Plan robustness and RBE influence for proton dose painting by numbers for head and neck cancers.

PURPOSE: To investigate the feasibility of dose painting by numbers (DPBN) with respect to robustness for proton therapy for head and neck cancers (HNC), and to study the influence of variable RBE on the TCP and OAR dose burden. METHODS AND MATERIALS: Data for 19 patients who have been scanned pretreatment with PET-FDG and subsequently treated with photon therapy were used in the study. A dose response model developed for photon therapy was implemented in a TPS, allowing DPBN plans to be created. Conventional homogeneous dose and DPBN plans were created for each patient, optimized with either fixed RBE = 1.1 or a variable RBE model. Robust optimization was used to create clinically acceptable plans. To estimate the maximum potential loss in TCP due to actual SUV variations from the pre-treatment imaging, we applied a test case with randomized SUV distribution. RESULTS: Regardless of the use of variable RBE for optimization or evaluation, a statistically significant increase (p < 0.001) in TCP was found for DPBN plans as compared to homogeneous dose plans. Randomizing the SUV distribution decreased the TCP for all plans. A correlation between TCP increase and variance of the SUV distribution and target volume was also found. CONCLUSION: DPBN for protons and HNC is feasible and could lead to a TCP gain. Risks associated with the temporal variation of SUV distributions could be mitigated by imposing minimum doses to targets. The correlation found between TCP increase and SUV variance and target volume may be used for patient selection.

Modelling tissue specific RBE for different radiation qualities based on a multiscale characterization of energy deposition.

PURPOSE: We present the nanoCluE model, which uses nano- and microdosimetric quantities to model RBE for protons and carbon ions. Under the hypothesis that nano- and microdosimetric quantities correlates with the generation of complex DNA double strand breakes, we wish to investigate whether an improved accuracy in predicting LQ parameters may be achieved, compared to some of the published RBE models. METHODS: The model is based on experimental LQ data for protons and carbon ions. We generated a database of track structure data for a number of proton and carbon ion kinetic energies with the Geant4-DNA Monte Carlo code. These data were used to obtain both a nanodosimetric quantity and a set of microdosimetric quantities. The latter were tested with different parameterizations versus experimental LQ-data to select the variable and parametrization that yielded the best fit. RESULTS: For protons, the nanoCluE model yielded, for the ratio of the linear LQ term versus the test data, a root mean square error (RMSE) of 1.57 compared to 1.31 and 1.30 for two earlier other published proton models. For carbon ions the RMSE was 2.26 compared to 3.24 and 5.24 for earlier published carbon ion models. CONCLUSION: These results demonstrate the feasibility of the nanoCluE RBE model for carbon ions and protons. The increased accuracy for carbon ions as compared to two other considered models warrants further investigation.

Adaptive dose painting for prostate cancer.

Purpose: Dose painting (DP) is a radiation therapy (RT) strategy for patients with heterogeneous tumors delivering higher dose to radiation resistant regions and less to sensitive ones, thus aiming to maximize tumor control with limited side effects. The success of DP treatments is influenced by the spatial accuracy in dose delivery. Adaptive RT (ART) workflows can reduce the overall geometric dose delivery uncertainty. The purpose of this study is to dosimetrically compare ART and non-adaptive conventional RT workflows for delivery of DP prescriptions in the treatment of prostate cancer (PCa). Materials and methods: We performed a planning and treatment simulation study of four study arms. Adaptive and conventional workflows were tested in combination with DP and Homogeneous dose. We used image data from 5 PCa patients that had been treated on the Elekta Unity MR linac; the patients had been imaged in treatment position before each treatment fraction (7 in total). The local radiation sensitivity from apparent diffusion coefficient maps of 15 high-risk PCa patients was modelled in a previous study. these maps were used as input for optimization of DP plans aiming for maximization of tumor control probability (TCP) under rectum dose constraints. A range of prostate doses were planned for the homogeneous arms. Adaptive plans were replanned based on the anatomy-of-the-day, whereas conventional plans were planned using a pre-treatment image and subsequently recalculated on the anatomy-of-the-day. The dose from 7 fractions was accumulated using dose mapping. The endpoints studied were the TCP and dose-volume histogram metrics for organs at risk. Results: Accumulated DP doses (adaptive and conventional) resulted in high TCP, between 96-99%. The largest difference between adaptive and conventional DP was 2.6 percentage points (in favor of adaptive DP). An analysis of the dose per fraction revealed substantial target misses for one patient in the conventional workflow that-if systematic-could jeopardize the TCP. Compared to homogeneous prescriptions with equal mean prostate dose, DP resulted in slightly higher TCP. Conclusion: Compared to homogeneous dose, DP maintains or marginally increases the TCP. Adaptive DP workflows could avoid target misses compared to conventional workflows.

