Dosimetric treatment course simulation based on a statistical model of deformable organ motion

Probabilistic evaluation of plan quality for time-dependent anatomical deformations in head and neck cancer patients

PURPOSE

In addition to patient set-up uncertainties, anatomical deformations, e.g., weight loss, lead to time-dependent differences between the planned and delivered dose in a radiotherapy course that currently cannot easily be predicted. The aim of this study was to create time-varying prediction models to describe both the average and residual anatomical deformations.

METHODS

Weekly population-based principal component analysis models were generated from on-treatment cone-beam CT scans (CBCTs) of 30 head and neck cancer patients, with additional data of 35 patients used as a validation cohort. We simulated treatment courses accounting for a) anatomical deformations, b) set-up uncertainties and c) a combination of both. The dosimetric effects of the simulated deformations were compared to a direct dose accumulation based on deformable registration of the CBCT data.

RESULTS

Set-up uncertainties were seen to have a larger effect on the organ at risk (OAR) doses than anatomical deformations for all OARs except the larynx and the primary CTV. Distributions from simulation results were in good agreement with those of the accumulated dose.

CONCLUSIONS

We present a novel method of modelling time-varying organ deformations in head and neck cancer. The effect on the OAR doses from these deformations are smaller than the effect of set-up uncertainties for most OARs. These models can, for instance, be used to predict which patients could benefit from adaptive radiotherapy, prior to commencing treatment.

A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy

Objective. In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. To assess the need for adaptation, motion models can be used to simulate dominant motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same set of deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient.Approach. We propose a deep learning probabilistic framework that generates deformation vector fields warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs with prostate, bladder, and rectum delineations from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and 'ground truth' distributions of volume and center of mass changes.Results. With a DICE score of 0.86 ± 0.05 and a distance between prostate contours of 1.09 ± 0.93 mm, DAM matches and improves upon previously published PCA-based models, using as few as 8 latent variables. The overlap between distributions further indicates that DAM's sampled movements match the range and frequency of clinically observed daily changes on repeat CTs.Significance. Conditioned only on planning CT values and organ contours of a new patient without any pre-processing, DAM can accurately deformations seen during following treatment sessions, enabling anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes.

An organ deformation model using Bayesian inference to combine population and patient-specific data

Objective.Organ deformation models have the potential to improve delivery and reduce toxicity of radiotherapy, but existing data-driven motion models are based on either patient-specific or population data. We propose to combine population and patient-specific data using a Bayesian framework. Our goal is to accurately predict individual motion patterns while using fewer scans than previous models.Approach.We have derived and evaluated two Bayesian deformation models. The models were applied retrospectively to the rectal wall from a cohort of prostate cancer patients. These patients had repeat CT scans evenly acquired throughout radiotherapy. Each model was used to create coverage probability matrices (CPMs). The spatial correlations between these estimated CPMs and the ground truth, derived from independent scans of the same patient, were calculated.Main results.Spatial correlation with ground truth were significantly higher for the Bayesian deformation models than both patient-specific and population-derived models with 1, 2 or 3 patient-specific scans as input. Statistical motion simulations indicate that this result will also hold for more than 3 scans.Significance.The improvement over previous models means that fewer scans per patient are needed to achieve accurate deformation predictions. The models have applications in robust radiotherapy planning and evaluation, among others.

Evaluating principal component analysis models for representing anatomical changes in head and neck radiotherapy

Background and purpose

Anatomical changes during radiotherapy pose a challenge to robustness of plans. Principal component analysis (PCA) is commonly used to model such changes. We propose a toolbox to evaluate how closely a given PCA model can represent actual deformations seen in the patient and highlight regions where the model struggles to capture these changes.

Materials and methods

We propose to calculate a residual error map from the difference between an actual displacement vector field (DVF) and the closest DVF that the PCA model can produce. This was done by taking the inner product of the DVF with the PCA components from the model. As a global measure of error, the 90th percentile of the residual errors (Mres90) across the whole scan was used. As proof of principle, we demonstrated this approach on both patient-specific cases and a population-based PCA in head and neck (H&N) cancer patients. These models were created using deformation data from deformable registrations between the planning computed tomography and cone-beam computed tomography (CBCTs), and were evaluated against DVFs from registrations of CBCTs not used to create the model.

Results

For our example cases, the oropharyngeal and the nasal cavity regions showed the largest local residual error, indicating the PCA models struggle to predict deformations seen in these regions. Mres90 ranged from 0.4 mm to 6.3 mm across the different models.

Conclusions

A method to quantitatively evaluate how well PCA models represent observed anatomical changes was proposed. We demonstrated our approach on H&N PCA models, but it can be applied to other sites.

