Dose Mass Histogram Research Articles

Averaging of absorbed doses: How matter matters.

Dosimetry in radionuclide therapy often requires the calculation of average absorbed doses within and between spatial regions, for example, for voxel-based dosimetry methods, for paired organs, or across multiple tumors. Formation of such averages can be made in different ways, starting from different definitions. The aim of this study is to formally specify different averaging strategies for absorbed doses, and to compare their results when applied to absorbed dose distributions that are non-uniform within and between regions. For averaging within regions, two definitions of the average absorbed dose are considered: the simple average over the region (the region average) and the average when weighting by the mass density (density-weighted region average). The latter is shown to follow from the definition of mean absorbed dose according to the ICRU, and to be consistent with the MIRD formalism. For averaging between different spatial regions, three definitions follow: the volume-weighted, the mass-weighted, and the unweighted average. With respect to characterizing non-uniformity, the different average definitions lead to the use of dose-volume histograms (DVHs) (region average), dose-mass histograms (DMHs) (density-weighted region average), and unweighted histograms (unweighted average). Average absorbed doses are calculated for three worked examples, starting from the different definitions. The first, schematic, example concerns the calculation of the average absorbed dose between two regions with different volumes or mass densities. The second, stylized, example concerns voxel-based dosimetry, for which the average absorbed-dose rate within a region is calculated. The geometries studied include three 177 Lu-filled voxelized spheres, where the sphere masses are held constant while the material compositions, densities, and volumes are varied. For comparison, the mean absorbed-dose rates obtained using unit-density sphere S-values are also included. The third example concerns SPECT/CT-based tumor dosimetry for five patients undergoing therapy with 177 Lu-PSMA and six patients undergoing therapy with 177 Lu-DOTA-TATE, for which the average absorbed-dose rates across multiple tumors are calculated. For the second and third examples, analyses also include representations by histograms. Example 1 shows that the average absorbed doses, calculated using different definitions, can differ considerably if the masses and absorbed doses for two regions are markedly different. From example 2 it is seen that the density-weighted region average is stable under different activity and density distributions and is also in line with results using S-values. In contrast, the region average varies as function of the activity distribution. In example 3, the absorbed dose rates for individual tumors differ by (1.1±4.3)% and (-0.1±0.4)% with maximum deviations of +34.4% and -1.4% for 177 Lu-PSMA and 177 Lu-DOTA-TATE, respectively, when calculated as region averages or density-weighted region averages, with largest deviations obtained when the density is non-uniform. The average absorbed doses calculated across all tumors are similar when comparing mass-weighted and volume-weighted averages but these differ substantially from unweighted averages. Different strategies for averaging of absorbed doses within and between regions can lead to substantially different absorbed-dose estimates. At reporting of radionuclide therapy dosimetry, it is important to specify the averaging strategy applied.

Deep inspiration breath-hold for left-sided breast irradiation: Analysis of dose-mass histograms and the impact of lung expansion

BackgroundThe aim of this study was to compare dose-volume histogram (DVH) with dose-mass histogram (DMH) parameters for treatment of left-sided breast cancer in deep inspiration breath-hold (DIBH) and free breathing (FB). Additionally, lung expansion and anatomical factors were analyzed and correlated to dose differences.MethodsFor 31 patients 3D conformal radiation therapy plans were retrospectively calculated on FB and DIBH CTs in the treatment planning system. The calculated doses, structures and CT data were transferred into MATLAB and DVHs and DMHs were calculated. Mean doses (Dmean), volumes and masses receiving certain doses (Vx, Mx) were determined for the left lung and the heart. Additionally, expansion of the left lung was evaluated using deformable image registration. Differences in DVH and DMH dose parameters between FB and DIBH were statistically analyzed and correlated to lung expansion and anatomical factors.ResultsDIBH reduced Dmean (DVH) and relative V20 (V20 [%]) of the left lung in all patients, on average by − 19 ± 9% (mean ± standard deviation) and − 24 ± 10%. Dmean (DMH) and M20 [%] were also significantly reduced (− 12 ± 11%, − 16 ± 13%), however 4 patients had higher DMH values in DIBH than in FB. Linear regression showed good correlations between DVH and DMH parameters, e.g. a dosimetric benefit smaller than 8.4% for Dmean (DVH) in DIBH indicated more irradiated lung mass in DIBH than in FB. The mean expansion of the left lung between FB and DIBH was 1.5 ± 2.4 mm (left), 16.0 ± 4.0 mm (anterior) and 12.2 ± 4.6 mm (caudal). No significant correlations were found between expansions and differences in Dmean for the left lung. The heart dose in DIBH was reduced in all patients by 53% (Dmean) and this dosimetric benefit correlated to lung expansion in anterior.ConclusionsTreatment of left-sided breast cancer in DIBH reduced dose to the heart and in most cases the lung dose, relative irradiated lung volume and lung mass. A mass related dosimetric benefit in DIBH can be achieved as long as the volume related benefit is about ≥8–9%. The lung expansion (breathing pattern) showed no impact on lung dose, but on heart dose. A stronger chest breathing (anterior expansion) for DIBH seems to be more beneficial than abdominal breathing.