Dosimetric robustness of lung tumor photon radiotherapy evaluated from multiple event CT imaging.

PURPOSE: Intrafractional respiratory motion is a concern for lung tumor radiotherapy but full evaluation of its impact is hampered by the lack of images representing the true motion. This study presents a novel evaluation using free-breathing images acquired over realistic treatment times to study the dosimetric impact of respiratory motion in photon radiotherapy. METHODS: Cine-CT images of 14 patients with lung cancer acquired during eight minutes of free-breathing at three occasions were used to simulate dose tracking of four different planning methods. These methods aimed to deliver 54 Gy in three fractions to D50% of the target and were denoted as robust 4D (RB4), homogeneous fluence to the ITV (FLU) and an isodose prescription to the ITV with a high central dose (ISD), concurrently renormalized (IRN). Differences in dose coverage probability and homogeneity between the methods were quantified. Correlations between underdosage and attributes regarding the tumor and its motion were investigated. RESULTS: Despite tumor motion amplitudes being larger than in the 4DCT all but FLU achieved the intended CTV D50% for the cohort average. For all methods but IRN at least 93% of the patients would have received 95% of the intended dose. No differences in D50% were found between RB4 and ISD nor IRN. However, RB4 led to better homogeneity. CONCLUSIONS: Tumor motion in free-breathing not covered by the 4DCT had a small impact on dose. The RB4 is recommended for planning of free-breathing treatments. No factor was found that consistently correlated dose degradation with patient or motion attributes.

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.

Handling of beam spectra in training and application of proton RBE models.

Published data from cell survival experiments are frequently used as training data for models of proton relative biological effectiveness (RBE). The publications rarely provide full information about the primary particle spectrum of the used beam, or its content of heavy secondary particles. The purpose of this paper is to assess to what extent heavy secondary particles may have been present in published cell survival experiments, and to investigate the impact of non-primary protons for RBE calculations in treatment planning. We used the Monte Carlo code Geant4 to calculate the occurrence of non-primary protons and heavier secondary particles for clinical protons beams in water for four incident energies in the [100, 250] MeV interval. We used the resulting spectra together with a conservative RBE parameterization and an RBE model to map both the rise of RBE at the beam entry surface due to heavy secondary particle buildup, and the difference in estimated RBE if non-primary protons are included or not in the beam quality metric. If included, non-primary protons cause a difference of 2% of the RBE in the plateau region of an spread out Bragg peak and 1% in the Bragg peak. Including non-primary protons specifically for RBE calculations will consequently have a negligible impact and can be ignored. A buildup distance in water of one millimeter was sufficient to reach an equilibrium state of RBE for the four incident energies selected. For the investigated experimental data, 83 out of the 86 data points were found to have been determined with at least that amount of buildup material. Hence, RBE model training data should be interpreted to include the contribution of heavy secondaries.

Evaluation of four surface surrogates for modeling lung tumor positions over several fractions in radiotherapy.

Patient breathing during lung cancer radiotherapy reduces the ability to keep a sharp dose gradient between tumor and normal tissues. To mitigate detrimental effects, accurate information about the tumor position is required. In this work, we evaluate the errors in modeled tumor positions over several fractions of a simple tumor motion model driven by a surface surrogate measure and its time derivative. The model is tested with respect to four different surface surrogates and a varying number of surrogate and image acquisitions used for model training. Fourteen patients were imaged 100 times with cine CT, at three sessions mimicking a planning session followed by two treatment fractions. Patient body contours were concurrently detected by a body surface laser scanning system BSLS from which four surface surrogates were extracted; thoracic point TP, abdominal point AP, the radial distance mean RDM, and a surface derived volume SDV. The motion model was trained on session 1 and evaluated on sessions 2 and 3 by comparing modeled tumor positions with measured positions from the cine images. The number of concurrent surrogate and image acquisitions used in the training set was varied, and its impact on the final result was evaluated. The use of AP as a surface surrogate yielded the smallest error in modeled tumor positions. The mean deviation between modeled and measured tumor positions was 1.9 mm. The corresponding deviations for using the other surrogates were 2.0 mm (RDM), 2.8 mm (SDV), and 3.0 mm (TP). To produce a motion model that accurately models the tumor position over several fractions requires at least 10 simultaneous surrogate and image acquisitions over 1-2 minutes.

