Large-scale Retrospective Monte Carlo Dosimetric Study for Permanent Implant Prostate Brachytherapy

AAPM Task Group Report 267: A joint AAPM GEC-ESTRO report on biophysical models and tools for the planning and evaluation of brachytherapy

Brachytherapy utilizes a multitude of radioactive sources and treatment techniques that often exhibit widely different spatial and temporal dose delivery patterns. Biophysical models, capable of modeling the key interacting effects of dose delivery patterns with the underlying cellular processes of the irradiated tissues, can be a potentially useful tool for elucidating the radiobiological effects of complex brachytherapy dose delivery patterns and for comparing their relative clinical effectiveness. While the biophysical models have been used largely in research settings by experts, it has also been used increasingly by clinical medical physicists over the last two decades. A good understanding of the potentials and limitations of the biophysical models and their intended use is critically important in the widespread use of these models. To facilitate meaningful and consistent use of biophysical models in brachytherapy, Task Group 267 (TG-267) was formed jointly with the American Association of Physics in Medicine (AAPM) and The Groupe Européen de Curiethérapie and the European Society for Radiotherapy & Oncology (GEC-ESTRO) to review the existing biophysical models, model parameters, and their use in selected brachytherapy modalities and to develop practice guidelines for clinical medical physicists regarding the selection, use, and interpretation of biophysical models. The report provides an overview of the clinical background and the rationale for the development of biophysical models in radiation oncology and, particularly, in brachytherapy; a summary of the results of literature review of the existing biophysical models that have been used in brachytherapy; a focused discussion of the applications of relevant biophysical models for five selected brachytherapy modalities; and the task group recommendations on the use, reporting, and implementation of biophysical models for brachytherapy treatment planning and evaluation. The report concludes with discussions on the challenges and opportunities in using biophysical models for brachytherapy and with an outlook for future developments.

An end-to-end deep convolutional neural network-based dose engine for parotid gland cancer seed implant brachytherapy

BACKGROUND

Seed implant brachytherapy (SIBT) is a promising treatment modality for parotid gland cancers (PGCs). However, the current clinical standard dose calculation method based on the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43) Report oversimplifies patient anatomy as a homogeneous water phantom medium, leading to significant dose calculation errors due to heterogeneity surrounding the parotid gland. Monte Carlo Simulation (MCS) can yield accurate dose distributions but the long computation time hinders its wide application in clinical practice.

PURPOSE

This paper aims to develop an end-to-end deep convolutional neural network-based dose engine (DCNN-DE) to achieve fast and accurate dose calculation for PGC SIBT.

METHODS

A DCNN model was trained using the patient's CT images and TG-43-based dose maps as inputs, with the corresponding MCS-based dose maps as the ground truth. The DCNN model was enhanced based on our previously proposed model by incorporating attention gates (AGs) and large kernel convolutions. Training and evaluation of the model were performed using a dataset comprising 188 PGC I-125 SIBT patient cases, and its transferability was tested on an additional 16 non-PGC head and neck cancers (HNCs) I-125 SIBT patient cases. Comparison studies were conducted to validate the superiority of the enhanced model over the original one and compare their overall performance.

RESULTS

On the PGC testing dataset, the DCNN-DE demonstrated the ability to generate accurate dose maps, with percentage absolute errors (PAEs) of 0.67% ± 0.47% for clinical target volume (CTV) D90 and 1.04% ± 1.33% for skin D0.1cc. The comparison studies revealed that incorporating AGs and large kernel convolutions resulted in 8.2% (p < 0.001) and 3.1% (p < 0.001) accuracy improvement, respectively, as measured by dose mean absolute error. On the non-PGC HNC dataset, the DCNN-DE exhibited good transferability, achieving a CTV D90 PAE of 1.88% ± 1.73%. The DCNN-DE can generate a dose map in less than 10 ms.

CONCLUSIONS

We have developed and validated an end-to-end DCNN-DE for PGC SIBT. The proposed DCNN-DE enables fast and accurate dose calculation, making it suitable for application in the plan optimization and evaluation process of PGC SIBT.