Dose to mass for evaluation and optimization of lung cancer radiation therapy

PurposeTo evaluate potential organ at risk dose-sparing by using dose-mass-histogram (DMH) objective functions compared with dose-volume-histogram (DVH) objective functions. MethodsTreatment plans were retrospectively optimized for 10 locally advanced non-small cell lung cancer patients based on DVH and DMH objectives. DMH-objectives were the same as DVH objectives, but with mass replacing volume. Plans were normalized to dose to 95% of the PTV volume (PTV-D95v) or mass (PTV-D95m). For a given optimized dose, DVH and DMH were intercompared to ascertain dose-to-volume vs. dose-to-mass differences. Additionally, the optimized doses were intercompared using DVH and DMH metrics to ascertain differences in optimized plans. Mean dose to volume, Dv‾, mean dose to mass, DM‾, and fluence maps were intercompared. ResultsFor a given dose distribution, DVH and DMH differ by >5% in heterogeneous structures. In homogeneous structures including heart and spinal cord, DVH and DMH are nearly equivalent. At fixed PTV-D95v, DMH-optimization did not significantly reduce dose to OARs but reduced PTV-Dv‾ by 0.20±0.2Gy (p=0.02) and PTV-DM‾ by 0.23±0.3Gy (p=0.02). Plans normalized to PTV-D95m also result in minor PTV dose reductions and esophageal dose sparing (Dv‾ reduced 0.45±0.5Gy, p=0.02 and DM‾ reduced 0.44±0.5Gy, p=0.02) compared to DVH-optimized plans. Optimized fluence map comparisons indicate that DMH optimization reduces dose in the periphery of lung PTVs. ConclusionsDVH- and DMH-dose indices differ by >5% in lung and lung target volumes for fixed dose distributions, but optimizing DMH did not reduce dose to OARs. The primary difference observed in DVH- and DMH-optimized plans were variations in fluence to the periphery of lung target PTVs, where low density lung surrounds tumor.

About the non-consistency of PTV-based prescription in lung

PurposeThe goal of this study is to show that the PTV concept is inconsistent for prescribing lung treatments when using type B algorithms, which take into account lateral electron transport. It is well known that type A dose calculation algorithms are not capable of calculating dose in lung correctly. Dose calculations should be based on type B algorithms. However, the combination of a type B algorithm with the PTV concept leads to prescription inconsistencies. MethodsA spherical isocentric setup has been simulated, using multiple realistic values for lung density, tumor density and collimator size. Different prescription methods are investigated using Dose-Volume-Histograms (DVH), Dose-Mass-Histograms (DMH), generalized Equivalent Uniform Dose (gEUD) and surrounding isodose percentage. ResultsIsodose percentages on the PTV drop down to 50% for small tumors and low lung density. When applying the same PTV prescription to different patients with different lung characteristics, the effective mean dose to the GTV is very different, with factors up to 1.4. The most consistent prescription method seems to be the D50%DMH (PTV) DMH point, but is also limited to tumors with size over 1cm. ConclusionsEven when using the different prescription methods, the prescription to the PTV is not consistent for type B-algorithm based dose calculations if clinical studies should produce coherent data. This combination leads to patients’ GTV with low lung density possibly receiving very high dose compared to patients with higher lung density. The only solution seems to remove the classical PTV concept for type B dose calculations in lung.

SU-G-BRC-08: Evaluation of Dose Mass Histogram as a More Representative Dose Description Method Than Dose Volume Histogram in Lung Cancer Patients

