bluuebunny/crossref_metadata_2025 huggingface dataset:
TH‐AB‐304‐03: Tumor Control Probability Applied to Lung Cancer: A Statistical Test of the Independence of Voxel Response
Authors: Fontanella A , Jeong J , Sonke J , Bradley J , Chen C , Deasy J
Published: June 2015

Purpose:

Conventional radiotherapy planning uses simplified DVH‐derived metrics (e.g. D99, D95) as surrogates for tumor control probability when evaluating plan quality. While this has the advantage of providing an easily derived predictor variable, such metrics fail to consider critical aspects of the underlying radiobiology. Counter to observed radiobiological effects, there is a prevailing assumption of tumor subvolume independence. We tested this by introducing a parameter to capture departures from independence using a modified metric, termed the “generalized tumor dose” (gTD).

Methods:

For given dose distributions, cell survival was derived from a comprehensive biological model (Jeong, 2013 Phys. Med. Biol.). The gTD metric applied a generalized mean term, q, to subvolume survival values, with this term optimized according to its predictive power over the NSCLS cohort (N=316). Patients were treated 1.8–2.2 Gy/Fx in 28–39 Fxs (n=50), or 2.75 Gy/Fx in 24 fractions (n=266). Results were compared to other metrics according binary outcome and actuarial survival prediction.

Results:

Fitting model parameters to combined cohort data using log‐likelihood and actuarial methods, we derived an optimized generalized mean parameter q of −1.14 (95% confidence interval upper bound=0.08). Patient‐specific gTD values derived using this value showed superior performance to other clinical metrics. For gTD, normalized log‐likelihood=−0.471, AUC=0.634, and R^2=0.066, versus normalized log‐likelihood=−0.508/−0.504, AUC=0.473/0.462, and R^2=0.0011/0.0014 for D99/D95. Furthermore, when q wasn't incorporated into the model (q=1, implicitly), AUC decreased to 0.622, normalized log‐likelihood to −0.501 and R^2 to 0.0144.

Conclusion:

Negative q values suggest areas of highest dose play a greater role in determining outcome than expected based only on TCP.The biological origin of this effect could be of several potential causes, including bystander effects, genomic intra‐tumor heterogeneity, or tumor shrinkage. Accounting for these interactions, the gTD metric provides superior accuracy as a predictor of outcome.
TH‐AB‐304‐04: Tumor Control Probability Modeling for Stereotactic Body Radiation Therapy of Early‐Stage Lung Cancer Using Multiple Bio‐Physical Models
Authors: Liu F , Tai A , Lee P , Biswas T , Ding G , El Naqa I , Grimm J , Jackson A , Kong F , LaCouture T , Loo B , Miften M , Solberg T , Li X
Published: June 2015

Purpose:

To analyze pooled clinical data using six radiobiological models and to understand the relationship between BED and TCP for SBRT of early‐stage NSCLC.

Methods:

The clinical data of 1‐, 2‐, 3‐, and 5‐year actuarial or Kaplan‐Meier TCP data from 46 studies were collected for SBRT of early stage (T1 and T2) NSCLC. The TCP data were separated for Stage T1 and T2 tumors if possible. BED was calculated at isocenters using six radiobiological models. Model parameters were determined from a fit to the TCP data using the least chi‐square (χ2) method with one set of parameters regardless of tumor stages or two sets for T1 and T2 tumors separately.

Results:

The fits to the clinic data yield consistent results of large α/β ratios of about 23 Gy for all models. The regrowth model that accounts for the tumor repopulation and heterogeneity leads to a better fit to the data, compared to other 5 models where the fits were indistinguishable between the models. The models based on the fitting parameters predict that the T2 tumors require about additional 1 Gy physical dose at isocenters per fraction (≤5 fractions) to achieve the optimal TCP when compared to the T1 tumors. Sample fractionation schemes were estimated based the determined model parameters for the regrowth model.

Conclusion:

A systematic analysis of a large set clinical data using six radiobiological models shows that local TCP for SBRT of early stage NSCLC has strong dependence on BED with large α/β ratios of about 23 Gy. The six models considered predict that a BED of 80 Gy is sufficient to achieve a 95% TCP. The regrowth model leads to better fit to the clinical data, the LQ model is the simplest and indistinguishable with remaining models. Sample fractionation regimens that can yield TCPs larger than 95% were estimated.
TH‐AB‐304‐06: Investigation of Fractionation Issues in NTCP Modeling of Pneumonitis: An Analysis of Common NTCP Models for Hypo‐Fractionated and Standard‐Fractionated Data
Authors: Troeller A , Soehn M , Grills I , Guckenberger M , Belderbos J , Sonke J , Hope A , Werner‐Wasik M , Xiao Y , Yan D
Published: June 2015

Purpose:

Previous studies showed that NTCP modeling of radiation pneumonitis for hypo‐fractionated radiotherapy (HFRT) has resulted in different model parameters, e.g. a much higher MLD5₀, than for standard‐fractionated RT (SFRT). This study investigates whether both fractionation schemes can be described by the same NTCP model.

Methods:

We retrospectively investigated lung DVHs of 487 patients. Of those, 377 were treated with HFRT (3–10 fractions, median Rx=54Gy) at 5 institutions, and 110 were treated with SFRT (23–47 fractions, median Rx=63Gy) at a single institution. NTD₂ was calculated using the LQ‐model and the low‐dose‐hyper‐radiosensitivity model (LDHRS). The latter could possibly explain the reduced toxicities observed in HFRT by assuming a threshold dose for induced repair. NTCP was modeled for all patients using the Lyman‐MLD and Lyman‐EUD model and compared with AICc. Goodness‐of‐fit was determined using Hosmer‐Lemeshow statistics.