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy

Objective.In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability.Approach.Data from 200 prostate patients, treated with125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input data. The dataset was randomly split into training (150), validation (25) and test (25) sets. The aleatoric (associated with the input data) and epistemic (associated with the model) uncertainties of the DL models were assessed.Main results.For the full test set, with respect to the MC reference, the predicted prostateD90metric had mean differences of -0.64% and 0.08% for the UNet and ResUNet TSE models, respectively. In voxel-by-voxel comparisons, the average global dose difference ratio in the [-1%, 1%] range included 91.0% and 93.0% of voxels for the UNet and the ResUNet TSE, respectively. One forward pass or prediction took 4 ms for a 3D dose volume of 2.56 M voxels (128 × 160 × 128). The ResUNet TSE model closely encoded the well-known physics of the problem as seen in a set of uncertainty maps. The ResUNet TSE rectum D2cchad the largest uncertainty metric of 0.0042.Significance.The proposed DL models serve as rapid dose predictors that consider the patient anatomy and interseed attenuation effects. The derived uncertainty is interpretable, highlighting areas where DL models may struggle to provide accurate estimations. The uncertainty analysis offers a comprehensive evaluation tool for dose predictor model assessment.

A Monte Carlo dose recalculation pipeline for durable datasets: an I-125 LDR prostate brachytherapy use case

Monte Carlo (MC) dose datasets are valuable for large-scale dosimetric studies. This work aims to build and validate a DICOM-compliant automated MC dose recalculation pipeline with an application to the production of I-125 low dose-rate prostate brachytherapy MC datasets. Built as a self-contained application, the recalculation pipeline ingested clinical DICOM-RT studies, reproduced the treatment into the Monte Carlo simulation, and outputted a traceable and durable dose distribution in the DICOM dose format. MC simulations with TG43-equivalent conditions using both TOPAS andegs_brachyMC codes were compared to TG43 calculations to validate the pipeline. The consistency of the pipeline when generating TG186 simulations was measured by comparing simulations made with both MC codes. Finally,egs_brachysimulations were run on a 240-patient cohort to simulate a large-scale application of the pipeline. Compared to line source TG43 calculations, simulations with both MC codes had more than 90% of voxels with a global difference under ±1%. Differences of 2.1% and less were seen in dosimetric indices when comparing TG186 simulations from both MC codes. The large-scale comparison ofegs_brachysimulations with treatment planning system dose calculation seen the same dose overestimation of TG43 calculations showed in previous studies. The MC dose recalculation pipeline built and validated against TG43 calculations in this work efficiently produced durable MC dose datasets. Since the dataset could reproduce previous dosimetric studies within 15 h at a rate of 20 cases per 25 min, the pipeline is a promising tool for future large-scale dosimetric studies.

Haralick texture feature analysis for characterization of specific energy and absorbed dose distributions across cellular to patient length scales

Objective.To investigate an approach for quantitative characterization of the spatial distribution of dosimetric data by introducing Haralick texture feature analysis in this context.Approach.Monte Carlo simulations are used to generate 3D arrays of dosimetric data for 2 scenarios: (1) cell-scale microdosimetry: specific energy (energy imparted per unit mass) in cell-scale targets irradiated by photon spectra (¹²⁵I,¹⁹²Ir, 6 MV); (2) tumour-scale dosimetry: absorbed dose in voxels for idealized models of¹²⁵I permanent implant prostate brachytherapy, considering 'TG186' (realistic tissues including 0% to 5% intraprostatic calcifications; interseed attenuation) and 'TG43' (water model, no interseed attenuation) conditions. Five prominent Haralick features (homogeneity, contrast, correlation, local homogeneity, entropy) are computed and trends are interpreted using fundamental radiation physics.Main results.In the cell-scale scenario, the Haralick measures quantify differences in 3D specific energy distributions due to source spectra. For example, contrast and entropy are highest for¹²⁵I reflecting the large variations in specific energy in adjacent voxels (photoelectric interactions; relatively short range of electrons), while 6 MV has the highest homogeneity with smaller variations in specific energy between voxels (Compton scattering dominates; longer range of electrons). For the tumour-scale scenario, the Haralick measures quantify differences due to TG186/TG43 simulation conditions and the presence of calcifications. For example, as calcifications increase from 0% to 5%, contrast increases while correlation decreases, reflecting the large differences in absorbed dose in adjacent voxels (higher absorbed dose in voxels with calcification due to photoelectric interactions).Significance.Haralick texture analysis provides a quantitative method for the characterization of 3D dosimetric distributions across cellular to tumour length scales, with promising future applications including analyses of multiscale tissue models, patient-specific data, and comparison of treatment approaches.