Purpose: Dose-volume-histogram (DVH) is widely used for plan evaluation in radiation treatment. The concept of dose-mass-histogram (DMH) is expected to provide a more representative description as it accounts for heterogeneity in tissue density. This study is intended to assess the difference between DVH and DMH for evaluating treatment planning quality. Methods: 12 lung cancer treatment plans were exported from the treatment planning system. DVHs for the planning target volume (PTV), the normal lung and other structures of interest were calculated. DMHs were calculated in a similar way as DVHs expect that the voxel density converted from the CT number was used in tallying the dose histogram bins. The equivalent uniform dose (EUD) was calculated based on voxel volume and mass, respectively. The normal tissue complication probability (NTCP) in relation to the EUD was calculated for the normal lung to provide quantitative comparison of DVHs and DMHs for evaluating the radiobiological effect. Results: Large differences were observed between DVHs and DMHs for lungs and PTVs. For PTVs with dense tumor cores, DMHs are higher than DVHs due to larger mass weighing in the high dose conformal core regions. For the normal lungs, DMHs can either be higher or lower than DVHs depending on the target location within the lung. When the target is close to the lower lung, DMHs show higher values than DVHs because the lower lung has higher density than the central portion or the upper lung. DMHs are lower than DVHs for targets in the upper lung. The calculated NTCPs showed a large range of difference between DVHs and DMHs. Conclusion: The heterogeneity of lung can be well considered using DMH for evaluating target coverage and normal lung pneumonitis. Further studies are warranted to quantify the benefits of DMH over DVH for plan quality evaluation.

SU‐F‐T‐454: Dose‐Mass‐Histogram Sensitivity to Anatomical Changes During Radiotherapy for HNSCC

Purpose:To determine the sensitivity of dose‐mass‐histogram (DMH) due to anatomical changes of head‐and‐neck squamous cell carcinoma (HNSCC) radiotherapy (RT).Methods:Eight patients undergoing RT treatment for HNSCC were scanned during the third and sixth week of RT. These second (CT2) and third (CT3) CTs were co‐registered to the planning CT (CT1). Contours were propagated via deformable registration from CT1 and doses were re‐calculated. DMHs were extracted for each CT set. DMH sensitivity was assessed by dose‐mass indices (DMIs), which represent the dose delivered to a certain mass of and anatomical structure. DMIs included: dose to 98%, 95% and 2% of the target masses (PTV1, PTV2, and PTV3) and organs‐at‐risk (OARs): cord DMI2%, brainstem DMI2%, left_ and right_parotid DMI2% and DMI50%, and mandible DMI2%. A two‐tailed paired t‐test was used to compare changes to DMIs in CT2 and CT3 with respect to CT1 (CT2/CT1 and CT3/CT1).Results:Changes to DMHs were found for all OARs and PTVs, but they were significant only for the PTVs. Maximum dose to PTVs increased significantly for CT2/CT1 in all three PTVs, but CT3/CT1 changes were only significantly different for PTV1 and PTV2. Dose coverage to the three PTVs was also significantly different, DMI98% was lower for both CT2/CT1 and CT3/CT1. DMI95% was significantly lower for PTV1 for CT2/CT1, PTV2 for CT2/CT1 and CT3/CT1, and PTV3 for CT3/CT1.Conclusion:Changes in anatomy significantly change dose‐mass coverage for the planning targets, making it necessary to re‐plan in order to maintain the therapeutic goals. Maximum dose to the PTVs increase significantly as RT progresses, which may not be problematic as long as the high dose remains in the gross tumor volume. Doses to OARs were minimally affected and the differences were not significant.

A dose error evaluation study for 4D dose calculations

Previous studies have shown that respiration induced motion is not negligible for Stereotactic Body Radiation Therapy. The intrafractional breathing induced motion influences the delivered dose distribution on the underlying patient geometry such as the lung or the abdomen. If a static geometry is used, a planning process for these indications does not represent the entire dynamic process. The quality of a full 4D dose calculation approach depends on the dose coordinate transformation process between deformable geometries. This article provides an evaluation study that introduces an advanced method to verify the quality of numerical dose transformation generated by four different algorithms.The used transformation metric value is based on the deviation of the dose mass histogram (DMH) and the mean dose throughout dose transformation. The study compares the results of four algorithms. In general, two elementary approaches are used: dose mapping and energy transformation. Dose interpolation (DIM) and an advanced concept, so called divergent dose mapping model (dDMM), are used for dose mapping. The algorithms are compared to the basic energy transformation model (bETM) and the energy mass congruent mapping (EMCM). For evaluation 900 small sample regions of interest (ROI) are generated inside an exemplary lung geometry (4DCT). A homogeneous fluence distribution is assumed for dose calculation inside the ROIs. The dose transformations are performed with the four different algorithms.The study investigates the DMH-metric and the mean dose metric for different scenarios (voxel sizes: 8 mm, 4 mm, 2 mm, 1 mm; 9 different breathing phases). dDMM achieves the best transformation accuracy in all measured test cases with 3–5% lower errors than the other models. The results of dDMM are reasonable and most efficient in this study, although the model is simple and easy to implement. The EMCM model also achieved suitable results, but the approach requires a more complex programming structure. The study discloses disadvantages for the bETM and for the DIM. DIM yielded insufficient results for large voxel sizes, while bETM is prone to errors for small voxel sizes.