Results:

Within the HFRT group and SFRT group, 7.4% and 10.6% of patients experienced pneumonitis grade>=2 (CTCAE), respectively (median follow‐up=2.13 years). Optimal model parameters (Lyman‐MLD‐LQ: MLD5₀ (NTD)=38.3Gy, m=0.51, AICc=271.0; Lyman‐EUD‐LQ: EUD5₀ (NTD)=24.3Gy, m=0.55, a=0.6, AICc=271.3; Lyman‐MLD‐LDHRS: MLD 5₀(NTD)=42.5Gy, m=0.51, AICc=272.0) for all models yielded acceptable fits to the entire dataset and subgroups (pHL>0.05). Differences in log‐likelihood and AICc values were not large enough to prefer one model over the other. The low volume‐effect parameter (a<1) for the Lyman‐EUD‐LQ model suggests that lower doses‐per‐fraction (<0.58Gy) may be an important factor determining NTCP. This is concurrent with the assumptions of the mechanistic LDHRS model.

Conclusion:

The results indicate that pneumonitis can, theoretically, be described by the same NTCP model for HFRT and SFRT using the investigated models. Furthermore, irradiation with low doses‐per‐fraction may play a role in causing toxicities. Prior findings of different NTCP model parameters for HFRT and SFRT may be due to extrapolation of the bias introduced by the individual datasets, such as differing volumes receiving low doses‐per‐fraction.

This study was supported by the Elekta Collaborative Lung Research Group grant. Dr. Grills discloses stock ownership and is a member of the Greater Michigan Gamma Knife board of directors.
TH‐AB‐304‐07: A Two‐Stage Signature‐Based Data Fusion Mechanism to Predict Radiation Pneumonitis in Patients with Non‐Small‐Cell Lung Cancer (NSCLC)
Authors: Luo Y , McShan D , Kong F , Schipper M , Haken R Ten
Published: June 2015

Purpose:

For NSCLC radiotherapy, toxicity outcomes such as radiation pneumonitis ≥G2 (RP2) may depend on patients’ physical, clinical, biological and genomic characteristics, and on biomarkers measured during the course of radiotherapy. This can include 100s of predictors. To reduce complexity, a two‐step, signature‐based data fusion mechanism was developed to estimate a relationship between patient specific characteristics and the probability of RP2 in terms of a modifying effect on mean lung dose (MLD).

Methods:

Data came from 82 NSCLC patients, 15 with RP2. Besides MLD, each had 179 predictors including 10 clinical factors (eg, age, gender, KPS), cytokines before (30) and during (30) treatment, microRNAs (49), and single‐nucleotide polymorphisms (SNPs) (60). In stage1, cytokines, microRNAs, and SNPs were used to build separate “signatures” via ridge regression. In stage2, a logistic regression predictive model for RP2 was determined in terms of MLD, the other clinical factors, and the signatures using the least absolute shrinkage and selection operator (LASSO). Leave‐one‐out cross‐validation was conducted. This was all implemented via ‘glmnet’ in the R programming environment.

Results:

For stage1, signatures modifying the effect of MLD for cytokine_pre, cytokine_during, microRNA and SNP included 2, 19, 3, 12 important predictors, respectively. For stage2, only the cytokine_during and SNP signatures remained as important modifying effects to MLD. The cross‐validated area under curve (AUC) reaches 0.81 (95% CI 0.70–0.89 based on 2000 stratified bootstrap replicates); significantly better than a null value of 0.50 (p<0.01).

Conclusions:

As implemented here, the two‐stage, signature‐based data fusion mechanism approach includes many patient specific measurements in generation of the signatures (a characteristic of ridge regression), then only includes important signatures and other clinical factors for RP2 prediction (a characteristic of LASSO). This potentially more intuitive approach to handling high dimensional predictors could be an important component of decision support for personalized adaptive radiation treatment.
TH‐AB‐304‐10: Modeling Organ Specific Mechanistic Interactions Between Radiation and Chemotherapy
Authors: Manem V , Grassberger C , Paganetti H
Published: June 2015

Purpose:

Investigate the mechanistic interactions between radiation therapy and chemotherapy for lung, breast and thyroid in the context of secondary malignancies, and derive the parameters needed to build a predictive model from clinical data of Hodgkin's Lymphoma (HL) patients.

Methods:

A biologically motivated mathematical framework, widely known as the initiation‐inactivation‐proliferation formalism, that describes the competition between cell kill and mutation induction, has been used to quantify the interaction between radiation and chemotherapy. HL data from studies including >17’000 patients, including the Childhood Cancer Survivors Study (CCSS), has been used to derive the parameters in the mathematical framework.

Results:

The underlying mathematical framework provides a quantitative measure to examine the interaction between radiation therapy and chemotherapy. The relative proliferation rate of initiated cells in thyroid, breast and lung was found to be 0.63, 076 and 0.96 respectively. We also extracted chemotherapy induced mutation rates and found that it varies by orders of magnitude for these three organs, from 10e‐9 to 10e‐12. More importantly, the excess relative risk (ERR) of secondary thyroid cancer predicted by the model was approximately 3 times lower then the ERR rates in the literature, suggesting a synergistic interaction between radiation and chemotherapy. For breast and lung tissue on the other hand, the model correctly predicted the ERR values after combination therapy found in the literature. This demonstrates that in these organs secondary cancer induction from radiation and chemotherapy merely add up.