Report of AAPM Task Group 155: Megavoltage photon beam dosimetry in small fields and non-equilibrium conditions.

Small-field dosimetry used in advance treatment technologies poses challenges due to loss of lateral charged particle equilibrium (LCPE), occlusion of the primary photon source, and the limited choice of suitable radiation detectors. These challenges greatly influence dosimetric accuracy. Many high-profile radiation incidents have demonstrated a poor understanding of appropriate methodology for small-field dosimetry. These incidents are a cause for concern because the use of small fields in various specialized radiation treatment techniques continues to grow rapidly. Reference and relative dosimetry in small and composite fields are the subject of the International Atomic Energy Agency (IAEA) dosimetry code of practice that has been published as TRS-483 and an AAPM summary publication (IAEA TRS 483; Dosimetry of small static fields used in external beam radiotherapy: An IAEA/AAPM International Code of Practice for reference and relative dose determination, Technical Report Series No. 483; Palmans et al., Med Phys 45(11):e1123, 2018). The charge of AAPM task group 155 (TG-155) is to summarize current knowledge on small-field dosimetry and to provide recommendations of best practices for relative dose determination in small megavoltage photon beams. An overview of the issue of LCPE and the changes in photon beam perturbations with decreasing field size is provided. Recommendations are included on appropriate detector systems and measurement methodologies. Existing published data on dosimetric parameters in small photon fields (e.g., percentage depth dose, tissue phantom ratio/tissue maximum ratio, off-axis ratios, and field output factors) together with the necessary perturbation corrections for various detectors are reviewed. A discussion on errors and an uncertainty analysis in measurements is provided. The design of beam models in treatment planning systems to simulate small fields necessitates special attention on the influence of the primary beam source and collimating devices in the computation of energy fluence and dose. The general requirements for fluence and dose calculation engines suitable for modeling dose in small fields are reviewed. Implementations in commercial treatment planning systems vary widely, and the aims of this report are to provide insight for the medical physicist and guidance to developers of beams models for radiotherapy treatment planning systems.

Evaluation of irregular breathing effects on internal target volume definition for lung cancer radiotherapy.

PURPOSE: Irregular breathing may compromise the treated volume for free-breathing lung cancer patients during radiotherapy. We try to find a measure based on a breathing amplitude surrogate that can be used to select the patients who need further investigation of tumor motion to ensure that the internal target volume (ITV) provides reliant coverage of the tumor. MATERIAL AND METHODS: Fourteen patients were scanned with four-dimensional computed tomography (4DCT) during free-breathing. The breathing motion was detected by a pneumatic bellows device used as a breathing amplitude surrogate. In addition to the 4DCT, a breath-hold (BH) scan and three cine CT imaging sessions were acquired. The cine images were taken at randomized intervals at a rate of 12 per minute for 8 minutes to allow tumor motion determination during a typical hypo-fractionated treatment scenario. A clinical target volume (CTV) was segmented in the BH CT and propagated over all cine images and 4DCT bins. The center-of-volume of the translated CTV (CTVCOV ) in the ten 4DCT bins were interconnected to define the 4DCT determined tumor trajectory (4DCT-TT). The volume of CTV inside ITV for all cine CTs was calculated and reported at the 10th percentile (VCTV10% ). The deviations between CTVCOV in the cine CTs and the 4DCT-TT were calculated and reported at its 90th percentile (d90% ). The standard deviation of the bellows amplitude peaks (SDP) and the ratio between large and normal inspirations, κrel , were tested as surrogates for VCTV10% and d90% . RESULTS: The values of d90% ranged from 0.6 to 5.2 mm with a mean of 2.2 mm. The values of VCTV10% ranged from 59-93% with a mean of 78 %. The SDP had a moderate correlation (r = 0.87) to d90% . Less correlation was seen between SDP and VCTV10% (r = 0.77), κrel and d90% (r = 0.75) and finally κrel and VCTV10% (r = 0.75). CONCLUSIONS: The ITV coverage had a large variation for some patients. SDP seems to be a feasible surrogate measure to select patients that needs further tumor motion determination.