Evaluation of dosimetric effects of metallic artifact reduction and tissue assignment on Monte Carlo dose calculations for 125 I prostate implants

PURPOSE

Monte Carlo (MC) simulation studies, aimed at evaluating the magnitude of tissue heterogeneity in ¹²⁵ I prostate permanent seed implant brachytherapy (BT), customarily use clinical post-implant CT images to generate a virtual representation of a realistic patient model (virtual patient model). Metallic artifact reduction (MAR) techniques and tissue assignment schemes (TAS) are implemented on the post-implant CT images to mollify metallic artifacts due to BT seeds and to assign tissue types to the voxels corresponding to the bright seed spots and streaking artifacts, respectively. The objective of this study is to assess the combined influence of MAR and TAS on MC absorbed dose calculations in post-implant CT-based phantoms. The virtual patient models used for ¹²⁵ I prostate implant MC absorbed dose calculations in this study are derived from the CT images of an external radiotherapy prostate patient without BT seeds and prostatic calcifications, thus averting the need to implement MAR and TAS.

METHODS

The geometry of the IsoSeed I25.S17plus source is validated by comparing the MC calculated results of the TG-43 parameters for the line source approximation with the TG-43U1S2 consensus data. Four MC absorbed dose calculations are performed in two virtual patient models using the egs_brachy MC code: (1) TG-43-based D_{w,w-TG 43} , (2) D_w,w-MBDC that accounts for interseed scattering and attenuation (ISA), (3) D_m,m that examines ISA and tissue heterogeneity by scoring absorbed dose in tissue, and (4) D_w,m that unlike D_m,m scores absorbed dose in water. The MC absorbed doses (1) and (2) are simulated in a TG-43 patient phantom derived by assigning the densities of every voxel to 1.00 g cm^-3 (water), whereas MC absorbed doses (3) and (4) are scored in the TG-186 patient phantom generated by mapping the mass density of each voxel to tissue according to a CT calibration curve. The MC absorbed doses calculated in this study are compared with VariSeed v8.0 calculated absorbed doses. To evaluate the dosimetric effect of MAR and TAS, the MC absorbed doses of this work (independent of MAR and TAS) are compared to the MC absorbed doses of different ¹²⁵ I source models from previous studies that were calculated with different MC codes using post-implant CT-based phantoms generated by implementing MAR and TAS on post-implant CT images.

RESULTS

The very good agreement of TG-43 parameters of this study and the published consensus data within 3% validates the geometry of the IsoSeed I25.S17plus source. For the clinical studies, the TG-43-based calculations show a D₉₀ overestimation of more than 4% compared to the more realistic MC methods due to ISA and tissue composition. The results of this work generally show few discrepancies with the post-implant CT-based dosimetry studies with respect to the D₉₀ absorbed dose metric parameter. These discrepancies are mainly Type B uncertainties due to the different ¹²⁵ I source models and MC codes.

CONCLUSIONS

The implementation of MAR and TAS on post-implant CT images have no dosimetric effect on the ¹²⁵ I prostate MC absorbed dose calculation in post-implant CT-based phantoms.

Fast beta-emitter Monte Carlo simulations and full patient dose calculations of targeted radionuclide therapy: introducing egs_mird

BACKGROUND

Targeted Radionuclide Therapy (TRT) is a fast-growing field garnering much interest, with several clinical trials currently underway, that has a steady increase in development of treatment techniques. Unfortunately, within the field and many clinical trials, the dosimetry calculation techniques used remain relatively simple, often using a mix of S-value calculations and kernel convolutions.

