Statistical Modeling of CTV Motion and Deformation for IMRT of Early-Stage Rectal Cancer

Bondar, L. N.; Intven, Martijn; Burbach, J.P.M.; Budiarto, Eka; Kleijnen, J.J.E.; Philippens, M.E.P.; Asselen, Bram van; Seravalli, Enrica; Reerink, O.; Raaymakers, B W

doi:10.1016/j.ijrobp.2014.06.040

Cited by 17 publications

(20 citation statements)

References 16 publications

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“…New samples can then be generated by a weighted sum of these eigenmodes [29,30]. Following this, the generated shapes representing the dominant deformations can be combined to define an ITV [31] or to propose a model-generated PTV [32]. Recent studies used the ability of the model to generate random or systematic deformations prior to the treatment according to the training set observation.…”

Section: Standard Principal Component Analysismentioning

confidence: 99%

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

Rigaud

Simon

Gobeli

et al. 2019

IEEE Trans. Med. Imaging

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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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Section: Standard Principal Component Analysismentioning

confidence: 99%

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

Rigaud

Simon

Gobeli

et al. 2019

IEEE Trans. Med. Imaging

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show abstract

“…Several approaches have been developed in order to quantify and characterize the geometrical uncertainties produced by organ motion and deformation between fractions, including: serial imaging measurement of the organ during treatment course (Roeske et al, 1995;van Herk et al, 1995;Ten Haken et al, 1991), fiducial markers (Crook et al, 1995;Balter et al, 1995), margins of organs at risk (OAR) (Thor et al, 2010;Mageras et al, 1999;Stroom et al, 1999), rigid-body motion (Thor et al, 2013a;Killoran et al, 1997;Craig et al, 2001), parametrization of the organ structure (Hoogeman et al, 2002;Mageras et al, 1996;Pavel-Mititean et al, 2004), as well as biomechanical (Yan et al, 1999) and statistical models (Fontenla et al, 2001a,b;Söhn et al, 2012;Budiarto et al, 2011;Thörnqvist et al, 2013;Bondar et al, 2014;Rios et al, 2016). Recently, a method based on weighted scenarios of the fundamental directions of the patient's geometric variability was implemented (Söhn et al, 2005;Budiarto et al, 2011).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In our context, it corresponds to repeated observations of the organ at different fractions for a patient population. This methodology was also applied to model motion and deformation of the CTVs and bladder in rectal and prostate cancer radiotherapy, respectively (Bondar et al, 2014;Rios et al, 2016). In addition, Hu et al (2015) proposed a population-PCA model to describe prostate deformation in magnetic resonance (MR)-tumor-targeted biopsies using a longitudinal database of prostates obtained from MR images and biomechanical models.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…As a first step towards this goal, PDM method was proposed to parameterize 3D shapes by means of a set of boundary points (Cootes et al, 1995;Lorenz and Krahnstöver, 2000). However, this approach required thousands of points to properly describe shape surfaces and a one-to-one correspondence between these boundary points (Yan et al, 1999;Lorenz and Krahnstöver, 2000;Söhn et al, 2005;Budiarto et al, 2011;Bondar et al, 2014). SPHARM, the extension of 2D Fourier techniques to three dimensions, was proposed to obtain smooth, accurate and fine parametrization of 3D shapes with spherical topology with a fewer number of parameters (Ballard and Brown, 1982;Brechbühler et al, 1995;Székely et al, 1996;Kelemen et al, 1999).…”

Section: Spherical Harmonicsmentioning

confidence: 99%

“…However, we believe that using the barycenter as a reference point is not feasible for all pelvic organs to obtain population-based models. For this reason, Bondar et al (2014) used a setup based on bone anatomy for each patient, to calculate intra-patient motion-deformation vectors and develop a population model of CTVs for rectum cancer, as did (Budiarto et al, 2011). Nevertheless, this method may not be the most suitable setup for analyzing in the same framework all the different sizes and shapes that an organ can manifest in patients (Rios et al, 2016).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Population model of bladder motion and deformation based on dominant eigenmodes and mixed-effects models in prostate cancer radiotherapy