Intrafractional motion models based on principal components in Magnetic Resonance guided prostate radiotherapy

Background and purpose

Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region.

Materials and methods

4D-scans of nine healthy male volunteers were utilized, FOV covering the entire pelvic region including prostate, bladder and rectum with manual segmentation of each organ at each time frame. Deformable image registration with an optical flow algorithm was performed for each subject with the first time frame as reference. PCA was performed on a subset of the resulting displacement vector fields to construct individualised motion models evaluated on the remaining fields.

Results

The registration algorithm produced accurate registration result, in general DICE overlap >0.95 across all time frames. Cumulative variance of the eigen values from the PCA showed that 50% or more of the motion is explained in the first component for all subjects. However, the size and direction for the components differed between subjects. Adding more than two components did not improve the accuracy significantly and the model was able to explain motion down to about 1 mm.

Conclusions

An individualised intrafractional male pelvic motion model is feasible. Geometric accuracy was about 1 mm based on 1–2 principal components.

Robust radiation therapy optimization using simulated treatment courses for handling deformable organ motion

We describe a radiation therapy treatment plan optimization method that explicitly considers the effects of interfraction organ motion through optimization on the clinical target volume (CTV), and investigate how it compares to conventional planning using a planning target volume (PTV). The method uses simulated treatment courses generated using patient images created by a deformable registration algorithm to replicate the effects of interfraction organ motion, and performs robust optimization aiming to achieve CTV coverage under all simulated treatment courses. The method was applied to photon-mediated treatments of three prostate cases and compared to conventional, PTV-based planning with margins selected to achieve similar CTV coverage as the robustly optimized plans. Clinical goals for the CTV and healthy tissue were used in comparison between the two types of plans. Out of the two clinical goals for overdosage of the CTV, the three robustly optimized plans violated respectively 2, 2, and 0 goals in the mean over the scenarios, whereas none of the PTV plans violated these goals. Of the ten clinical goals for rectum, bladder, anal canal, and bulbus, the robustly optimized plans violated respectively 0, 1, and 1 goals in the mean, whereas the PTV plans violated 5, 7, and 4 goals. Compared to PTV-based planning, the inclusion of treatment course scenarios in the optimization has the potential to reduce the dose to healthy tissues while retaining a high probability of target coverage. This may reduce the need for adaptive replanning.

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

PURPOSE

To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment.

METHODS

Planning CT images of ten H&N patients were artificially deformed to create "digital phantom" images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients' actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment.

RESULTS

Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA's ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion.

CONCLUSIONS

Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are established early in a treatment course, or based on population models.

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

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.

Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations

Purpose

Intensity modulated radiation therapy (IMRT) of head and neck tumors allows a precise conformation of the high-dose region to clinical target volumes (CTVs) while respecting dose limits to organs a risk (OARs). Accurate patient setup reduces translational and rotational deviations between therapy planning and therapy delivery days. However, uncertainties in the shape of the CTV and OARs due to e.g. small pose variations in the highly deformable anatomy of the head and neck region can still compromise the dose conformation. Routinely applied safety margins around the CTV cause higher dose deposition in adjacent healthy tissue and should be kept as small as possible.

Materials and Methods

In this work we evaluate and compare three approaches for margin generation 1) a clinically used approach with a constant isotropic 3 mm margin, 2) a previously proposed approach adopting a spatial model of the patient and 3) a newly developed approach adopting a biomechanical model of the patient. All approaches are retrospectively evaluated using a large patient cohort of over 500 fraction control CT images with heterogeneous pose changes. Automatic methods for finding landmark positions in the control CT images are combined with a patient specific biomechanical finite element model to evaluate the CTV deformation.

Results

The applied methods for deformation modeling show that the pose changes cause deformations in the target region with a mean motion magnitude of 1.80 mm. We found that the CTV size can be reduced by both variable margin approaches by 15.6% and 13.3% respectively, while maintaining the CTV coverage. With approach 3 an increase of target coverage was obtained.

Conclusion

Variable margins increase target coverage, reduce risk to OARs and improve healthy tissue sparing at the same time.

Robustness of sweeping-window arc therapy treatment sequences against intrafractional tumor motion

PURPOSE

Due to the potentially periodic collimator dynamic in volumetric modulated arc therapy (VMAT) dose deliveries with the sweeping-window arc therapy (SWAT) technique, additional manifestations of dosimetric deviations in the presence of intrafractional motion may occur. With a fast multileaf collimator (MLC), and a flattening filter free dose delivery, treatment times close to 60 s per fraction are clinical reality. For these treatment sequences, the human breathing period can be close to the collimator sweeping period. Compared to a random arrangement of the segments, this will cause a further degradation of the dose homogeneity.