PURPOSE

The common TRT calculation techniques, although very quick, can often ignore important aspects of patient anatomy and radionuclide distribution, as well as the interplay there-in. This paper introduces egs_mird, a new Monte Carlo (MC) application built in EGSnrc which allows users to model full patient tissue and density (using clinical CT images) and radionuclide distribution (using clinical PET images) for fast and detailed dose TRT calculation.

METHODS

The novel application egs_mird is introduced along with a general outline of the structure of egs_mird simulations. The general structure of the code, and the track-length estimator scoring implementation for variance reduction, is described. A new egs++ source class egs_internal_source, created to allow detailed patient-wide source distribution, and a modified version of egs_radionuclide_source, changed to be able to work with egs_internal_source, are also described. The new code is compared to other MC calculations of S-values kernels of ¹³¹ I, ⁹⁰ Y, and ¹⁷⁷ Lu in the literature, along with further self-validation using a histogram variant of the electron Fano test. Several full patient prostate ¹⁷⁷ Lu TRT prostate cancer treatment simulations are performed using a single set of patient DICOM CT and [¹⁸ F]-DCFPyL PET data.

RESULTS

Good agreement is found between S-value kernels calculated using egs_mird with egs_internal_source and those found in the literature. Calculating 1000 doses (individual voxel uncertainties of ∼0.05%) in a voxel grid Fano test for monoenergetic 500 keV electrons and ¹⁷⁷ Lu electrons results in 94 and 99% of the doses being within 0.1% of the expected dose, respectively. For a hypothetical ¹⁷⁷ Lu treatment, patient prostate, rectum, bone marrow, and bladder dose volume histograms (DVHs) results did not vary significantly when using the track-length estimator and not modelling electron transport, modelling bone marrow explicitly (rather than using generic tissue compositions), and reducing activity to voxels containing partial or full calcifications to half or none, respectively. Dose profiles through different regions demonstrate there are some differences with model choices not seen in the DVH. Simulations using the track-length estimator can be completed in under 15 minutes (∼30 minutes when using standard interaction scoring).

CONCLUSION

This work shows egs_mird to be a reliable MC code for computing TRT doses as realistically as the patient CT and PET data allow. Furthermore, the code can compute doses to sub-1% uncertainty within 15 minutes, with little to no optimization. Thus, this work supports the use of egs_mird for dose calculations in TRT. This article is protected by copyright. All rights reserved.

Monte Carlo characterization of high atomic number inorganic scintillators for in vivo dosimetry in 192 Ir brachytherapy

BACKGROUND

There is increased interest in vivo dosimetry for ¹⁹² Ir brachytherapy (BT) treatments using high atomic number (Z) inorganic scintillators. Their high light output enables construction of small detectors with negligible stem effect and simple readout electronics. Experimental determination of absorbed-dose energy dependence of detectors relative to water is prevalent, but it can be prone to high detector positioning uncertainties and does not allow for decoupling of absorbed-dose energy dependence from other factors affecting detector response.

PURPOSE

To investigate which measurement conditions and detector properties could affect their absorbed-dose energy dependence in BT in vivo dosimetry.

METHODS

We used a general-purpose MC code penelope for the characterization of high-Z inorganic scintillators with the focus on ZnSe (Z¯=32). Two other promising media CsI (Z¯=54) and Al₂ O₃ (Z¯=11) were included for comparison in selected scenarios. We determined absorbed-dose energy dependence of crystals relative to water under different scatter conditions (calibration phantom 12 × 12 × 30 cm³ , characterization phantoms 20 × 20 × 20 cm³ , 30 × 30 × 30 cm³ , 40 × 40 × 40 cm³ , and patient-like elliptic phantom 40 × 30 × 25 cm³ ). To mimic irradiation conditions during prostate treatments, we evaluated whether the presence of pelvic bones and calcifications affect ZnSe response. ZnSe detector design influence was also investigated.

RESULTS

In contrast to low-Z organic and medium-Z inorganic scintillators, ZnSe and CsI media have substantially greater absorbed-dose energy dependence relative to water. The response was phantom-size dependent and changed by 11 % between limited- and full-scatter conditions for ZnSe, but not for Al₂ O₃ . For a given phantom size, a part of the absorbed-dose energy dependence of ZnSe is caused not due to in-phantom scatter but due to source anisotropy. Thus, the absorbed-dose energy dependence of high-Z scintillators is a function of not only the radial distance but also the polar angle. Pelvic bones did not affect ZnSe response, whereas large and intermediate size calcifications reduced it by 9 % and 5 %, respectively, when placed midway between the source and the detector.