SU-E-T-518: Effect of Statistic Uncertainty On DVH and Dose Mass Histogram (DMH) Derived Dose Indices (DI) for Head and Neck IMRT-Monte Carlo(MC) Treatments

Purpose: The use of DMH as an alternative method to validate the treatment plans of the head and neck region has been investigated in the past[1].For patient geometry contains large air cavities,high statistical uncertainties were found in dose distributions calculated using MC method in air cavities and tissues that surround air cavities.These previous studies reported that the shape of DMH is less affected by the statistical uncertainty than DVH. But the authors did not verify the differences between DIs derived from DVH and DMH.In the present work we compare the DIs derived from DVHs and DMHs based on dose distributions calculated using MC method and different levels of statistical precision. Methods: Six IMRT plans were calculated using the EGS4 based MCSIM code.MCSHOW allows to built DVHs and DMHs from dose distributions calculated with different statistical uncertainty(from 0.5% to 10%). The DIs were derived from the DVHs and DMHs for the target and critical structures (cord, chiasm, larynx,brainstem and parotid). Results: Comparing the values (averaged over 6 patients) of target DMH (0.5%) and DMH (2%) we found that all DIs (D05, D95, Mp, Dmax) do not differ in more than 0.7%. By comparison of target DVH (0.5%) and target DVH (2%) we observed differences of about 14% on the value of Dmax and V95 changes in more than 3%. We verified that the statistical effect is not significant (less than 1%) for the DVH of critical structures. Conclusion: The DIs derived from DMHs are not strong dependent on the statistical uncertainty of the dose values as those derived from DVHs. Our results confirmed the previous idea that DMH is a better parameter than DVH for evaluating treatment plans calculated by MC simulations in low density regions. [1]-G.Mora et al., MCTP 2009,Cardiff 2009.

TU‐G‐BRB‐05: Dose to Mass in Lung Cancer IMRT Optimization

Purpose: To assess the efficacy of intensity modulated radiotherapy (IMRT) optimization for lung cancer based on dose‐to‐structure mass objectives. Methods: Three patient cases are planned for two radiotherapy deliveries; each optimized using either dose‐volume histogram (DVH) constraints or dose‐mass histogram (DMH) constraints. An inhale‐phase optimization simulates breath hold (BH) treatment; an average CT (aCT) optimization simulates treatment under free breathing. DVH plan objectives include 70 Gy to 98% of the PTV while minimizing lung V20, esophagus V25, heart V30. DMH optimization utilizes the same beam angles with corresponding objectives; 70 Gy to 98% of the PTV mass and minimum dose to relative mass of risk structures. Relative mass at objective dose levels are compared for DVH and DMH optimization. Results: For BH plans, DMH optimization maintains or improves target‐mass coverage (up to 2% increase in mass at 70 Gy) and decreases lung mass by up to 2%, heart mass by up to 4%, and esophagus mass by up to 2% at the objective dose levels. The DMH plans optimized on aCT increase target mass coverage by <:1% in all cases and do not improve risk structure mass sparing compared to DVH based plans. The DVH to DMH optimization differences are patient and radiation path dependent. For a complete set of voxels composing a given volume, irradiated voxels are a subset which can have a different mean density than the entire set. This leads to variations in DVH and DMH solutions for the PTV, lungs, esophagus, and heart. Preferred (reduced) radiation path‐lengths through regions of lower density lung are observed in BH‐DMH optimization. Conclusions: DMH optimization has the potential to improve the therapeutic ratio on well‐defined geometry (for example during BH‐RT). This may have advantages in the heart, however, functional lung sub‐units must be further investigated. Support: P01CA11602 and Philips Medical Systems

SU-E-T-572: Dose Mass Histogram (DMH) versus Dose Volume Histogram (DVH) for SBRT and Craniospinal Patients: What Can We Learn?