Robust treatment planning of dose painting for prostate cancer based on ADC-to-Gleason score mappings - what is the potential to increase the tumor control probability?

BACKGROUND AND PURPOSE: The aim of this study was to evaluate the potential to increase the tumor control probability (TCP) with 'dose painting by numbers' (DPBN) plans optimized in a treatment planning system (TPS) compared to uniform dose plans. The DPBN optimization was based on our earlier published formalism for prostate cancer that is driven by dose-responses of Gleason scores mapped from apparent diffusion coefficients (ADC). MATERIAL AND METHODS: For 17 included patients, a set of DPBN plans were optimized in a TPS by maximizing the TCP for an equal average dose to the prostate volume (CTVT) as for a conventional uniform dose treatment. For the plan optimizations we applied different photon energies, different precisions for the ADC-to-Gleason mappings, and different CTVT positioning uncertainties. The TCP increasing potential was evaluated by the DPBN efficiency, defined as the ratio of TCP increases for DPBN plans by TCP increases for ideal DPBN prescriptions (optimized without considering radiation transport phenomena, uncertainties of the CTVT positioning, and uncertainties of the ADC-to-Gleason mapping). RESULTS: The median DPBN efficiency for the most conservative planning scenario optimized with a low precision ADC-to-Gleason mapping, and a positioning uncertainty of 0.6 cm was 10%, meaning that more than half of the patients had a TCP gain of at least 10% of the TCP for an ideal DPBN prescription. By increasing the precision of the ADC-to-Gleason mapping, and decreasing the positioning uncertainty the median DPBN efficiency increased by up to 40%. CONCLUSIONS: TCP increases with DPBN plans optimized in a TPS were found more likely with a high precision mapping of image data into dose-responses and a high certainty of the tumor positioning. These findings motivate further development to ensure precise mappings of image data into dose-responses and to ensure a high spatial certainty of the tumor positioning when implementing DPBN clinically.

Probabilistic optimization of dose coverage in radiotherapy.

BACKGROUND AND PURPOSE: Probabilistic optimization is an alternative to margins for handling geometrical uncertainties in treatment planning of radiotherapy where uncertainties are explicitly incorporated in the optimization. We present a novel probabilistic method based on the same statistical measures as those behind conventional margin based planning. MATERIAL AND METHODS: Percentile Dosage (PD) was defined as the dose coverage that a treatment plan meet or exceed to a given probability. For optimization, we used the convex measure Expected Percentile Dosage (EPD) defined as the average dose coverage below a given PD. An iterative method gradually adjusted the constraint tolerance associated with the EPD until the desired target PD was met. It was applied to planning of cervical cancer patients focusing on systematic uncertainty caused by organ deformation. The resulting plans were compared to margin based plans using target and organ at risk PDs. RESULTS: The EPD tolerance converged in less than ten iterations to produce a PD within 0.1 Gy of the requested. The PD was on average within 0.5% of the requested PD when validated versus independent scenarios. The rectum volume, extracted from the PDs, receiving 90% of the intended target dose was decreased with 16% for the same target PD in comparison to margin based plans. CONCLUSIONS: The proposed probabilistic optimization method enabled prescription of a dose volume histogram metric to a chosen confidence. The probabilistic plans showed improved target dose homogeneity and decreased rectum dose for the same target dose coverage compared to margin based plans.

Robust maximization of tumor control probability for radicality constrained radiotherapy dose painting by numbers of head and neck cancer.

BACKGROUND AND PURPOSE: Radiotherapy with dose painting by numbers (DPBN) needs another approach than conventional margins to ensure a geometrically robust dose coverage for the tumor. This study presents a method to optimize DPBN plans that as opposed to achieve a robust dose distribution instead robustly maximize the tumor control probability (TCP) for patients diagnosed with head and neck cancer. MATERIAL AND METHODS: Volumetric-modulated arc therapy (VMAT) plans were optimized with a robust TCP maximizing objective for different dose constraints to the primary clinical target volume (CTVT) for a set of 20 patients. These plans were optimized with minimax optimization together with dose-responses driven by standardized uptake values (SUV) from 18F-fluorodeoxyglucose positron emission tomography (18FDG-PET). The robustness in TCP was evaluated through sampling treatment scenarios with isocenter displacements. RESULTS: The average increase in TCP with DPBN compared to a homogeneous dose treatment ranged between 3 and 20 percentage points (p.p.) which depended on the different dose constraints for the CTVT. The median deviation in TCP increase was below 1p.p. for all sampled treatment scenarios versus the nominal plans. The standard deviation of SUV multiplied by the CTVT volume were found to correlate with the TCP gain with R 2 ≥ 0.9. CONCLUSIONS: Minimax optimization of DPBN plans yield, based on the presented TCP modelling, a robust increase of the TCP compared to homogeneous dose treatments for head and neck cancers. The greatest TCP gains were found for patients with large and SUV heterogeneous tumors, which may give guidance for patient selection in prospective trials.