Ríos

Crevoisier

Ospina

et al. 2017

Medical Image Analysis

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In radiotherapy for prostate cancer irradiation of neighboring organs at risk may lead to undesirable side-effects. Given this setting, the bladder presents the largest inter-fraction shape variations hampering the computation of the actual delivered dose vs. planned dose. This paper proposes a population model, based on longitudinal data, able to estimate the probability of bladder presence during treatment, using only the planning computed tomography (CT) scan as input information. As in previously-proposed principal component analysis (PCA) population-based models, we have used the data to obtain the dominant eigenmodes that describe bladder geometric variations between fractions. However, we have used a longitudinal analysis along each mode in order to properly characterize patient's variance from the total population variance. We have proposed is a mixed-effects (ME) model in order to separate intra- and inter-patient variability, in an effort to control confounding cohort effects. Other than using PCA, bladder shapes are represented by using spherical harmonics (SPHARM) that additionally enables data compression without information lost. Based on training data from repeated CT scans, the ME model was thus implemented following dimensionality reduction by means of SPHARM and PCA. We have evaluated the model in a leave-one-out cross validation framework on the training data but also using independent data. Probability maps (PMs) were thus generated with several draws from the learnt model as predicted regions where the bladder will likely move and deform. These PMs were compared with the actual regions using metrics based on mutual information distance and misestimated voxels. The prediction was also compared with two previous population PCA-based models. The proposed model was able to reduce the uncertainties in the estimation of the probable region of bladder motion and deformation. This model can thus be used for tailoring radiotherapy treatments.

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Reducing systematic errors due to deformation of organs at risk in radiotherapy

et al. 2021

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In radiotherapy (RT), the planning CT (pCT) is commonly used to plan the full RT-course. Due to organ deformation and motion, the organ shapes seen at the pCT will not be identical to their shapes during RT. Any difference between the pCT organ shape and the organ's mean shape during RT will cause systematic errors. We propose to use statistical shrinkage estimation to reduce this error using only the pCT and the population mean shape computed from training data. Methods: The method was evaluated for the rectum in a cohort of 37 prostate cancer patients that had a pCT and 7-10 treatment CTs with rectum delineations. Deformable registration was performed both within-patient and between patients, resulting in point-to-point correspondence between all rectum shapes, which enabled us to compute a population mean rectum. Shrinkage estimates were found by combining the pCTs linearly with the population mean. The method was trained and evaluated using leave-one-out cross validation. The shrinkage estimates and the patient mean shapes were compared geometrically using the Dice similarity index (DSI), Hausdorff distance (HD), and bidirectional local distance. Clinical dose/volume histograms, equivalent uniform dose (EUD) and minimum dose to the hottest 5% volume (D5%) were compared for the shrinkage estimate and the pCT. Results: The method resulted in moderate but statistically significant increase in similarity to the patient mean shape over the pCT. On average, the HD was reduced from 15.6 to 13.4 mm, while the DSI was increased from 0.74 to 0.78. Significant reduction in the bias of volume estimates was found in the DVHrange of 52.5-65 Gy, where the bias was reduced from −1.3 to −0.2 percentage points, but no significant improvement was found in EUD or D5%, Conclusions: The results suggest that shrinkage estimation can reduce systematic errors due to organ deformations in RT. The method has potential to increase the accuracy in RT of deformable organs and can improve motion modeling.

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Statistical Modeling of CTV Motion and Deformation for IMRT of Early-Stage Rectal Cancer

Cited by 17 publications

References 16 publications

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

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

Population model of bladder motion and deformation based on dominant eigenmodes and mixed-effects models in prostate cancer radiotherapy

Reducing systematic errors due to deformation of organs at risk in radiotherapy

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