Conclusion:

This study enhances the understanding of secondary malignancy induction due to concurrent therapy protocols, and could serve as a tool to design efficient therapeutic strategies for childhood cancers that minimize secondary cancer induction.
TH‐AB‐BRD‐00: Proton Therapy
Authors: Arjomandy Bijan
Published: June 2015

The goal of this session is to introduce the audience to the physics, dosimetry, treatment planning techniques, and quality assurances (QA) procedures used in proton therapy. The course material covers the physics of proton interaction with matter and physical characteristics of clinical proton beams. It will provide information on proton delivery systems and beam delivery techniques for double scattering (DS), uniform scanning (US), and pencil beam scanning (PBS). The session covers the treatment planning strategies used in DS, US, and PBS for various anatomical sites, methods to address uncertainties in proton therapy and uncertainty mitigation to generate robust treatment plans. Challenges involved in the motion management in proton therapy will also be discussed. It will also include the methodology for proton beam dosimetry for clinical commissioning, beam calibration, and quality assurance checks.

Learning Objectives:

Gain knowledge on physics, dosimetry, and treatment planning for proton therapy including intensity modulated proton therapy.

Be acquainted with the procedures involved in machine and patient treatment field specific QA.

Understand the uncertainties associated with proton therapy and currently used strategies for their mitigation in treatment planning.
TH‐AB‐BRD‐02: Proton Treatment Planning
Authors: Pankuch M.
Published: June 2015

The goal of this session is to introduce the audience to the physics, dosimetry, treatment planning techniques, and quality assurances (QA) procedures used in proton therapy. The course material covers the physics of proton interaction with matter and physical characteristics of clinical proton beams. It will provide information on proton delivery systems and beam delivery techniques for double scattering (DS), uniform scanning (US), and pencil beam scanning (PBS). The session covers the treatment planning strategies used in DS, US, and PBS for various anatomical sites, methods to address uncertainties in proton therapy and uncertainty mitigation to generate robust treatment plans. Challenges involved in the motion management in proton therapy will also be discussed. It will also include the methodology for proton beam dosimetry for clinical commissioning, beam calibration, and quality assurance checks.

Learning Objectives:

Gain knowledge on physics, dosimetry, and treatment planning for proton therapy including intensity modulated proton therapy.

Be acquainted with the procedures involved in machine and patient treatment field specific QA.

Understand the uncertainties associated with proton therapy and currently used strategies for their mitigation in treatment planning.
TH‐AB‐BRB‐02: Enabling Web‐Based Treatment Planning Using a State‐Of‐The‐Art Convex Optimization Solver
Authors: Ungun B , Folkerts M , Bush K , Boyd S , Xing L
Published: June 2015

Purpose:

To develop an ultra‐fast web‐based inverse planning framework for VMAT/IMRT. To achieve high speed, we investigate the use of a simple convex formulation of the inverse treatment planning problem that takes advantage of recent developments in the field of distributed optimization.

Methods:

A Monte Carlo (MC) dose calculation algorithm was used to calculate the dose matrix (268228 voxels × 360 beams, 96M non‐zeros) for a 360‐aperture, 4‐arc VMAT plan taken from the clinic. We wrote the objective for the inverse treatment planning problem as a sum of convex (piecewise‐linear) penalties on the dose at each voxel in the planning volume. This convex voxel‐separable formulation allowed us to apply a new, open‐source, CPU‐ and GPU‐capable optimization solver (http://foges.github.io/pogs/) to calculate our solutions of optimal beam intensities. In each planning session, after performing one full optimization we accelerated subsequent runs by “warm‐starting”: for run k, the optimal solution from run k‐1 was used as an initial guess. We implemented the treatment planning application as a Python web server running on a standard g2–2xlarge GPU node on Amazon EC2.

Results:

Our method formed optimal treatment plans in 5–15 seconds. Warm‐start times ranged from 100ms–8s (mean 3s) while sweeping out a 5‐log range of tradeoffs between target coverage and OAR sparing in 1000 total optimizations. Satisfactory plans were reached in 1–10 iterations of the optimization, with total planning time <10 minutes. Dosimetric characteristics such as the DVH curves showed that the resultant plans were comparable or superior to the clinically delivered plan.

Conclusion:

This work demonstrates the feasibility of high‐quality, low‐latency treatment planning using a convex problem formulation and GPU‐ based convex solver, making it practical to manipulate treatment objectives and view DVH curves and dose‐wash views in nearly real‐time in a web application.

Funding support for this work is provided by the Stanford Bio‐X Bowes Graduate Fellowship and NIH Grant 5R01CA176553
TH‐AB‐BRB‐04: Dosimetric and Clinical Benefits of Conformal Radiotherapy Plus Volumetric Modulated Arc Therapy in the Treatment of Non‐Small Cell Lung Cancer
Authors: Jin X , Xie C
Published: June 2015

Purpose:

The purpose of this manuscript is to investigate the dosimetric and clinical benefits of a technique by combining conformal radiotherapy (CRT) plus volumetric modulated arc therapy (VMAT) in the treatment of non‐small cell lung cancer (NSCLC).