CONCLUSIONS

Unlike currently prevalent low- and medium-Z scintillators, high-Z crystals are sensitive to characterization and in vivo measurement conditions. However, good agreement between MC data for ZnSe in the present study and experimental data for ZnSe:O by Jørgensen et al (2021) suggest that detector signal is proportional to the average absorbed dose to the detector cavity. This enables an easy correction for non-TG43-like scenarios (e.g., patient sizes and calcifications) through MC simulations. Information that should be provided to the clinic by the detector vendors. This article is protected by copyright. All rights reserved.

Intraprostatic calcification and biochemical recurrence in men treated with cesium-131 prostate brachytherapy

PURPOSE

Baseline intraprostatic calcification (IC) has been shown to be associated with a higher rate of biochemical recurrence (BCR) in men treated with iodine-125 prostate brachytherapy (PB). We evaluated this association in a cohort of men treated with cesium-131 PB.

METHODS AND MATERIALS

We retrospectively reviewed the charts of all low- and intermediate-risk prostate cancer patients treated with cesium-131 PB +/- external beam radiotherapy (EBRT) at our institution from 2/2011 to 7/2018. Patients with < 24 months of follow up or those who received androgen deprivation therapy were excluded. Baseline IC status (defined as one or more ICs ≥ 5 mm) was determined on post-PB CT scans. Cox analysis was used to assess predictors of BCR and Kaplan-Meier survival curves were calculated.

RESULTS

Two hundred and sixteen low- and intermediate-risk prostate cancer patients treated with cesium-131 PB +/- EBRT were included. Median follow up was 56.9 months (range 24.1-111.4). Overall, 76 (35.2%) patients had baseline IC and 140 (64.8%) did not. Baseline disease characteristics did not differ significantly between groups. On univariate Cox analysis, only risk group (p = 0.047) and initial PSA (p = 0.016) were significant predictors of BCR, whereas baseline IC was not (p = 0.11). The 5-year BCR-free survival in patients with versus without baseline IC was 97.7% versus 93.8% (p = 0.405), respectively.

CONCLUSIONS

In a cohort of low- and intermediate-risk prostate cancer patients treated with cesium-131 PB, the rate of BCR in men with baseline IC was low and baseline IC was not associated with a higher risk of BCR.

Dosimetric and radiobiological investigation of permanent implant prostate brachytherapy based on Monte Carlo calculations

PURPOSE

Permanent implant prostate brachytherapy plays an important role in prostate cancer treatment, but dose evaluations typically follow the water-based TG-43 formalism, ignoring patient anatomy and interseed attenuation. The purpose of this study is to investigate advanced TG-186 model-based dose calculations via retrospective dosimetric and radiobiological analysis for a new patient cohort.

METHODS AND MATERIALS

A cohort of 155 patients treated with permanent implant prostate brachytherapy from The Ottawa Hospital Cancer Centre is considered. Monte Carlo (MC) dose calculations are performed using tissue-based virtual patient models. Dose-volume histogram (DVH) metrics (target, organs at risk) are extracted from 3D dose distributions and compared with those from calculations under TG-43 assumptions (TG43). Equivalent uniform biologically effective dose and tumor control probability are calculated.

RESULTS

For the target, D₉₀ (V₁₀₀) is 136.7 ± 20.6 Gy (85.8% ± 7.8%) for TG43 and 132.8 ± 20.1 Gy (84.1% ± 8.2%) for MC; D₉₀ is 3.0% ± 1.1% lower for MC than TG43. For organs at risk, MC D_1cc = 104.4 ± 27.4 Gy (TG43: 106.3 ± 28.3 Gy) for rectum and 80.8 ± 29.7 Gy (TG43: 78.4 ± 28.4 Gy) for bladder; D_1cc = 185.9 ± 30.2 Gy (TG43: 191.1 ± 32.0 Gy) for urethra. Equivalent uniform biologically effective dose and tumor control probability are generally lower when evaluated using MC doses. The largest dosimetric and radiobiological discrepancies between TG43 and MC are for patients with intraprostatic calcifications, for whom there are low doses (cold spots) in the vicinity of calcifications within the target, identified with MC but not TG43.

