Volume 36, Issue 6, June 2009
Index of content:
- Therapy Scientific Session: Ballroom B
- IMRT I
36(2009); http://dx.doi.org/10.1118/1.3182211View Description Hide Description
Purpose: During the delivery of volumetric modulated arc therapy (VMAT), random errors exist in both the MLC leaf positions and the gantry angle. In this work, we investigated the impact of such random errors on VMAT plan quality and delivery accuracy. The impact of system calibration errors was also examined. For comparison purposes, we performed a similar study on step‐and‐shoot IMRT plans. Material and Method: VMAT plans for three treatment sites (prostate, pancreas and head‐&‐neck) were created using a home‐grown arc sequencer. Next, random and systematic leaf position errors were introduced into these plans with the random errors sampled from Gaussian distributions of varying widths ranging from 1 to 3mm. Two types of systematic errors, including MLC bank shifts in the same direction (Type I: leaf gap unchanged) and MLC bank shifts in opposite directions (Type II: leaf gap increase/decrease). The plan quality variations were compared in the Pinnacle3 planning system. Plans with systematic errors were verified using the MatriXX ion chamber array with gamma evaluation criteria of 3%/3mm. Results: The plan degradation observed for VMAT plans was slightly less as compared to that for fixed‐field IMRT plans when random errors up to 3mm to the leaf positions were introduced. With type I systematic errors of 3mm on leaf positions, the average standard deviation of PTV dose increased by 10.2%. This value increased to 18.4% for the corresponding fixed‐field IMRT plans. A larger impact on the IMRT plans was also observed when type II systematic errors were introduced. The above results were confirmed by plan verification measurements with higher gamma passing rates for VMAT plans when systematic errors were applied. Conclusion: The VMAT delivery technique has better tolerance to random and systematic errors in gantry angle and MLC leaf position errors as compared with step‐and‐shoot IMRT.
Research supported by Elekta.
MO‐D‐BRB‐02: Using Total‐Variation Regularization for IMRT Inverse Planning with Field‐Specific Numbers of Segments36(2009); http://dx.doi.org/10.1118/1.3182212View Description Hide Description
Purpose: Currently, there are two types of treatment planning algorithms for intensity modulated radiation therapy(IMRT). The beamlet‐based algorithm generates fluence maps with high complexity, resulting in large numbers of segments in the delivery. The segment‐based direct aperture optimization (DAO) algorithm uses a small number of segments. However, the number of segments is typically pre‐fixed, and the optimization is computationally intensive. In this work, a regularization based algorithm is proposed to overcome the drawbacks of the DAO method. Method and Materials: Instead of smoothing the fluence maps, we include a total‐variation term in the optimization objective function to reduce the number of signal levels of the fluence maps and therefore the number of deliverable apertures. As compared to the DAO algorithm, our method has an efficient form of quadratic optimization, with an additional advantage of optimizing field specific numbers of segments based on the modulation complexity. Results: The proposed approach is evaluated using two clinical cases. Provided that the clinical acceptance criteria of the treatment plan are satisfied, for the prostate patient, the total number of segments is reduced from 61 using the Eclipse planning system to 35 using the proposed algorithm; for the head and neck patient, the total number of segments is reduced from 107 to 28. The head and neck result is also compared to that using an equal number of 4 segments for each field. The comparison shows that using field‐specific numbers of segments achieves a much improved dose distribution. Conclusion: A total‐variation based inverse planning method is proposed in this work. As compared to other existing methods, the proposed algorithm is derived using different principles and implemented efficiently. The patient studies show that the proposed algorithm significantly reduces the total number of segments used in the treatment without compromising the delivered dose distribution.