Methods:

There were 200 NSCLC patients analyzed retrospectively and treated by CRT, CRT plus VMAT, and full course of VMAT, respectively. Dosimetric differences, radiation pneumonitis (RP) rates and their correlation were investigated.

Results:

The number of patients analyzed in CRT, CRT plus VMAT and full course of VMAT were 42, 28, and 53, respectively. The V93 and V95 (percent volume covered by isodose line) of planning target volume (PTV) were improved from 95.7%±2.2% and 94.5%±2.4% in CRT to 97.9%±4.6%, 98.3%±3.2% and 96.3%±5.8%, 97.1%±4.1% in CRT plus VMAT and full course of VMAT, respectively. CRT plus VMAT improved the PTV coverage compared with CRT in a cost of higher spinal cord maximum dose (p=0.02), larger lung volume receiving 5 Gy (V5, p=0.02) and mean lung dose (MLD) (p=0.04), and decreased the low dose lung volumes compared with VMAT. The RP rates were 26%, 39% and 49% for CRT, CRT plus VMAT and VMAT, respectively. V5 was significantly associated with RP and had a threshold of 60% and 65% for CRT plus VMAT and VMAT, respectively, to limit the RP rate <30%.

Conclusion:

CRT plus VMAT is a promising with increased target coverage compared with CRT and reduced low dose lung volume and RP compared with full course of VMAT in the treatment of NSCLC. V5 was significantly associated with RP and with a predicted threshold of 60% and 65% for CRT plus VMAT and VMAT, respectively, to limit the RP rate <30%. Clinical trials with large population are needed to further verify the benefits of CRT plus VMAT in the treatment of NSCLC.
TH‐AB‐BRB‐05: Quantification and Visualization of Treatment Plan Robustness in Radiotherapy
Authors: Fix MK , Volken W , Frei D , Terribilini D , Aebersold DM , Manser P
Published: June 2015

Purpose:

Currently, the robustness is not taken into account in the evaluation process of treatment plans in radiotherapy. One reason is the lack of algorithms to determine and to visualize the robustness. Thus, the aim of this work is to develop tools and methods to quantify and visualize the robustness for treatment plans including random and systematic setup uncertainties.

Methods:

A setup error phase‐space including systematic and random setup errors for translation and rotation is explored to determine the plan robustness by applying different approaches. One approach shifts the optimized dose distributions relatively to the patient geometry and, thus, dosimetric parameters or DVHs can be quickly estimated. A ‘robustness‐map’ for this plan is created based on user‐defined criteria defining the robustness for the case considered. These criteria subdivide the robustness‐map for the setup error phase‐space into a region that is compatible with these criteria and another that is not. However, approaches using further dose calculations for the setup error phase space are needed to achieve reliable conclusions of the robustness. Thus, additional dose calculations using different resolutions of the setup error phase‐space are performed guided by the robustness‐map achieved using the dose‐shifting approach. The intermediate dose distributions are determined by nearest neighbor interpolation.

Results:

A graphical user interface based on QT version 5.3.1 was developed to visualize robustness‐maps, dose‐differences and DVHs. Additionally, correlations of all quantities can be displayed. The creation of robustness‐maps is useful to assess and compare the robustness of different treatment plans and was successfully applied to plans covering different tumor‐sites. Differences in DVH parameters using the different approaches are within 5%.

Conclusion:

The developed tool for visualization and analysis of robustness‐maps is an easy and efficient way to compare the robustness of treatment plans. This work was supported by Varian Medical Systems.

This work was supported by Varian Medical Physics.
TH‐AB‐BRB‐06: Accelerating Sliding Window VMAT by Open‐Out/close‐in Field Delivery Techniques
Authors: Craft D
Published: June 2015

Purpose:

To describe how to make sliding window VMAT delivery more efficient by customizing the delivery of each angular sector based on the shape of the fluence maps.

Methods:

VMAT is a highly non‐convex optimization problem. We decompose the problem by first considering an IMRT problem with 30 equispaced beams. This problem is solved with smoothing terms and the resulting fluence maps are analyzed for delivery considerations. Rows of the fluence maps that are bimodal are to be delivered with a leaf sweep across the field, both leaves moving in same direction. Unimodal rows can be delivered more efficiently with leaves either closing inward or opening outward. More classifications of this type are done, as well as re‐optimizations of the 30 beam IMRT problem with penalties to achieve the desired fluence map restrictions. Mixed integer programming is used for some leaf trajectory calculations and to decide which sectors will get non‐sliding window delivery.

Results:

By iterating between fluence map classification and optimization with penalties which support the classifications, VMAT plans of similar quality to full sliding window plans can be produced with improved delivery time. For a fluence row that is unimodal and “steep enough” (shallow unimodal fields require unrealistically fast leaf speeds), delivery time using leaf close‐in/open‐out can be more than twice as fast as sliding window delivery. For a prostate case, by classifying 50% of the fluence maps as unimodal deliverable, we are able to reduce the treatment time from 3.5 minutes to 2.6 minutes with a negligible change in plan quality.

Conclusion:

By choosing the delivery style of fluence maps based on their shapes, rather than imposing that every map gets a full sliding window delivery, we can retain the high‐quality of sliding window style VMAT while gaining delivery efficiencies of non‐sliding window VMAT.