Purpose: Dose volume histogram (DVH) has been a valuable tool for plan evaluation, but it provides limited information for dose assessment of irradiated tumors and organs, especially for those with significant density heterogeneities such as lungs. DVH is also at odds with the fact that radiationdose is defined as energy deposited in a unit mass of medium irradiated. In this study, dose mass histograms (DMH) were used to evaluate plans from patients treated with stereotactic body radiotherapy(SBRT) and craniospinal axis irradiation (CSAI). Methods: Eight patients were enrolled in the study, of which four of them were treated with SBRT using a BrainLAB system and the other half were CSAI patients treated with a Helical TomoTherapy unit. DMHs for these patients were computed based on the treatment plan's DVHs by weighting each voxel volume according to its density as derived from the CT number vs. density table used in the treatment planning system. All eight plans were exported to a personal computer for DMH computation and analysis. Comparison between each patient's DVH and DMH for planning target volume (PTV) and organs at risk (OAR) were performed. Results: DMH was generated for the PTV and OARs. No significant difference between DMH and DVH was observed for PTV and OARs (heart, kidneys, spinal cord) except for lungs.Lung DMHs from SBRT patients indicated potential room of dose escalation while those from CSAI patients showed the opposite. Conclusions: Dose mass histogram serves as an important tool for evaluation of treatment plans for organs with variable densities. For organs with uniform density distribution, DMHs were not significantly different than DVHs, The potential usefulness of DMH for plan evaluation for organs of heterogeneous density distributions warrants further study.

SU‐E‐T‐848: Dose Mass — Based IMRT Inverse Planning for Radiotherapy of Thoracic Cancer

Purpose: The heterogeneity of lung tissue compromises the therapeutic quality of conventional dose‐volume based intensity modulated radiation therapy (IMRT) treatment planning, particularly when the lung volume varies with patientˈs respiration. An innovative inverse planning strategy was developed based on the concept of dose mass constraint as a more physical descriptor of dose delivered to lung. Methods: To better represent the actual lung cells being damaged by ionizing radiation, lung mass rather than lung volume was introduced in the inverse planning process. The conversion from volume of each voxel to mass was achieved with its local density, which was obtained from the computed tomography (CT or Hounsfield) number as a function of density relationship that is used by the treatment planning system. The dose mass constraints for targets and critical structures were integrated into the optimization cost function. For instance, a mass constraint would limit the dose to 20% of the lung mass to 20 Gy or less(coined as M20). The dose‐mass based inverse planning was applied on six thoracic cancer patients. To assess the dosimetric impact, comparisons were made between the dose mass histograms (DMH) and the dose volume histograms (DVH) and resulting dose distributions. Results: While the same planning target volume was covered, dose to lung from the DMH based plans were about 2% lower than DVH based plans. Lower doses could have important implications in terms of therapeutic effects. Conclusions: Dose mass‐based IMRT inverse planning framework was successfully implemented to take into account tissue density variations during plan optimization. Preliminary results suggest that gains in terms of lower dose to healthy lung are possible.

SU‐GG‐J‐15: Evaluation of Dose Uncertainties Introduced by Dose Mapping Process Implemented in a Commercial 4D Treatment Planning System

Purpose: To evaluate dose uncertainties introduced by dose mapping process implemented in a commercial 4D treatment planning system by evaluating integral dose conservation in common volumes between unmapped and mapped images. Method and Materials: CT images corresponding with the peak‐of‐exhale (TO) and peak‐of‐inhale (T5) from a lung patient are used. Physician drawn regions of interest (ROIs) of the rightlung, GTV, spinal cord and heart on TO are propagated via the surface landmark‐based deformable image registration algorithm built into the commercial software to generate deformation vector fields (DVFs). An IMRT treatment plan on T5 and dose distribution on T5 is calculated, and the DVFT0→T5 is used to map the dose from T5 to TO. Cubic ROIs with volumes of 1, 2, 4 and 8 cm3 are selected on T0 and mapped to T5 using bitmaps to evaluate dose uncertainty as follows: let M0, E0, and M5, E5 be the mass and integral dose within the T0 and T5 ROIs. Dose uncertainty, defined as ΔD = ∂D/∂E × ΔE + ∂D/∂M × ΔM = (E5 − E0)/M5 − (M5 − M0)/M02 × E0, is evaluated for each ROI. The mean dose uncertainty, defined as ΔD= E0/M0 − E5/M5, is also evaluated. The mean dose, DVH and Dose‐Mass‐Histograms (DMHs) for critical organs are also compared. Results: The average dose uncertainties detected are: 5.56%, 5.03%, 4.18% and 3.72% for the 1, 2, 4, and 8 cm3 volumes respectively. The average mean dose uncertainty detected are 3.7%, 3.6%, 3.1% and 3.1%. The largest dose uncertainty points are close to the boundary of tumor. Conclusion: Dose uncertainties introduced by the dose mapping process implemented in a commercial 4D treatment planning system are evaluated. Though the average dose uncertainty is small, some large dose uncertainties are observed. More test cases are needed. Work supported by NIH P01CA116602.