METHODS

Fifty VMAT sequences of potentially moving target volumes were delivered on a two dimensional ionization chamber array. In order to detect interplay effects along all three coordinate axes, time resolved measurements were performed twice--with the detector aligned in vertical (V) or horizontal (H) orientation. All dose matrices were then moved within a simulation software by a time-dependent motion vector. The minimum relative equivalent uniform dose EUDr,m for all breathing starting phases was determined for each amplitude and period. Furthermore, an estimation of periods with minimum EUD was performed. Additionally, LINAC logfiles were recorded during plan delivery. The MLC, jaw, gantry angle, and monitor unit settings were continuously saved and used to calculate the correlation coefficient between the target motion and the dose weighed collimator motion component for each direction (CC, LR, AP) separately.

RESULTS

The resulting EUDr,m were EUDr,m(CCV) = (98.3 ± 0.6)%, EUDr,m(CCH) = (98.6 ± 0.5)%, EUDr,m(APV) = (97.7 ± 0.9)%, and EUDr,m(LRH) = (97.8 ± 0.9)%. The overall minimum relative EUD observed for 360(∘) arc midventilation treatments was 94.6%. The treatment plan with the shortest period and a minimum relative EUD of less than 97% was found at T = 6.1 s. For a partial 120(∘) arc, an EUDr,m = 92.0% was found. In all cases, a correlation coefficient above 0.5 corresponded to a minimum in EUD.

CONCLUSIONS

With the advent of fast VMAT delivery techniques, nonrobust treatment sequences for human breathing patterns can be generated. These sequences are characterized by a large correlation coefficient between a target motion component and the corresponding collimator dynamic. By iteratively decreasing the maximum allowed dose rate, a low correlation coefficient and consequentially a robust treatment sequence are ensured.

Coverage-based treatment planning to accommodate deformable organ variations in prostate cancer treatment

PURPOSE

To compare two coverage-based planning (CP) techniques with standard fixed margin-based planning (FM), considering the dosimetric impact of interfraction deformable organ motion exclusively for high-risk prostate treatments.

METHODS

Nineteen prostate cancer patients with 8-13 prostate CT images of each patient were used to model patient-specific interfraction deformable organ changes. The model was based on the principal component analysis (PCA) method and was used to predict the patient geometries for virtual treatment course simulation. For each patient, an IMRT plan using zero margin on target structures, prostate (CTVprostate) and seminal vesicles (CTVSV), were created, then evaluated by simulating 1000 30-fraction virtual treatment courses. Each fraction was prostate centroid aligned. Patients whose D98 failed to achieve 95% coverage probability objective D98,95 ≥ 78 Gy (CTVprostate) or D98,95 ≥ 66 Gy (CTVSV) were replanned using planning techniques: (1) FM (PTVprostate = CTVprostate + 5 mm, PTVSV = CTVSV + 8 mm), (2) CPOM which optimized uniform PTV margins for CTVprostate and CTVSV to meet the coverage probability objective, and (3) CPCOP which directly optimized coverage probability objectives for all structures of interest. These plans were intercompared by computing probabilistic metrics, including 5% and 95% percentile DVHs (pDVH) and TCP/NTCP distributions.

RESULTS

All patients were replanned using FM and two CP techniques. The selected margins used in FM failed to ensure target coverage for 8/19 patients. Twelve CPOM plans and seven CPCOP plans were favored over the other plans by achieving desirable D98,95 while sparing more normal tissues.

CONCLUSIONS

Coverage-based treatment planning techniques can produce better plans than FM, while relative advantages of CPOM and CPCOP are patient-specific.

Forecasting longitudinal changes in oropharyngeal tumor morphology throughout the course of head and neck radiation therapy

PURPOSE

To create models that forecast longitudinal trends in changing tumor morphology and to evaluate and compare their predictive potential throughout the course of radiation therapy.

METHODS

Two morphology feature vectors were used to describe 35 gross tumor volumes (GTVs) throughout the course of intensity-modulated radiation therapy for oropharyngeal tumors. The feature vectors comprised the coordinates of the GTV centroids and a description of GTV shape using either interlandmark distances or a spherical harmonic decomposition of these distances. The change in the morphology feature vector observed at 33 time points throughout the course of treatment was described using static, linear, and mean models. Models were adjusted at 0, 1, 2, 3, or 5 different time points (adjustment points) to improve prediction accuracy. The potential of these models to forecast GTV morphology was evaluated using leave-one-out cross-validation, and the accuracy of the models was compared using Wilcoxon signed-rank tests.