David Craft is partially supported by a research collaboration with RaySearch Laboratories.
TH‐AB‐BRB‐11: A Method of Unwrapping 3D Complex Hollow Organs for Spatial Dose Surface Analysis
Authors: Witztum A , Warren S , Partridge M , Hawkins M A
Published: June 2015

Purpose:

To describe and evaluate (on 15 duodena) a method of mapping 3D surface dose for hollow organs with complex geometry (duodenum) to a 2D plane. Methods have previously been presented for unwrapping tubular organs (oesophagus and rectum) for spatial modelling of toxicity. However, the complex geometry of the duodenum demands a new method for producing 2D surface dose maps (SDMs).

Methods:

The organ is defined by Delaunay triangulation and voxelised into a 0.085×0.085×0.085 cm3 binary image. This is thinned using a distance transform resulting in a cluster of points approximately down the organ centre. A subset of points is manually selected and a central axis spline (CAS) fit. The CAS is discretised into 30 points and a ray cast from each point (angular step 12°, axial step 0.05 cm) on the plane normal to the vector to the next CAS point and the dose at the closest perimeter point recorded. These perimeter slices are “unwrapped” from the edge distal to the pancreas so the high dose region (proximal to the tumour) falls in the centre of the dose map.

Results:

A complete methodology has been described that produces 2D SDMs for organs with simple or complex geometries. This method was successfully tested by extracting SDMs for 15 duodena, with one oesophagus illustrating application to simple geometries. Visual comparisons show that a 30×30 map provides sufficient resolution to view features of interest.

Conclusion:

A new method for unwrapping surface dose in complex hollow organs has been developed and tested on 15 duodenal cases. The method is robust, requires minimal human interaction, and has been shown to be generalisable to simpler geometries (oesophagus). The 2D dose maps produced provide spatial dose distribution information which will be explored to create models that may improve toxicity prediction in treatments for locally‐advanced pancreatic cancer.
TH‐AB‐BRB‐12: A Novel 2D Expansion Technique Based On Beam Geometry for Generating Margins
Authors: Tsang H , Kamerling CP , Nill S , Oelfke U
Published: June 2015

Purpose:

Current practice of using 3D margins in radiotherapy with high‐energy photon beams provides larger‐than‐required target coverage. Negligible dose changes for small CTV displacements in the beam direction allow for the design of 2D‐based CTV‐PTV margins. We investigate the impact of a novel 2D margin concept for prostate radiotherapy treatments.

Methods:

Our implementation of 2D margins follows van Herk's (2000) margin recipe by considering only geometrical uncertainties perpendicular to the incident beam direction. The union of all beam‐angle‐dependent expansions defines our PTV. Five prostate radiotherapy plans were generated using IMRT inverse planning following clinical protocols with a prescription of 74Gy in 37 fractions. Systematic uncertainties of 1.1, 1.1, 1.5mm in the LR, SI, and AP directions, respectively, and 0.9, 1.1, 1.0mm for the random uncertainties, were used. A verification tool was used to simulate the effects of systematic and random errors, using a population of 10,000 identical geometries. The fraction of the population that satisfies or violates a certain dose or volume constraint was used for comparison.

Results:

The median observed PTV volume reduction between the 3D and 2D margins is 7.85% (range, 7.14–8.29%). Using the verification tool, all CTVs had close to 100% of the population satisfying D98% > 95% of the prescribed dose. When applying the verification tool to analyse the rectal doses delivered, a median relative reduction of 1.05% (range, 0.66–6.17%) of the population was observed for D2% above 70Gy; similarly for D2% above 74Gy, a median relative reduction of 31.7% (range, 13.3–52.6%) was observed.

Conclusion:

We are able to ascertain sufficient dose coverage using our 2D margin concept for planning whilst lowering dose to nearby structures, though the amount of sparing depends largely on the patients’ anatomy. With the coverage observed using our novel technique, we believe additional room exists for sparing dose to target‐adjacent OARs.

Research at The Institute of Cancer Research is supported by Cancer Research UK under Programme C33589/A19727. We acknowledge NHS funding to the NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research.
TH‐AB‐BRA‐03: Development of a Scintillation Dosimetry System Capable of Real‐Time Dosimetry During LDR Brachytherapy Seed Implant
Authors: Therriault‐Proulx F , Beaulieu L , Beddar S
Published: June 2015

Purpose:

To develop a scintillation dosimetry system with sufficient sensitivity for accurate dose measurements during low dose‐rate (LDR) brachytherapy, including single‐seed insertion.

Methods:

First, the impact of the energy dependence of plastic scintillation detectors (PSDs) was studied using GEANT4 Monte Carlo simulations adapted from the ALGEBRA source code for brachytherapy. The photon energy spectrum was obtained in water around an I‐125 brachytherapy seed and convoluted to a detector response. Secondly, because low dose rate results in a corresponding low light emission, another part of this study focused on optimizing the choice of each component of the system to enhance the signal‐to‐noise ratio. A novel motorized and automated PSD positioning device mounted to a water tank was also developed to minimize uncertainties. Measurements of 15s were performed for distances from 1 cm to 7 cm in different radial and longitudinal directions and compared to the expected values.

Results:

Even though the photon energy spectrum around the I‐125 source was shown to vary considerably, the effect on dose measurement accuracy was minor (<1%). This holds true as long as the PSD is calibrated using the same brachytherapy isotope. The different components of the PSD probe did not show a significant effect on the calculated spectrum (<0.1%). Proper selection of photodetector and optical components helped improving by over 35X the signal‐to‐noise ratio. The measurements agreed with the expected values in all conditions. The PSD system was shown capable of detecting source misplacements for all clinically relevant conditions of measurement.

