esta se genera habitualmente a partir de varios TC de planificación (2 o 3) con diferente volumen de llenado de la vejiga, o utilizando herramientas de deformación y datos poblacionales. 150 Para el guiado y elección del plan diario se usa mayoritariamente el CBCT pretratamiento. 71 Aunque no existe evidencia clínica sobre la superioridad del guiado con imagen RM frente a CBCT, con la mayor disponibilidad de la imagen RM incluso en la unidad de tratamiento, es esperable que la calidad de imagen que esta proporciona facilite la elección del plan del día, y sobre todo posibilite la replanificación online.…”

Deformable image registration for radiation therapy: principle, methods, applications and evaluation

“…In cervical cancer, a DIR-based model has been generated to simulate different treatment scenarios and assess the dose coverage probability of the CTV and OAR [24]. A similar DIRbased model was used to generate a patient-specific planning library that predicts systematic deformations of the uterus [25]. A simpler use of surface-based DIR has been proposed to estimate intermediate positions of the uterus between full-and empty-bladder anatomies, allowing a planning library to be generated [26].…”