Toward automated and personalized organ dose determination in CT examinations - A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System.

PURPOSE: Computed tomography (CT) is a versatile tool in diagnostic radiology with rapidly increasing number of examinations per year globally. Routine adaption of the exposure level for patient anatomy and examination protocol cause the patients' exposures to become diversified and harder to predict by simple methods. To facilitate individualized organ dose estimates, we explore the possibility to automate organ dose calculations using a radiotherapy treatment planning system (TPS). In particular, the mapping of CT number to elemental composition for Monte Carlo (MC) dose calculations is investigated. METHODS: Organ dose calculations were done for a female thorax examination test case with a TPS (Raystation™, Raysearch Laboratories AB, Stockholm, Sweden) utilizing a MC dose engine with a CT source model presented in a previous study. The TPS's inherent tissue characterization model for mapping of CT number to elemental composition of the tissues was calibrated using a phantom with known elemental compositions and validated through comparison of MC calculated dose with dose measured with Thermo Luminescence Dosimeters (TLD) in an anthropomorphic phantom. Given the segmentation tools of the TPS, organ segmentation strategies suitable for automation were analyzed for high contrast organs, utilizing CT number thresholding and model-based segmentation, and for low contrast organs utilizing water replacements in larger tissue volumes. Organ doses calculated with a selection of organ segmentation methods in combination with mapping of CT numbers to elemental composition (RT model), normally used in radiotherapy, were compared to a tissue characterization model with organ segmentation and elemental compositions defined by replacement materials [International Commission on Radiological Protection (ICRP) model], frequently favored in imaging dosimetry. RESULTS: The results of the validation with the anthropomorphic phantom yielded mean deviations from the dose to water calculated with the RT and ICRP model as measured with TLD of 1.1% and 1.5% with maximum deviations of 6.1% and 8.7% respectively over all locations in the phantom. A strategy for automated organ segmentation was evaluated for two different risk organ groups, that is, low contrast soft organs and high contrast organs. The relative deviation between organ doses calculated with the RT model and with the ICRP model varied between 0% and 20% for the thorax/upper abdomen risk organs. CONCLUSIONS: After calibration, the RT model in the TPS provides accurate MC dose results as compared to measurements with TLD and the ICRP model. Dosimetric feasible segmentation of the risk organs for a female thorax demonstrates a possibility for automation using the segmentation tool available in a TPS for high contrast organs. Low contrast soft organs can be represented by water volumes, but organ dose to the esophagus and thyroid must be determined using standardized organ shapes. The uncertainties of the organ doses are small compared to the overall uncertainty, at least an order of magnitude larger, in the estimates of lifetime attributable risk (LAR) based on organ doses. Large-scale and automated individual organ dose calculations could provide an improvement in cancer incidence estimates from epidemiological studies.

Target Size Variation in Microdosimetric Distributions and its Impact on the Linear-Quadratic Parameterization of Cell Survival.

The linear-quadratic (LQ) parameterization of survival fraction [SF( D)] inherently assumes that all cells in a population receive the same dose ( D), albeit the distribution of specific energy z over the individual cells f( z, D) can be very wide. From these microdosimetric distributions, which are target size dependent, we estimate the size of the cellular sensitive volume by analyzing its influence on the LQ parameterization of cell survival. A Monte Carlo track structure code was used to simulate detailed tracks from a 60Co source as well as proton and carbon ions of various energies. From these tracks, f( z, D) distributions were calculated for spherical targets with diameters ranging from 10 nm to 12 µm. A cell survival function based on f( z, D) was fitted to experimental LQ α values, revealing an intrinsic limitation that target size imposes on the usage of f( z, D) to describe the linear term of the LQ parameterization. The results indicate that such threshold volume arises naturally from the relationship between the particle's probability of no-hit and the probability of cell survival. Further analysis led to the proposal of a radiobiological property [Formula: see text], defined as the mean lineal energy corresponding to the target size that allows equivalence between the mean inactivation dose (MID) and the mean specific energy [Formula: see text]. The fact that [Formula: see text] is an increasing continuous function of target size within the range of biological targets of interest in radiobiology, ensures the uniqueness of [Formula: see text] for any radiation quality, thus, its potential usefulness in modeling. In conclusion, an accurate estimation of such threshold volumes may be useful for improving modeling of cell survival curves.