Conclusion:

The system developed in this study allows for accurate dose measurements around an I‐125 seed and could therefore provide valuable information in real‐time during LDR brachytherapy seed implant.

Part of this research was supported by the Odyssey Program at The University of Texas MD Anderson Cancer Center.
TH‐AB‐BRA‐04: A Physical Phantom for Experimental Commissioning and Performance Testing of 192Ir MBDCAs
Authors: Pappas E , Moutsatsos A , Zoros E , Peppa V , Zourari K , Karaiskos P , Papagiannis P
Published: June 2015

Purpose:

To present a phantom‐based methodology for the experimental commissioning and performance testing of model‐based dose calculation algorithms (MBDCAs), which have been recently introduced in 192Ir HDR brachytherapy treatment planning systems (TPSs).

Methods:

The phantom was constructed from PMMA slabs properly machined to accommodate material and density inhomogeneity inserts, as well as TLD detectors (TLD‐100, 1×1×1 mm3), radiochromic films (Gafchromic EBT‐3) and a cylindrical Presage dosimeter (d=6cm, h=8cm). The spatial arrangement of the different dosimeters within the phantom permitted measurements at regions of scatter conditions departing from TG43 assumptions (e.g., at phantom boundary) and/or close to the inhomogeneity inserts. The phantom was CT‐imaged and a multiple 192Ir source position treatment plan was prepared using the Oncentra Brachy v4.5 TPS. Dose calculations of the Oncentra‐ACE MBDCA were exported in DICOM‐RT for further evaluation. The plan was delivered using a microSelectron v.2 192Ir source and 4 plastic catheters embedded in PMMA slabs. The treatment plan data were subsequently imported into an in‐house developed software tool (BrachyGuide) used to obtain corresponding Monte Carlo (MC) simulation dosimetry results with the MCNP code. Detectors’ measurements were compared to both MBDCA‐ and MC‐calculated results in terms of absolute point dose differences, 2D and 3D relative dose and gamma index distributions.

Results:

Experimental dosimetry results and MC calculations were found in agreement within uncertainties. The corresponding dosimetry comparison between detector measurements and ACE‐calculations showed also a good agreement which deteriorated with increasing distance from the implant due, mainly, to MBDCA assumptions and optimization settings. The latter was highlighted in the comparison of the 3D dose distribution measured by the Presage dosimeter to corresponding ACE calculations.

Conclusion:

The proposed phantom/methodology can be used for both commissioning and quality assurance of MBDCA‐based TPSs, as well as for benchmarking MC‐calculated reference dose distributions.

Research co‐financed by the ESF and Greek funds through the Operational Program Education and Lifelong Learning Investing in Knowledge Society of the NSRF. Research Funding Program: Aristeia.

Nucletron, an Elekta company (Veenendaal, The Netherlands) is gratefully acknowledged for providing Oncentra Brachy v4.5 for research purposes.
TH‐AB‐BRA‐05: Infrastructure and Process for Model‐Based Dose Calculation Software Commissioning in Brachytherapy
Authors: Sloboda R , Ballester F , Tedgren A Carlsson , Culberson W , Esthappan J , Haworth A , Lowenstein J , Ma Y , Mourtada F , Papagiannis P , Pike T , Rivard M , Siebert F , Smith R , Spadinger I , Taylor P , Verhaegen F , Vijande J , Beaulieu L
Published: June 2015

Purpose:

To describe a new infrastructure and process that will enable end‐users to commission model‐based dose calculation (MBDC) software for brachytherapy planning, and to invite end‐users to participate in the beta testing phase of this process.

Methods:

The AAPM working groups on model‐based dose calculation algorithms in brachytherapy (WGDCAB) and brachytherapy source registry (WGBSR) collaboratively implemented the basic MBDC software commissioning workflow described in the AAPM+ESTRO TG‐186 report. For a small number of initial test cases virtual CT image, anatomical structure set, treatment plan, and associated 3D dose data for a single dwell position occupied by a WGDCAB generic high‐dose‐rate Ir‐192 virtual source, with or without a generic shielded applicator, were generated. Dose distributions were calculated using Monte Carlo (MC) code MCNP6, Elekta's Advanced Collapsed cone Algorithm (ACE™), and Varian's Acuros™ BV module. All data were saved in DICOM RT format and uploaded to a provisional, web‐accessible repository in the joint AAPM—IROC Houston brachytherapy source registry. It is envisioned that end‐users will download data for a test case from the repository, verify its correctness, calculate 3D dose locally, and then compare with MCNP6 and MBDC doses obtained from the repository using TPS‐based comparison tools. User guides in preparation for each TPS will facilitate the process.

Results:

Initial evaluations of DICOM data download, TPS import, MBDC dose calculation, and reference dose comparison indicate that the infrastructure and process described above are both viable and practicable. Ancillary data including generic source and shielded applicator specifications, CAD drawings, and MC code definitions are also available in the repository.

Conclusion:

With further refinement and beta‐test input from end‐users, a practical MBDC software commissioning process for brachytherapy can be established. A procedure for adding new test cases submitted by end‐users to the repository will be put in place by WGDCAB.
TH‐AB‐BRA‐07: Determination and Evaluation of a Dose‐Rate Conversion Coefficient for the Xoft Axxent® Electronic Brachytherapy Source in the Xoft Titanium Cervical Applicator
Authors: Simiele S , Micka J , Culberson W , DeWerd L
Published: June 2015

Purpose:

A new NIST‐traceable air‐kerma standard has been established for electronic brachytherapy (EBT) sources which has motivated the development of a modified AAPM TG‐43 dosimetry formalism. Due to the low‐energy nature of EBT, it is important to verify that a single dose‐rate conversion coefficient (DRCC) value is appropriate for use with each applicator type, as small changes in applicator thickness between individual applicators due to manufacturing tolerances may have a large impact on the DRCC. This work aims to determine the DRCC for the Xoft source in multiple titanium cervical applicators and verify that a single representative DRCC value is acceptable for clinical use.