SU-FF-T-513: DVM Vs. DMH in Lung Cancer Radiotherapy

Purpose: To examine whether the Dose Mass Histogram (DMH) concept can be better associated with the expected lung complications than the widely used Dose Volume Histogram (DVH) concept. Materials and Methods: A group of lungcancer patients was used to clinically quantify the difference between DVHs and DMHs. Treatment planning was carried out in the Pinnacle3treatment planning system. The prescribed dose of 50Gy was delivered over a period of 5 weeks at a daily dose of 2Gy. The plans were created using 5 IMRT beams of 6MV photon energy. The DMHs were computed so that the volume of each CT voxel is weighted by its local density. The influence of the deviation between DVHs and DMHs on the clinical endpoint of radiation pneumonitis was estimated. Results: For the group of patients, according to the DVH, the right lung receives 18.9Gy, whereas according to the DMH it receives 24.9Gy (6.0Gy difference). Interpreting these figures in terms of lung response, DVH gives 10.1% probability for lung complications compared to 26.4% given by DMH for the Relative Seriality model. The respective total control probabilities, P B are 93.3% and 87.0%, whereas the corresponding total complication probabilities, P I are 10.1% and 17.5%. Conclusions: The results indicate that the use of IMRT in lungcancerradiotherapy can encompass the often large PTV required while minimizing the volume of the organs at risk receiving high dose. The use of the proper descriptor of the 3D dose distribution is important factor because it is related to the clinical outcome of the applied therapy. The effectiveness of the dose distribution seems to be closer related to the lung complications when using the DMH rather than the DVH concept. Furthermore, the expected lung complications appear to be overestimated when using the DVH concept.

SU‐GG‐T‐429: Dose‐Mass‐Histogram (DMH) Vs. Dose‐Volume Histogram (DVH) in Predicting Lung Complications

Purpose: In radiotherapy for breast cancer, it is very important to estimate the expected lung complications. In this study, it is examined whether the Dose Mass Histogram (DMH) concept can be better associated with the expected lung complications than the widely used Dose Volume Histogram (DVH) concept. Method and Mataerials: The problem was investigated theoretically by applying two hypothetical dose distributions (Gaussian and Semi‐Gaussian shaped) on two lungs of uniform and varying densities. Furthermore, a group of breast cancer patients was used to clinically quantify the difference between DVHs and DMHs. These patients were treated with resection and irradiation with two tangential fields. The influence of the deviation between DVHs and DMHs on the clinical endpoint of radiation pneumonitis is estimated by using the Relative Seriality and LKB models. The biological equivalent of the corresponding difference in their mean doses was estimated by the Biologically Effective Uniform Dose (BEUD) and Equivalent Uniform Dose (EUD) concepts, respectively. Results: The relation of the DVHs and DMHs varies depending on the underlying cell density distribution and the applied dose distribution. The range of their deviation in terms of expected clinical outcome was large both for the theoretical and the clinical studies. For the group of patients, according to the DVH, lung receives 9.82 Gy, whereas according to the DMH it receives 8.06 Gy (1.76 Gy difference). Interpreting these figures in terms of lung response, DVH gives 0.30% probability for lung complications compared to 0.10% given by DMH for the Relative Seriality model. Similarly, for the LKB model the corresponding values are 2.62% and 1.85%, respectively. Conclusion: Concluding, the expected lung complications appear to be overestimated when using the DVH concept, whereas the effectiveness of the dose distribution is closer related to the radiation effects when using the DMH concept.

SU‐GG‐T‐351: Using Dose Mass Histograms (DMH) for the Evaluation of Head and Neck IMRT Plans Calculated by Monte Carlo

Purpose: Dose volume histograms (DVH) represent an important tool for the clinical evaluation of radiotherapy treatment plans. For head and neck regions, however, the presence of air cavities makes the DVH tool less adequate for Monte Carlo calculated IMRT plans. The air cavities may introduce high dose uncertainties in both air cavities and surrounding tissues (interface effects). Since the dose to air is clinically irrelevant DVH becomes less clinically representative in the presence of large air cavities, and hence is no longer a good parameter for plan evaluation. In this work we assess the limitations of DVH for head and neck plan evaluation and investigate the dose mass histogram (DMH) as an alternative to overcome those limitations. Method and Materials: Seven IMRT head and neck cases were included in this study. The Monte Carlo simulation geometry (2mm voxels) was built from patient CT data. Patient dose calculations were performed using the EGS4 based MCSIM code and photon source models for 6 and 10MV beams. Isodose distributions and DVH (including air) were generated for these plans. A new feature was created in order to calculate DMH for both the target (PTV) and critical structures. In addition, DVH excluding air were also generated. Results: Our results for the 7 patients show significant differences (up to 10%) between the DVH calculated including and excluding air for the PTV and critical structures. DMH, on the other hand, eliminates the effect of large Monte Carlo statistical uncertainties in air cavities and is a better parameter than DVH for evaluating head and neck IMRT treatment plans. Conclusion: DMH is a better parameter than DVH for evaluating treatment plans calculated by Monte Carlo simulations that may have large statistical uncertainties in low‐density regions such as air cavities and lung tissues.