Reproducibility of heart and thoracic wall position in repeated deep inspiration breath holds for radiotherapy of left-sided breast cancer patients.

BACKGROUND: Deep inspiration breath hold (DIBH) for radiotherapy of left-sided breast cancer patients can effectively move the heart away from the target and reduce the heart dose compared to treatments in free breathing. This study aims to investigate the positional reproducibility of heart edge (HE) and thoracic wall (TW) during repeated DIBHs. MATERIAL AND METHODS: At three occasions, 11 left-sided breast cancer patients were CT imaged during 6 minutes of repeated DIBHs with 60 cine CT series. The series were evenly distributed over three bed positions and for each bed position, the heart edge associated maximum heart distance (MHD) and thoracic wall-associated maximum lung distance (MLD) from a reference line were retrospectively analyzed. The high temporal resolution of the CT series enabled intrinsic heart movements to be resolved from breath hold variations. A body surface laser scanning system continuously extracted the thorax height and displayed it in a pair of goggles for patient feedback. To check for 'fake-breathing' movements, e.g. that the patient lifts its back from the couch to reach DIBH, the couch-to-spine distance was also measured in all CT series. RESULTS: The analysis was done for 1432 cine CTs captured during 292 breath holds. The DIBH moved the heart on average 15 mm in medial direction compared with free breathing. For the three bed positions studied, the mean value of the max range, across all patients, was between 11-13 mm for the MHD and 4-8 mm for the MLD. The MHD variation due to breath hold variation was twice as large as the MHD variation due to intrinsic heart movement. The couch-to-spine distance varied less than 3 mm for all fractions, i.e., no fake-breathing was discovered. CONCLUSIONS: The heart edge and thoracic wall reproducibility was high in relation to the medial heart displacement induced by the DIBH.

Evaluation of two commercial CT metal artifact reduction algorithms for use in proton radiotherapy treatment planning in the head and neck area.

PURPOSE: To evaluate two commercial CT metal artifact reduction (MAR) algorithms for use in proton treatment planning in the head and neck (H&N) area. METHODS: An anthropomorphic head phantom with removable metallic implants (dental fillings or neck implant) was CT-scanned to evaluate the O-MAR (Philips) and the iMAR (Siemens) algorithms. Reference images were acquired without any metallic implants in place. Water equivalent thickness (WET) was calculated for different path directions and compared between image sets. Images were also evaluated for use in proton treatment planning for parotid, tonsil, tongue base, and neck node targets. The beams were arranged so as to not traverse any metal prior to the target, enabling evaluation of the impact on dose calculation accuracy from artifacts surrounding the metal volume. Plans were compared based on γ analysis (1 mm distance-to-agreement/1% difference in local dose) and dose volume histogram metrics for targets and organs at risk (OARs). Visual grading evaluation of 30 dental implant patient MAR images was performed by three radiation oncologists. RESULTS: In the dental fillings images, ΔWET along a low-density streak was reduced from -17.0 to -4.3 mm with O-MAR and from -16.1 mm to -2.3 mm with iMAR, while for other directions the deviations were increased or approximately unchanged when the MAR algorithms were used. For the neck implant images, ΔWET was generally reduced with MAR but residual deviations remained (of up to -2.3 mm with O-MAR and of up to -1.5 mm with iMAR). The γ analysis comparing proton dose distributions for uncorrected/MAR plans and corresponding reference plans showed passing rates >98% of the voxels for all phantom plans. However, substantial dose differences were seen in areas of most severe artifacts (γ passing rates of down to 89% for some cases). MAR reduced the deviations in some cases, but not for all plans. For a single patient case dosimetrically evaluated, minor dose differences were seen between the uncorrected and MAR plans (γ passing rate approximately 97%). The visual grading of patient images showed that MAR significantly improved image quality (P < 0.001). CONCLUSIONS: O-MAR and iMAR significantly improved image quality in terms of anatomical visualization for target and OAR delineation in dental implant patient images. WET calculations along several directions, all outside the metallic regions, showed that both uncorrected and MAR images contained metal artifacts which could potentially lead to unacceptable errors in proton treatment planning. ΔWET was reduced by MAR in some areas, while increased or unchanged deviations were seen for other path directions. The proton treatment plans created for the phantom images showed overall acceptable dose distributions differences when compared to the reference cases, both for the uncorrected and MAR images. However, substantial dose distribution differences in the areas of most severe artifacts were seen for some plans, which were reduced by MAR in some cases but not all. In conclusion, MAR could be beneficial to use for proton treatment planning; however, case-by-case evaluations of the metal artifact-degraded images are always recommended.