MO‐D‐BRB‐03: Using a Database of Patient Geometric and Dosimetric Information for Quantitative IMRT Plan Quality Control36(2009); http://dx.doi.org/10.1118/1.3182213View Description Hide Description
Purpose: To empower IMRTtreatment planners with the ability to judge new plans against the performance of similar past plans. Method and Materials: This is accomplished by searching a database of treated patients using the geometric relationships between targets and organs at risk (OARs). We introduce the concept of a shape relationship descriptor to quantify this intuitively important relationship. The overlap volume histogram (OVH) simplifies the complex 3D relationship between a target and an OAR. The OVH is the normalized 1D histogram of the OAR volume within a distance of the target. The OVH descriptor was used to search a patient database, providing a patient specific set of dose volume histograms (DVHs). These DVHs were then presented to the planner to aid their decisions. Results: The method was applied to both parotids of 32 treated head‐and‐neck patients. The 17 parotids that promised the greatest reduction in D 50 were selected for re‐planning. These 17 parotids came from 13 patients. Our method indicated that the doses of the other nine parotids of the 13 patients could not be reduced, so they were included in the re‐planning process as controls. Re‐planning with an effort to reduce D 50 was conducted on the 26 parotids of these 13 patients. Average reductions of D 50 were 6.6Gy for the 17 improvement candidates and 1.9Gy for the controls. Originally, several parotids violated the RTOG planning goal of V(30Gy)⩽50%. Eleven of these were improvement candidates, and re‐planning reduced this number to three. Re‐planning had no impact on the five control parotids that were violating the RTOG planning goal. According to the physician reviews, re‐planning did not degrade target coverage or OAR sparing. Conclusions: Our method offers a patient specific DVH evaluation for OARs, providing an effective mechanism of quantitative IMRT plan quality control.
MO‐D‐BRB‐04: Mixed Integer Models for Elucidating the Tradeoff Between Treatment Time and Plan Quality in VMAT Delivery36(2009); http://dx.doi.org/10.1118/1.3182214View Description Hide Description
Purpose: Rotational IMRTdelivery techniques, such as volumetric modulated arc therapy (VMAT), are alternatives to standard IMRTdelivery, claiming faster delivery and equally good dose distributions. In reality there is a cost to faster delivery times. The purpose of this investigation is to understand the tradeoff between plan quality and treatment time in rotational delivery by use of optimization models. Method and Materials: We study a simplified VMAT model for a 2D phantom and construct an exact mixed integer optimization model for it. The model includes treatment time as a constraint, and otherwise does not specify how the delivery is to be done, allowing for either VMAT or IMRT solutions. The model is too large to solve, but we trap the optimal solution between tight upper and lower bounds with additional models. The lower bound solutions are used to warm start the upper bound solutions. We construct Pareto surfaces for the joint tradeoffs of treatment time, tumor maximum dose, and organ at risk mean dose. Results: Upper bounds are within 10% of lower bound solutions. This allows us to conclusively demonstrate that 1) the treatment time constraint becomes more important when high intensity modulation is required to achieve optimal dose distributions, and 2) finding a good feasible VMAT plan is benefitted (by ∼5% in objective value) by warmstarting using the fluence modulation required at each angular location. Conclusion: For problems where the optimal dose distribution has non‐convex iso‐dose lines (to avoid nearby critical structures), the high modulation necessary to achieve such plans translates into longer treatment times. In these cases, when allowed treatment time is small, the optimal solutions show fluence being delivered at every angle — VMAT. If treatment time is allowed to be larger, IMRT plans are found to be optimal.
MO‐D‐BRB‐05: A Novel Dynamic MLC Leaf Sequencing Algorithm for 4D Treatment with Deformable Target Motion Correction36(2009); http://dx.doi.org/10.1118/1.3182215View Description Hide Description
Purpose: To investigate a dynamic MLC (DMLC) leaf sequencing method for the 4D IMRT delivery. Materials and methods: A 4D CT was obtained and 10 phase CTdata sets were created retrospectively. Individual IMRT plan was generated for each phase on Pinnacle treatment planning station, and a 4D plan was created using a deformable image registration technique. Based on the DMLC leaf sequences generated from the plan of each phase, the non‐rigid motion corrected 4D leaf sequence was created by matching the time indexes of the leaf sequences and the breathing cycles of each phase. The 4D leaf sequence was iteratively adjusted for the maximum leaf speed to avoid any beam hold‐offs. At the same time, a rigid motion corrected 4D leaf sequence was calculated by adjusting the leaf center corresponding to the center of mass position of the target in each phase. The opening density map (ODM) of the 4D plan was used to create a traditional no‐motion corrected leaf sequence. All three leaf sequences were delivered by Varian 2100 C/D with Millennium 120 leaf DMLC. The MatriXX (IBA Dosimetry) was used to measure the delivered fluence map every 40 ms. The delivered fluence maps were registered back to the patient's coordinate system. Results: Using the 4D plan ODM as reference, the fluence map from the non‐rigid motion corrected 4D leaf sequence showed a maximum dosimetric difference of 3%. However, the fluence maps from the rigid motion corrected 4D leaf sequence and the no‐motion corrected leaf sequence showed maximum dosimetric differences of 30%. Conclusion: For 4D plan DMLC delivery, the non‐rigid motion corrected leaf sequencing, the rigid motion corrected 4D leaf sequencing and the no‐motion corrected leaf sequencing were investigated. Results showed that the non‐rigid motion corrected leaf sequencing is the one that closest deliverers the 4D plan.