CONCLUSIONS

DVH metrics and radiobiological indices evaluated with TG43 are systematically inaccurate by upward of several percent compared with MC patient-specific models. Mean cohort DVH metrics and their MC:TG43 variances are sensitive to patient cohort and clinical practice, underlining the importance of further retrospective MC studies toward widespread clinical adoption of advanced model-based dose calculations.

Investigating energy deposition in glandular tissues for mammography using multiscale Monte Carlo simulations

PURPOSE

To investigate energy deposition in glandular tissues of the breast on macro- and microscopic length scales in the context of mammography.

METHODS

Multiscale mammography models of breasts are developed, which include segmented, voxelized macroscopic tissue structure as well as nine regions of interest (ROIs) embedded throughout the breast tissue containing explicitly-modelled cells. Using a 30 kVp Mo/Mo spectrum, Monte Carlo (MC) techniques are used to calculate dose to ∼mm voxels containing glandular and/or adipose tissues, as well as energy deposition on cellular length scales. ROIs consist of at least 1000 mammary epithelial cells and ∼200 adipocytes; specific energy (energy imparted per unit mass; stochastic analogue of the absorbed dose) is calculated within mammary epithelial cell nuclei.

RESULTS

Macroscopic dose distributions within segmented breast tissue demonstrate considerable variation in energy deposition depending on depth and tissue structure. Doses to voxels containing glandular tissue vary between ∼0.1 and ∼4 times the mean glandular dose (MGD, averaged over the entire breast). Considering microscopic length scales, mean specific energies for mammary epithelial cell nuclei are ∼30% higher than the corresponding glandular voxel dose. Additionally, due to the stochastic nature of radiation, there is considerable variation in energy deposition throughout a cell population within a ROI: for a typical glandular voxel dose of 4 mGy, the standard deviation of the specific energy for mammary epithelial cell nuclei is 85% relative to the mean. Thus, for a glandular voxel dose of 4 mGy at the centre of the breast, corresponding mammary epithelial cell nuclei will receive specific energies up to ∼9 mGy (considering the upper end of the 1σ standard deviation of the specific energy), while a ROI located 2 cm closer to the radiation source will receive specific energies up to ∼40 mGy. Energy deposition within mammary epithelial cell nuclei is sensitive to cell model details including cellular elemental compositions and nucleus size, underlining the importance of realistic cellular models.

CONCLUSIONS

There is considerable variation in energy deposition on both macro- and microscopic length scales for mammography, with glandular voxel doses and corresponding cell nuclei specific energies many times higher than the MGD in parts of the breast. These results should be considered for radiation-induced cancer risk evaluation in mammography which has traditionally focused on a single metric such as the MGD.

DVH-Based Inverse Planning Using Monte Carlo Dosimetry for LDR Prostate Brachytherapy

PURPOSE

Inverse planning is an integral part of modern low-dose-rate brachytherapy. Current clinical planning systems do not exploit the total dose information and largely use the American Association of Physicists in Medicine TG-43 dosimetry formalism to ensure clinically acceptable planning times. Thus, suboptimal plans may be derived as a result of TG-43-related dose overestimation and nonconformity with dose distribution requirements. The purpose of this study was to propose an inverse planning approach that can improve planning quality by combining dose-volume information and precision without compromising the overall execution times.

METHODS AND MATERIALS

The dose map was generated by accumulating precomputed Monte Carlo (MC) dose kernels for each candidate source implantation site. The MC computational burden was reduced by using graphics processing unit acceleration, allowing accurate dosimetry calculations to be performed in the intraoperative environment. The proposed dose-volume histogram (DVH) fast simulated annealing optimization algorithm was evaluated using clinical plans that were delivered to 18 patients who underwent low-dose-rate prostate brachytherapy.

RESULTS

Our method generated plans in 37.5 ± 3.2 seconds with similar prostate dose coverage, improved prostate dose homogeneity of up to 6.1%, and lower dose to the urethra of up to 4.0%.