Dose painting of prostate cancer based on Gleason score correlations with apparent diffusion coefficients.

BACKGROUND: Gleason scores for prostate cancer correlates with an increased recurrence risk after radiotherapy (RT). Furthermore, higher Gleason scores correlates with decreasing apparent diffusion coefficient (ADC) data from diffusion weighted MRI (DWI-MRI). Based on these observations, we present a formalism for dose painting prescriptions of prostate volumes based on ADC images mapped to Gleason score driven dose-responses. METHODS: The Gleason score driven dose-responses were derived from a learning data set consisting of pre-RT biopsy data and post-RT outcomes for 122 patients treated with a homogeneous dose to the prostate. For a test data set of 18 prostate cancer patients with pre-RT ADC images, we mapped the ADC data to the Gleason driven dose-responses by using probability distributions constructed from published Gleason score correlations with ADC data. We used the Gleason driven dose-responses to optimize dose painting prescriptions that maximize the tumor control probability (TCP) with equal average dose as for the learning sets homogeneous treatment dose. RESULTS: The dose painting prescriptions increased the estimated TCP compared to the homogeneous dose by 0-51% for the learning set and by 4-30% for the test set. The potential for individual TCP gains with dose painting correlated with increasing Gleason score spread and larger prostate volumes. The TCP gains were also found to be larger for patients with a low expected TCP for the homogeneous dose prescription. CONCLUSIONS: We have from retrospective treatment data demonstrated a formalism that yield ADC driven dose painting prescriptions for prostate volumes that potentially can yield significant TCP increases without increasing dose burdens as compared to a homogeneous treatment dose. This motivates further development of the approach to consider more accurate ADC to Gleason mappings, issues with delivery robustness of heterogeneous dose distributions, and patient selection criteria for design of clinical trials.

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

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.

Collapsed cone dose calculations for heterogeneous tissues in brachytherapy using primary and scatter separation source data.

BACKGROUND AND OBJECTIVE: Brachytherapy is a form of radiation therapy using sealed radiation sources inserted within or in the vicinity of the tumor of, e.g., gynecological, prostate or head- and neck cancers. Accurate dose calculation is a crucial part of the treatment planning. Several reviews have called for clinical software with model-based algorithms that better take into account the effects of patient individual distribution of tissues, source-channel and shielding attenuation than the commonly employed TG-43 formalism which simply map homogeneous water dose distributions onto the patient. In this paper we give a comprehensive and thorough derivation of such an algorithm based on collapsed cone point-kernel superposition, and describe details of its implementation into a commercial treatment planning system for clinical use. METHODS: A brachytherapy version of the collapsed-cone algorithm using analytical raytraces of the primary photon radiation followed by successive scattering dose calculation for once- and multiply scattered photons is described in detail, including derivation of the corresponding set of recursive equations for energy transport along cone axes/transport lines and the coupling to clinical source modeling. Specific implementation issues for setting up of the calculation grid, handling of intravoxel gradients and voxels partly containing non-patient applicator material are given. RESULTS: Sample runs for two clinical cases are shown, one being a gynecological application with a tungsten-shielded applicator and one a breast implant. These two cases demonstrate the impact of improved dose calculation versus TG-43 formalism. CONCLUSIONS: Use of model-based dose calculation algorithms for brachytherapy taking the three-dimensional treatment geometry into account increases the dosimetric accuracy in planning and follow up of treatments. The comprehensive description and derivations provided gives a rigid background for further clinical, educational and research applications.