MO‐D‐BRB‐06: Comparison of Dosimetric Inaccuracies Introduced by Intra‐Fraction Respiratory Motion in IMRT and VMAT36(2009); http://dx.doi.org/10.1118/1.3182216View Description Hide Description
Purpose: To compare the dosimetric inaccuracies introduced by intra‐fraction respiratory motion in equivalent IMRT and VMAT plans. Method and Materials: First, a commercial respiratory motion platform was adapted to support the 3D dosimetric phantom. A slit‐field IMRT and equivalent VMAT plan were delivered to the phantom with one‐dimensional (S‐I direction) respiratory motion (cos4 pattern, peak‐to‐peak amplitude = 1.5cm, 15 BPM). A clinical patient IMRT plan and equivalent VMAT plan were also delivered to the moving phantom. The plans were delivered to the phantom with the motion platform both static and dynamic with a range of different starting phases in the respiratory cycle. Measurements were compared using the percent differences (PD) of the dynamic measurements relative to the static measurements. Results: The mean dynamic versus static target PD for non‐clinical IMRT and VMAT fractions were −6.4 ±11.9% and −4.2 ±6.7%, respectively. The mean dynamic versus static target PD for clinical lungIMRT and VMAT fractions were ‐1.1 ..4.2% and ‐0.9 ..3.3%, respectively. The PD of the clinical IMRT plan showed sensitivity toward the relative starting phase of the target, while the clinical VMAT plan did not. This suggested that the IMRT plan experienced a greater degree of the MLC interplay effect than the VMAT plan. After several fractions, IMRT and VMAT dosimetric inaccuracies due to respiratory motion became less distinguished, most notably for the IMRT plan. Conclusion: In the context of stereotactic radiosurgery, in which fewer fractions are delivered, VMAT may be particularly advantageous to reduce discrepancies introduced by intra‐fraction motion.
MO‐D‐BRB‐07: Retrospective RapidArc Dose Reconstruction Based On MLC Dynamic and Delivery Log Files Recorded During Treatment36(2009); http://dx.doi.org/10.1118/1.3182217View Description Hide Description
Purpose: To develop a methodology for retrospectively reconstructing the dose delivered to head‐and‐neck (HN) patients in RapidArc treatment based on dynamic log‐files which record the actual leaf positions, gantry angles, and delivered monitor units (MUs) during the RapidArc delivery. Method and Materials: After a RapidArc treatment was finished, two dynamic log‐files were retrieved from the linear accelerator: (1) MLC log‐file which recorded the actual leaf positions and respective gantry angles every 50 ms and (2) delivery log‐file which recorded the actual delivered MUs and gantry angles at the control points defined in the RapidArc plan. Through the common parameter of gantry angle recorded for both dynamic log files, the actual delivery status such as leaf positions, delivered dose indices, and gantry angles for every control points were re‐constituted. This data was compiled and converted into a DICOM radiotherapy plan (RP) file using in‐house developed software written in MatLab code (Mathworks, Natick, MA). The DICOM RP file was then imported into Eclipse treatment planning system (Varian Medical Systems, Palo Alto, CA) and the actual delivered dose was reconstructed on the on‐treatment CBCT acquired for the patient. Results: A retrospective dose reconstruction procedure has been established for RapidArc and applied to a phantom and two dummy HN cases. For the case in which the tumor shrinkage is minimal, the reconstructed and planned doses were consistent to within 3–5% in high dose region. The DVHs of the target and other organs do not have significant differences. However, large dosimetric changes (10–15%) were observed for the case with tumor shrinkage, indicating the need for re‐planning or adaptive measure to be taken. Conclusion: RapidArc dose reconstruction provides a pragmatic way to probe the actual dose delivery at a particular fraction and represents an indispensable step toward adaptive radiotherapy for this highly conformal treatment.