CONCLUSIONS

A DVH-based optimization algorithm using MC dosimetry was developed. The inclusion of the DVH requirements allowed for increased control over the optimization outcome. The optimal plan's quality was further improved by considering tissue heterogeneity.

The influence of tissue composition uncertainty on dose distributions in brachytherapy

BACKGROUND AND PURPOSE

Model-based dose calculation algorithms (MBDCAs) have evolved from serving as a research tool into clinical practice in brachytherapy. This study investigates primary sources of tissue elemental compositions used as input to MBDCAs and the impact of their variability on MBDCA-based dosimetry.

MATERIALS AND METHODS

Relevant studies were retrieved through PubMed. Minimum dose delivered to 90% of the target (D₉₀), minimum dose delivered to the hottest specified volume for organs at risk (OAR) and mass energy-absorption coefficients (μ_en/ρ) generated by using EGSnrc "g" user-code were compared to assess the impact of compositional variability.

RESULTS

Elemental composition for hydrogen, carbon, oxygen and nitrogen are derived from the gross contents of fats, proteins and carbohydrates for any given tissue, the compositions of which are taken from literature dating back to 1940-1950. Heavier elements are derived from studies performed in the 1950-1960. Variability in elemental composition impacts greatly D₉₀ for target tissues and doses to OAR for brachytherapy with low energy sources and less for ¹⁹²Ir-based brachytherapy. Discrepancies in μ_en/ρ are also indicative of dose differences.

CONCLUSIONS

Updated elemental compositions are needed to optimize MBDCA-based dosimetry. Until then, tissue compositions based on gross simplifications in early studies will dominate the uncertainties in tissue heterogeneity.

Coupling I-125 permanent implant prostate brachytherapy Monte Carlo dose calculations with radiobiological models

PURPOSE

To investigate the coupling of radiobiological models with patient-specific Monte Carlo (MC) dose calculations for permanent implant prostate brachytherapy (PIPB). To compare radiobiological indices evaluated with different radiobiological models using MC and simulated AAPM TG-43 dose calculations.

METHODS

Three-dimensional dose distributions previously computed using MC techniques with two types of patient models, TG43sim (AAPM TG-43 water-based conditions) and MCDmm (realistic tissues and interseed effects), for 613 PIPB patients are coupled with biological dose and tumour control probability (TCP) models. Two approaches and their extensions are considered to evaluate biological doses, biologically effective dose (BED) and isoeffective dose (IED), as well as two methods to evaluate TCP. Three novel extensions of equivalent uniform biologically effective dose (EUBED) are suggested which consider the spatial distribution of doses within the target volume. Adopted radiobiological model parameter values (α, β, etc) are those suggested by AAPM TG-137, and sensitivity to parameter choice is discussed.

RESULTS

MCDmm dose calculations can reveal low doses in the prostate target volume, due to tissue heterogeneities or inter-seed effects; considering these low doses in EUBED calculations can lower TCP estimates by up to 70%, with largest differences in patients with calcifications. There are large variations in biological doses and TCPs evaluated over the 613 patient cohort for each radiobiological model considered, reflecting the spectrum of physical doses calculated for these patients with either MCDmm or TG43sim. Depending on the model details, BED, IED and EUBED are, on average, 6.0-9.8%, 7.4-9.2% and 1.8-15% higher, respectively, with TG43sim than MCDmm. TCP estimates computed using MCDmm dose distributions are much lower than expected based on past treatment outcome studies, suggesting a need to re-assess model parameters when evaluating radiobiological indices coupled with heterogeneous tissue model-based dose calculations.

CONCLUSIONS

Cohort average differences in biological dose and TCP estimates between radiobiological models are generally larger than differences for any one radiobiological model evaluated with TG43sim or MCDmm dose calculations. However, heterogeneous tissue dose calculations, like MCDmm, can identify clinically-relevant low dose volumes, e.g., in patients with calcifications, which would otherwise be missed with TG-43. In addition to affecting physical dose distributions, these low dose volumes can largely impact radiobiological dose and TCP estimates, which further motivates the clinical implementation of model-based dose calculations for PIPB.