The impact of photon dose calculation algorithms on expected dose distributions in lungs under different respiratory phases

A planning study was carried out on a cohort of CT datasets from breast patients scanned during different respiratory phases. The aim of the study was to investigate the influence of different air filling in lungs on the calculation accuracy of photon dose algorithms and to identify potential patterns of failure with clinical implications. Selected respiratory phases were free breathing (FB), representative of typical end expiration, and deep inspiration breath hold (DIBH), a typical condition for clinical treatment with respiratory gating. Algorithms investigated were the pencil beam (PBC), the anisotropic analytical algorithm (AAA) and the collapsed cone (CC) from the Varian Eclipse or Philips Pinnacle planning system. Reference benchmark calculations were performed with the Voxel Monte Carlo (VMC++). An analysis was performed in terms of physical quantities inspecting either dose–volume or dose–mass histograms and in terms of an extension to three dimensions of the γ index of Low. Results were stratified according to a breathing phase and algorithm. Collectives acquired in FB or DIBH showed well-separated average lung density distributions with mean densities of 0.27 ± 0.04 and 0.16 ± 0.02 g cm−3, respectively, and average peak densities of 0.17 ± 0.03 and 0.09 ± 0.02 g cm−3. Analysis of volume–dose or mass–dose histograms proved the expected deviations on PBC results due to the missing lateral transport of electrons with underestimations in the low dose region and overestimations in the high dose region. From the γ analysis, it resulted that PBC is systematically defective compared to VMC++ over the entire range of lung densities and dose levels with severe violations in both respiratory phases. The fraction of lung voxels with γ > 1 for PBC reached 25% in DIBH and about 15% in FB. CC and AAA performed, in contrast, similarly and with fractions of lung voxels with γ > 1 in average inferior to 2% in FB and 4–5% (AAA) or 6–8% (CC) in DIBH. In summary, PBC proved to be severely defective in calculations involving lungs and particularly for cases where specific respiratory phases (e.g. DIBH) are assumed for treatment. In contrast, CC and AAA manifested a high degree of consistency against the Monte Carlo method and provided stable results over the entire range of clinically relevant densities.

Dose–volume modeling of the risk of postoperative pulmonary complications among esophageal cancer patients treated with concurrent chemoradiotherapy followed by surgery

The aim of this study was to investigate the effect of radiation dose distribution in the lung on the risk of postoperative pulmonary complications among esophageal cancer patients. We analyzed data from 110 patients with esophageal cancer treated with concurrent chemoradiotherapy followed by surgery at our institution from 1998 to 2003. The endpoint for analysis was postsurgical pneumonia or acute respiratory distress syndrome. Dose-volume histograms (DVHs) and dose-mass histograms (DMHs) for the whole lung were used to fit normal-tissue complication probability (NTCP) models, and the quality of fits were compared using bootstrap analysis. Normal-tissue complication probability modeling identified that the risk of postoperative pulmonary complications was most significantly associated with small absolute volumes of lung spared from doses > or = 5 Gy (VS5), that is, exposed to doses < 5 Gy. However, bootstrap analysis found no significant difference between the quality of this model and fits based on other dosimetric parameters, including mean lung dose, effective dose, and relative volume of lung receiving > or = 5 Gy, probably because of correlations among these factors. The choice of DVH vs. DMH or the use of fractionation correction did not significantly affect the results of the NTCP modeling. The parameter values estimated for the Lyman NTCP model were as follows (with 95% confidence intervals in parentheses): n = 1.85 (0.04, infinity), m = 0.55 (0.22, 1.02), and D50 = 17.5 Gy (9.4 Gy, 102 Gy). In this cohort of esophageal cancer patients, several dosimetric parameters including mean lung dose, effective dose, and absolute volume of lung receiving < 5 Gy provided similar descriptions of the risk of postoperative pulmonary complications as a function of the radiation dose distribution in the lung.