MO‐D‐BRB‐08: A Hybrid Strategy of Offline Adaptive Planning and Online Image Guidance for Prostate Cancer Radiotherapy36(2009); http://dx.doi.org/10.1118/1.3182218View Description Hide Description
Purpose: In online image‐guidedradiotherapy of prostate cancer, the setup error and inter‐fractional motion is eliminated through pre‐treatment imaging and couch correction at each fraction. However, the rotation and deformation is not corrected and only accounted for in planning margin. In this study, we propose a hybrid of online and offline adaptive image‐guidance strategy for both low risk patients (LRP, CTV=prostate) and intermediate risk patients (IRP, CTV=prostate+seminal vesicles). The benefit of margin reduction is evaluated geometrically. Method and Materials: Planning and treatment helical CTimages from 25 patients over 412 fractions were used. Online image‐guidance was simulated by matching the center of mass of CTV in treatmentCT to planning CT. Offline replanning was performed by constructing the internal target volume (ITV) from the union of position‐corrected CTVs from the first 5 treatment CTs. The volume overlap index (OI) of ITV and CTVs of the remaining fractions were compared with the OIs between PTV from planning CT and the treatment CTVs. Margins from 0 to 10 mm were investigated. Results: The mean ITV volumes are 62.5 and 90.1 cm3 for LRP and IRP, respectively, equivalent to 0.8 and 1.0 mm uniform margin to CTV0 (mean volumes of 55.7 and 78.0 cm3). The margins needed for 99% OI for ITV (V99% = 67.9 and 105.5 cm3) are 1.7 mm and 2.6 mm less than those for the planning CTV (V99% = 84.5 and 144.7 cm3) for LRP and IRP, respectively. Conclusion: The hybrid of online and offline adaptive radiotherapy protocol can effectively account for the patient‐specific interfraction organ motion and setup errors for prostate cancer patients. The planning margin can be reduced further using the hybrid strategy compared with online image guidance alone. Reduced irradiated volume will also lead to decreased toxicity in critical organs.
36(2009); http://dx.doi.org/10.1118/1.3182219View Description Hide Description
Purpose: A planning study was performed to investigate the geometry‐based adaption of a step and shoot IMRT‐plan. Method and Materials: Six cases with large rectum and prostate deformations were selected. A 9 field IMRT‐plan (A) was planned on a first CT(CT1). The plan fulfilled all requirements for prostate IMRT in our clinic and its quality was comparable to a conventional high‐quality step and shoot IMRT plan. For a second CT(CT2), three plans were considered: the original plan with optimized isocentre position (B), a newly optimised plan (C) and the original plan, adapted using optimization rules (D), based on a geometry‐based concept called “2‐Step IMRT”. Several DVH‐parameters were utilized for quantification of plan quality: CTV D99, central PTV D95, V95 for an outer PTV, V80 and V50 for rectum and bladder. Results: Unlike B, D achieved almost the same target coverage as plan C. For the OARs, the rectum V80 was slightly increased for the original plan. The volume with more than 95% of the target dose was 1.5 ± 1.5 cm3 for C, compared to 2.2 ± 1.3 cm3 for A in CT1 and 7.2 ± 4.8 cm3 in CT2. D resulted in 4.3 ± 2.1 cm3, an intermediate dose load to the rectum. All other parameters were comparable for C and D in contrast to the results from B. Conclusion: The first results for adaptation using the 2‐Step IMRT algorithm are encouraging. The plans were superior to plans with optimised isocentre position B and only marginally worse than a newly optimized plan C. Computerisation is needed to accelerate the procedure, which is currently performed manually. Checks have to be developed to allow an ad‐hoc application of the adapted plan.
- Radiobiology I
TU‐C‐BRB‐01: Junior Investigator Winner: Outcome Prediction of Cervical Cancer: Kinetic Model of Tumor Regression During Radiation Therapy36(2009); http://dx.doi.org/10.1118/1.3182326View Description Hide Description
Purpose: A kinetic model on describing tumor response to radiation therapy (RT) was developed to analyze the tumor regression data and predict the RT outcome for cervical cancer.Methods and Materials: Eighty patients of cervical cancer (stages IB2‐IVA) completed four sequential magnetic resonance imaging(MRI) scans before, during and after RT. The median follow‐up time is 5.5 years (range 0.2–9.4 years). A kinetic model incorporating three major effects: radiation cell killing, dead‐cell resolving, and tumor repopulation, was developed to fit the volumetric regression data measured by serial MRI scans. The derived radiobiological parameters were correlated with long‐term clinical outcome.