Methods:

The DRCC and radial dose function (RDF) were measured using three titanium cervical applicators and one source. An annulus of Virtual Water™ was used to position TLD‐100 microcubes at 30 ‐degree increments around the source and at radial distances of 1, 2, 3, and 4 cm from the source in a liquid water phantom. A slit collimator, designed to fit inside an HDR1000 Plus well‐ionization chamber, was used to measure the anode‐output profile (AOP) of each source. The measured profile was used with an MCNP5‐simulated AOP to align the manufacturer‐stated anode origin in the plane of the microcubes. A statistical analysis was performed to compare the DRCC values measured in each applicator.

Results:

Statistical analysis results indicated that there is no significant difference between the DRCCs measured in the different applicators. The RDF values determined in the separate applicators are within 5% of each other at 4 cm depth.

Conclusion:

The DRCC was measured in three separate applicators to determine if the difference in applicator thickness would require a DRCC specific to each applicator. The results demonstrate that a single representative DRCC, determined with a subset of titanium cervical applicators, is acceptable.

This work was partially supported by the NIH radiological sciences training grant T32‐CA009206 and Xoft Inc. (a subsidiary of iCAD).
TH‐CD‐201‐02: Networks, Connectivity, and Interoperability
Authors: Curran B.
Published: June 2015

Radiation Oncology is a data intensive discipline. Data moves among various systems (imaging, treatment planning, image guidance and delivery) and the development of troubleshooting methods and appropriate QA and QC measures strongly depends on the medical physicists’ understanding of fundamental information technology concepts. This session will cover the concepts involved in networking, data transfer, DICOM and HL7 connectivity, database operations and QA for IT. Databases are at the heart of all modern radiotherapy clinics and are used for planning as well as treatment delivery, and they also drive the observed state of the data at various clients. Querying and reporting from the database is an important skill that medical physicists need to learn. Interactions between clients and databases need to be well understood to ensure that correct, synchronized, and up‐to‐date information is properly communicated amongst all stakeholders involved in the treatment management process. Entrance into the database via DICOM, HL7 and other proprietary transfer mechanisms must also be tested for accuracy of information storage and presentation. Issues with inter‐operability between systems must also be well understood during acceptance testing and commissioning activities. The ongoing efforts of IHE‐RO will be explained in the context of the need for increased engagement on the part of medical physicists.

Learning Objectives:

Understand the basic IT infrastructure required to run a radiotherapy department

Become familiar with database concepts to improve troubleshooting skills

Learn the principles of designing a QA program tailored to one's specific clinical IT environment.

Curan: Funding from AAPM for travel and expenses DICOM and Funding from ASTRO for travel and expenses IHE RO
TH‐CD‐210‐00: Nuclear Medicine 102
Authors: MacDougall Robert
Published: June 2015

There have been significant recent advances in SPECT/CT and PET/CT for Oncology and Cardiology. In oncologic imaging, some commercial SPECT/CT systems now output quantitative uptake values; calibration and calculation of these quantitative uptake values will be discussed. Advances in PET detector design and PET data acquisition modes that have implemented in commercial PET/CT systems will be presented. Innovations in iterative reconstruction of SPECT/CT and PET/CT will also be described.

In cardiac imaging, novel collimator designs, such as multi‐pinhole and locally focusing collimators arranged in geometries that are optimized for cardiac imaging have been implemented to reduce imaging time and radiation dose. These new collimators have been coupled with solid‐state photon detectors. The new SPECT scanners demonstrate up to a 7‐fold increase in photon sensitivity and up to 2 times improvement in image resolution. Although PET scanners are used primarily for oncological imaging, cardiac imaging can benefit from the improved PET sensitivity of 3D systems without inter‐plane septa and implementation of the time‐of‐flight reconstruction. Additionally, all major PET vendors now implement resolution recovery techniques. These new methods improve image contrast, image resolution, and reduce image noise for cardiac imaging. New quantification techniques have been proposed for analysis of cardiac SPECT and PET images. These include direct change analysis, motion‐frozen analysis, hybrid quantification of SPECT and anatomical CT angiography data, and integration of several quantitative parameters in composite scores.

Learning Objectives:

To discuss clinical implementation of commercial quantitative SPECT/CT systems

To describe advances in PET detector design and data acquisition

To report on innovations in iterative reconstruction of SPECT/CT and PET/CT

4 Describe novel hardware designed for cardiac SPECT

Understand how recent hardware advances in PET/CT are relevant to cardiology

Describe new software techniques for quantification of cardiac SPECT and PET
TH‐CD‐210‐02: Advances in SPECT, SPECT/CT and PET/CT for Cardiology
Authors: Slomka P.
Published: June 2015

There have been significant recent advances in SPECT/CT and PET/CT for Oncology and Cardiology. In oncologic imaging, some commercial SPECT/CT systems now output quantitative uptake values; calibration and calculation of these quantitative uptake values will be discussed. Advances in PET detector design and PET data acquisition modes that have implemented in commercial PET/CT systems will be presented. Innovations in iterative reconstruction of SPECT/CT and PET/CT will also be described.