SU-FF-T-380: Dose Mass Histogram and Its Application for 4D Treatment Planning

Purpose: In evaluating dose distributions of lung treated during respiration, dose volume histogram (DVH) may not be an appropriate term because of variable lung volume with respiration. The purposes of this work are to investigated the use of dose mass histogram (DMH) for lung and assess the differences between DVH and DMH for conventional and 4D treatment planning. Method and Materials: DMH was calculated based on a similar concept of DVH excepted mass of each voxel, which was calculated from CT number to density conversion, was used in tallying dose distributions. For conventional treatment planning, DVHs and DMHs of normal lung (excluding GTV) were calculated and compared for 51 lung cancer cases and 52 esophagus cancer cases. For 4D treatment planning, ten lung cancer cases were analyzed, each of which had 4DCT scans of full lung and optimal CT image quality. Total normal lung was delineated on CT images of all respiratory phases using auto-thresholding with a single CT number. Dose distributions were computed on all respiratory phases with DVH and DMH being calculated for the lung. Results: For conventional plans, difference between DMH and DVH existed for cases with highly inhomogenuous lung tissues. For a majority of cases, such differences were small and may not be clinically significant. For 4D treatment planning, lung volume changed on average by 16.3% between inspiration and expiration. As expected, variation of lung mass was much smaller, only by about 6.6% as assessed from the 4DCT. The change of lung DMH with breathing was often different from that of lung DVH, indicating that deformation of lung mass followed different patterns than that of the lung volume during breathing. Conclusion: DMH may be more relevant than DVH considering varying alveolar-cell density in lung and conservation of lung mass in 4D treatment planning.

Dosimetric benefits of respiratory gating: a preliminary study

In this study, we compared the amount of lung tissue irradiated when respiratory gating was imposed during expiration with the amount of lung tissue irradiated when gating was imposed during inspiration. Our hypothesis was that the amount of lung tissue spared increased as inspiration increased. Computed tomography (CT) image data sets were acquired for 10 patients diagnosed with primary bronchogenic carcinoma. Data sets were acquired during free breathing, during breath‐holds at 0% tidal volume and 100% tidal volume, and, when possible, at deep inspiration corresponding to approximately 60% vital capacity. Two treatment plans were developed on the basis of each of the gated data sets: one in which the treatment portals were those of the free‐breathing plan, and the other in which the treatment portals were based on the gated planning target volumes. Dose‐mass histograms of the lungs calculated at 0% tidal volume were compared to those calculated at deep inspiration and at 100% tidal volume. Data extracted from the dose‐mass histograms were used to determine the most dosimetrically beneficial point to gate, the reduction in the amount of irradiated lung tissue that resulted from gating, and any disease characteristics that might predict a greater need for gating. The data showed a reduction in the mass of normal tissue irradiated when treatment portals based on the gated planning target volume were used. More normal lung tissue was spared at deep inspiration than at the other two gating points for all patients, but normal lung tissue was spared at every point in the respiratory cycle. No significant differences in the amount of irradiated tissue by disease characteristic were identified. Respiratory gating of thoracic radiation treatments can often improve the quality of the treatment plan, but it may not be possible to determine which patients may benefit from gating prior to performing the actual treatment planning.PACS numbers 87.53 –j; 87.53.Tf

Dosimetric benefits of respiratory gating: a preliminary study

In this study, we compared the amount of lung tissue irradiated when respiratory gating was imposed during expiration with the amount of lung tissue irradiated when gating was imposed during inspiration. Our hypothesis was that the amount of lung tissue spared increased as inspiration increased. Computed tomography (CT) image data sets were acquired for 10 patients who had been diagnosed with primary bronchogenic carcinoma. Data sets were acquired during free breathing and during breath-holds at 0% tidal volume and 100% tidal volume, and, when possible, at deep inspiration, corresponding to approximately 60% vital capacity. Two treatment plans were developed on the basis of each of the gated data sets: one in which the treatment portals were those of the free-breathing plan, and the other in which the treatment portals were based on the gated planning target volumes. Dose-mass histograms of the lungs calculated at 0% tidal volume were compared to those calculated at deep inspiration and at 100% tidal volume. Data extracted from the dose-mass histograms were used to determine the most dosimetrically beneficial point to gate, the reduction in the amount of irradiated lung tissue that resulted from gating, and any disease characteristics that might predict a greater need for gating. The data showed a reduction in the mass of normal tissue irradiated when treatment portals based on the gated planning target volume were used. More normal lung tissue was spared at deep inspiration than at the other two gating points for all patients, but normal lung tissue was spared at every point in the respiratory cycle. No significant differences in the amount of irradiated tissue by disease characteristic were identified. Respiratory gating of thoracic radiation treatments can often improve the quality of the treatment plan, but it may not be possible to determine which patients may benefit from gating prior to performing the actual treatment planning.