Results: The tumor regression kinetics was significantly different for the two outcome groups: local control vs. local failure. The tumor regression rate and model parameters correlated significantly with the treatment outcome (p<0.001), with a median 2‐Gy surviving fraction (S2) of 0.65 and a median half‐time of dead‐cell resolving (T1/2) of 7.9 days for locally controlled tumors, vs. S2=0.70 and T1/2=18 days for locally recurrent tumors. The 6‐year local tumor control rate was 87% vs. 54% for patients with S2<0.70 vs. ⩾0.70 (p=0.001), and 95% vs. 57% for patients with T1/2 <22 days vs. ⩾22 days (p<0.001), respectively. Similarly the 6‐year disease‐free survival was 73% vs. 41% for the patients with S2<0.70 vs. ⩾0.70 (p=0.025), and 87% vs. 52% for patients with T1/2<22 days vs. ⩾22 days (p=0.002), respectively. Conclusion: The kinetic model well fits the temporal change of 3D tumor volume measured by serial MRI. Our data demonstrated that the locally recurrent tumors were not only radioresistant, but also had slow dead‐cell resolving probably due to the poor tumormicrocirculation. This approach shows promise for volume‐based tumor response modeling and potential to refine outcome prediction.
36(2009); http://dx.doi.org/10.1118/1.3182327View Description Hide Description
Purpose: To evaluate the the effect of total radiation dose received by cochlea on the risk of radiation induced sensory‐neural hearing loss (RI‐SNHL) using the Lyman NTCP model.Methods and Materials: A retrospective analysis for RI‐SNHL (audiology, otolaryngology, and radiation oncology records) of 410 patients (820 total ears) treated for selected treatment sites of head and neck cancers were performed. Due to the small volume of the organ, the dose volume analysis was not performed, instead the response was evaluated as a function of the average dose to cochlea. RI‐SNHL was defined as 10 dB loss at high freq (4 kHz) or low freq (average of frequencies between 0.5–2.0 kHz). The Lyman NTCP model was used to describe the dose response. Parameters of clinical significance were estimated using the maximum‐likelihood method with confidence intervals determined from the asymptotic covariance matrix (95% CI). Results: The average doses to cochlea varied between 0.5 – 81 Gy. The estimated values of TD50, m, and TD5 (calculated) for high frequency hearing loss were 63.1±1.9 Gy, 0.263±0.043, and 36.0 Gy and those for low frequency loss were 81.3±5.5, 0.168±0.041, and 59.0 Gy, respectively. The calculated γ50 for high and low frequency SNHL were 1.52 and 2.38, respectively. Conclusion: Lyman NTCP model fit to the RI‐SNHL data for high and low frequency hearing loss showed significant variation in pattern of incidence of hearing loss with increase in the dose to cochlea. The probality of incidence of RI‐SNHL at high frequency starts at lower dose (TD5 = 36 Gy) and increases relatively slowly (γ50 = 1.52) compared with that for low frequency RI‐SNHL (TD5 = 59 Gy, γ50 = 2.38). The NTCP model fit to the clinical data suggests that the dose to cochlea be limited to 36 Gy to reduce the possibility of any RI‐SNHL complications.
36(2009); http://dx.doi.org/10.1118/1.3182328View Description Hide Description
Purpose: To review the available clinical dose response data for extramedullary plasmacytomas (EMP) and solitary plasmacytomas of the bones (SPB), including standard 12 Gy TBI treatments for multiple myeloma (MM), to compute the expected dose response for plasma cell neoplasms and evaluate differences between EMP and SPB dose response. Method and Materials: Articles from 27 published studies on plasmacytomas were analyzed. Local control (LC) was used as the end point. Clinical data are often reported as LC for the median of a dose range — only data from ranges of width ⩽10 Gy were used. The maximum likelihood method (ML) was used to estimate the parameters of a tumour control probability (TCP) model based on Poisson statistics, and approximate likelihood confidence regions (CR) were determined. A Monte Carlo experiment (MC) assessed the parameters' uncertainty due to the 10 Gy dose interval. A statistical test based on the ability of the MC distributions of the parameters to discriminate between different kinds of tumors was performed. Results:Radiation therapy was used as the sole treatment in more than 70% of the patients and in 8 of the 12 studies selected. Parameters characterizing TCP and 95% confidence intervals from MC are reported, along with graphical representations of the dose response, and 2D MC histograms and the CRs on the parameter space. Conclusion: An extensive review of plasmacytoma clinical data was performed. Although the data suffer from a lack of low dose data and are mostly reported within a dose range, this approach is a preliminary assessment of dose response relationship for plasma cell neoplasms. The parameters of the TCP model were determined. Significant difference was seen between EMP and SPB dose response. The models could be used to interpolate clinical data and estimate TCP when assessing new therapies and comparing different treatment planning approaches.