RESULTS

Adding a single adjustment point to the static model without any adjustment points decreased the median error in forecasting the position of GTV surface landmarks by the largest amount (1.2 mm). Additional adjustment points further decreased the forecast error by about 0.4 mm each. Selection of the linear model decreased the forecast error for both the distance-based and spherical harmonic morphology descriptors (0.2 mm), while the mean model decreased the forecast error for the distance-based descriptor only (0.2 mm). The magnitude and statistical significance of these improvements decreased with each additional adjustment point, and the effect from model selection was not as large as that from adding the initial points.

CONCLUSIONS

The authors present models that anticipate longitudinal changes in tumor morphology using various models and model adjustment schemes. The accuracy of these models depended on their form, and the utility of these models includes the characterization of patient-specific response with implications for treatment management and research study design.

Segmentation of pelvic structures for planning CT using a geometrical shape model tuned by a multi-scale edge detector

Accurate segmentation of the prostate and organs at risk in computed tomography (CT) images is a crucial step for radiotherapy planning. Manual segmentation, as performed nowadays, is a time consuming process and prone to errors due to the a high intra- and inter-expert variability. This paper introduces a new automatic method for prostate, rectum and bladder segmentation in planning CT using a geometrical shape model under a Bayesian framework. A set of prior organ shapes are first built by applying principal component analysis to a population of manually delineated CT images. Then, for a given individual, the most similar shape is obtained by mapping a set of multi-scale edge observations to the space of organs with a customized likelihood function. Finally, the selected shape is locally deformed to adjust the edges of each organ. Experiments were performed with real data from a population of 116 patients treated for prostate cancer. The data set was split in training and test groups, with 30 and 86 patients, respectively. Results show that the method produces competitive segmentations w.r.t standard methods (averaged dice = 0.91 for prostate, 0.94 for bladder, 0.89 for rectum) and outperforms the majority-vote multi-atlas approaches (using rigid registration, free-form deformation and the demons algorithm).

Adaptive radiotherapy in locally advanced prostate cancer using a statistical deformable motion model

Daily treatment plan selection from a plan library is a major adaptive radiotherapy strategy to account for individual internal anatomy variations. This strategy depends on the initial input images being representative for the variations observed later in the treatment course. Focusing on locally advanced prostate cancer, our aim was to evaluate if residual motion of the prostate (CTV-p) and the elective targets (CTV-sv, CTV-ln) can be prospectively accounted for with a statistical deformable model based on images acquired in the initial part of treatment.

METHODS

Thirteen patients with locally advanced prostate cancer, each with 9-10 repeat CT scans, were included. Displacement vectors fields (DVF) obtained from contour-based deformable registration of delineations in the repeat- and planning CT scans were used to create patient-specific statistical motion models using principal component analysis (PCA). For each patient and CTV, four PCA-models were created: one with all 9-10 DVF as input in addition to models with only four, five or six DVFs as input. Simulations of target shapes from each PCA-model were used to calculate iso-coverage levels, which were converted to contours. The levels were analyzed for sensitivity and precision.

RESULTS

A union of the simulated shapes was able to cover at least 97%, 97% and 95% of the volumes of the evaluated CTV shapes for PCA-models using six, five and four DVFs as input, respectively. There was a decrease in sensitivity with higher iso-coverage levels, with a sharper decline for greater target movements. Apart from having the steepest decline in sensitivity, CTV-sv also displayed the greatest influence on the number of geometries used in the PCA-model.

CONCLUSIONS

PCA-based simulations of residual motion derived from four to six DVFs as input could account for the majority of the target shapes present during the latter part of the treatment. CTV-sv displayed the greatest range in both sensitivity and precision.

Dynamic Target Definition: a novel approach for PTV definition in ion beam therapy

PURPOSE

To present a beam arrangement specific approach for PTV definition in ion beam therapy.

MATERIALS AND METHODS

By means of a Monte Carlo error propagation analysis a criteria is formulated to assess whether a voxel is safely treated. Based on this a non-isotropical expansion rule is proposed aiming to minimize the impact of uncertainties on the dose delivered.

RESULTS

The method is exemplified in two cases: a Head and Neck case and a Prostate case. In both cases the modality used is proton beam irradiation and the sources of uncertainties taken into account are positioning (set up) errors and range uncertainties. It is shown how different beam arrangements have an impact on plan robustness which leads to different target expansions necessary to assure a predefined level of plan robustness. The relevance of appropriate beam angle arrangements as a way to minimize uncertainties is demonstrated.

CONCLUSIONS

A novel method for PTV definition in on beam therapy is presented. The method show promising results by improving the probability of correct dose CTV coverage while reducing the size of the PTV volume. In a clinical scenario this translates into an enhanced tumor control probability while reducing the volume of healthy tissue being irradiated.