In cardiac imaging, novel collimator designs, such as multi‐pinhole and locally focusing collimators arranged in geometries that are optimized for cardiac imaging have been implemented to reduce imaging time and radiation dose. These new collimators have been coupled with solid‐state photon detectors. The new SPECT scanners demonstrate up to a 7‐fold increase in photon sensitivity and up to 2 times improvement in image resolution. Although PET scanners are used primarily for oncological imaging, cardiac imaging can benefit from the improved PET sensitivity of 3D systems without inter‐plane septa and implementation of the time‐of‐flight reconstruction. Additionally, all major PET vendors now implement resolution recovery techniques. These new methods improve image contrast, image resolution, and reduce image noise for cardiac imaging. New quantification techniques have been proposed for analysis of cardiac SPECT and PET images. These include direct change analysis, motion‐frozen analysis, hybrid quantification of SPECT and anatomical CT angiography data, and integration of several quantitative parameters in composite scores.

Learning Objectives:

To discuss clinical implementation of commercial quantitative SPECT/CT systems

To describe advances in PET detector design and data acquisition

To report on innovations in iterative reconstruction of SPECT/CT and PET/CT

4 Describe novel hardware designed for cardiac SPECT

Understand how recent hardware advances in PET/CT are relevant to cardiology

Describe new software techniques for quantification of cardiac SPECT and PET
TH‐EF‐BRD‐03: Demonstration of a Concurrent Monte Carlo Dose Calculation and Fluence Optimization Platform with Low Memory Footprint
Authors: Yang Y , Svatos M , Zankowski C , Bednarz B
Published: June 2015

Purpose:

To enable a treatment planning solution that utilizes voxel‐specific penalties requires optimizing over a large solution space both quickly and accurately. Although challenging, such methods will enhance the clinical impact of adaptive (online and offline) and biologically guided radiotherapy. Monte Carlo calculations generally provide improved accuracy over deterministic methods, but with increased computation time. Instead, we present a low memory footprint direct Monte Carlo optimization platform that optimizes the fluence concurrently during the MC dose calculation. This reduces the wasted computation time spent on minimally contributing beamlets, and makes the combined optimization and dose calculation tasks both fast and accurate even over a large mathematical space.

Methods:

The Monte Carlo code Geant4 v9.6 was used to create the Fluence Adjusted Scaled Transport Monte Carlo (FASTMC) platform. The FASTMC platform minimizes clinical cost objectives during the dose calculation using multiple co‐planar sources of arbitrary gantry angle and SID, for rotational or fixed beams. Beamlet particle histories are transported in iterative steps; cost is evaluated after each iteration, with beamlet intensity and dose profile incremented such that the cost is decreased. A Greedy Heuristic algorithm is employed that circumvents the necessity of a dose coefficient matrix, resulting in a memory footprint scaling that is independent of the patient CT resolution, and allows for intelligently biased selection, and calculation of beamlets based on costs in prior iterations.

Results:

A multi‐field coplanar prostate plan was generated during the MC dose calculation, resulting in an optimized dose profile that is calculated concurrently with the optimized fluence. All clinical constraints for the target and OARs were met for this plan.

Conclusion:

This study demonstrated the feasibility of optimizing an incident fluence map using voxel‐specific penalties during Monte Carlo dose calculations as a means of treatment planning tailored to emerging radiation therapy delivery paradigms.

This work is partially based upon an idea conceived by Varian Medical Systems. This work is partially supported by the NIH Training Grant 2T32CA009206‐36A1.
TH‐EF‐BRD‐08: A Retrospective Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality
Authors: Liu S , Mao J , Li H , Mutic S , Yang D
Published: June 2015

Purpose:

Simple rules and common senses based on prior knowledge could be powerful tools for medical physicists to identify and prevent severe errors in radiation therapy (RT) treatment plans. However, rules, e.g. the acceptable MU/cGy ratio for different treatment sites, are not yet available quantitatively and therefore cannot be utilized effectively. In this study, we have retrospectively analyzed all the EBRT treatment plans of the past 8 years at authors’ institution. The goal is to better understand the most common treatment plan data statistics, involving MU/cGy ratio, numbers of beams and segments, and ranges of gantry angles for lateral targets, in order to quantitatively define the common sense‐based rules that could be used to guide manual or automatic physics checks.

Methods:

15844 previous treatment plans were obtained from Mosaiq. Plan data were extensively processed including 1) reducing data size, 2) removing empty and unapproved site and fields, 3) deriving common site names from names of prescriptions and beams that were spelled and misspelled extremely diversely, and 4) removing the plans not covered by this study, e.g. HDR plans. Processed data was then statistically analyzed using multivariate regression algorithms to quantitatively investigate the relationships between the input parameters, i.e. treatment site, technique, modality, and laterality, and the output parameters, i.e. MU/cGy radio, the total number of treatment beams and segments, and the beam gantry angles (only for lateral targets).

Results:

Mean values and standard deviations of the output parameters were calculated and tabulated per treatment site, technique and modality. Beam gantry angle range and mean gantry angle were analyzed per site for plans treating lateral targets.

Conclusion:

The results of this study, as the statistics of the major treatment plan parameters, could be useful to quantitatively evaluate the consistency of new treatment plans in either manual or automatic plan checking procedures.