Shape Modeling for On-line Adaptive Radiotherapy in Cervical Cancer

Khan, Aafreen; Jensen, Lindsay G.; Sun, Shibin; Yashar, C.M.; Mundt, A.J.; Zhang, F.; Jiang, S.; Mell, Loren K.

doi:10.1016/j.ijrobp.2011.06.099

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“…The CTV of cervix patients has very large deformations governed to a high degree by the variation in bladder filling normally requiring large planning target margins thus causing extensive irradiation of OAR (Jadon et al 2014). Khan et al (2011) used a SSM of cervix cancer patients for studying plan selection in online adaptive radiotherapy but did not report about treatment simulations for treatment planning. It is of high interest to study the dosimetric effects of the organ motion to provide better information regarding various trade-offs in dose planning.…”

Section: Introductionmentioning

confidence: 99%

Dose coverage calculation using a statistical shape model—applied to cervical cancer radiotherapy

et al. 2017

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A comprehensive methodology for treatment simulation and evaluation of dose coverage probabilities is presented where a population based statistical shape model (SSM) provide samples of fraction specific patient geometry deformations. The learning data consists of vector fields from deformable image registration of repeated imaging giving intra-patient deformations which are mapped to an average patient serving as a common frame of reference. The SSM is created by extracting the most dominating eigenmodes through principal component analysis of the deformations from all patients. The sampling of a deformation is thus reduced to sampling weights for enough of the most dominating eigenmodes that describe the deformations. For the cervical cancer patient datasets in this work, we found seven eigenmodes to be sufficient to capture 90% of the variance in the deformations of the, and only three eigenmodes for stability in the simulated dose coverage probabilities. The normality assumption of the eigenmode weights was tested and found relevant for the 20 most dominating eigenmodes except for the first. Individualization of the SSM is demonstrated to be improved using two deformation samples from a new patient. The probabilistic evaluation provided additional information about the trade-offs compared to the conventional single dataset treatment planning.

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Section: Introductionmentioning

confidence: 99%