TU‐C‐BRB‐04: Enhanced Modeling of Radiation Therapy for Head and Neck Cancers with Probabilistic Outcomes Using Mixed Predictors36(2009); http://dx.doi.org/10.1118/1.3182329View Description Hide Description
Purpose: To develop a probabilistic model of outcomes of radiation therapy which includes both dosimetric and non‐dosimetric predictors, and includes a decision‐making component to quantify the balance between disease cure and radiation‐induced side‐effects. This model was implemented to assess IMRTtreatment plans for individual patients for head and neck cancer.Materials and Methods: Physicians have available many resources that may not be easily reconcilable to predict patient outcomes. Dosimetric indicators, such as the EUD and NTCP are probabilistic in nature, without explicit representation of the underlying biology. Clinical trials focus on patient and disease characteristics, such as disease location, T‐stage, nodal involvement, Karnofsky performance status, and often include one treatment variable, such as DVH‐cutpoints or chemotherapy regimes. Newly recognized factors, such as HPV positivity, may affect outcome, however, without definitive clinical data, integrating such factors into clinical decision‐making is not straightforward. Finally, experience‐driven beliefs affect treatment choices and may vary between physicians. We combine all of the aforementioned resources using a Bayesian network in order to make an outcome prediction for each IMRT plan. Outcome predictions highlight the stark trade‐off between preventing recurrent disease that generally has a fatal prognosis and preventing radiation‐induced side effects that range from xerostemia to blindness to paralysis. We use a MarkovModel to compute a quality‐adjusted life expectancy using patient preferences for health states. Results: Probabilities of local and distant control matched published values well, as did life expectancies. The trade‐offs between quality of life and quantity of life are explored. Sensitivity analysis highlighted physician beliefs that affected treatment choices. Conclusions:Modeling of radiation therapy has grown progressively more sophisticated. We present a method by which probabilities and expected values of clinically relevant outcomes, based on a range of variables, are calculated.
TU‐C‐BRB‐05: Proton Relative Biological Effectiveness (RBE) Determined Using Monte Carlo DNA Damage Simulations36(2009); http://dx.doi.org/10.1118/1.3182330View Description Hide Description
Purpose Determine tissue‐specific proton relative biological effectiveness (RBE) as a function of fraction size, dose‐rate and position within the spread out Bragg peak (SOBP). Methods A general formula derived from the linear‐quadratic (LQ) model is developed to determine a dose‐weighted RBE for mixed radiation fields, including position‐dependent mixtures of protons in the SOBP. Biologically motivated formulas with just two biological parameters (q and q/k) are used to link trends in a and a/b to double strand break (DSB) induction. DSB induction for protons is determined using the Monte Carlo Damage Simulation (MCDS). Because q and q/k are independent or a weak function of radiation quality, cell‐ and tissue‐specific estimates of a and a/b for a reference radiation (e.g., x‐rays or 6 MV photons) can be used to determine cell and tissue‐specific radiosensitivity parameters for protons.Results For constant fraction size, proton RBE increases as a/b decreases. The approach predicts that the RBE for cell survival is always greater than or equal to the RBE for DSB induction. For very low energy protons (0.1 MeV), the proton RBE ranges from 3.2 for DSB induction to 12.3 for cell survival (a/b = 1.5 Gy). Near the proximal edge of the SOBP, the predicted RBE is close to unity. In the center of the SOBP, the dose‐weighted RBE ranges from about 1.13 (a/b = 10 Gy) to 1.15 (a/b = 1.5 Gy). The estimated RBE 5 mm from the Bragg peak ranges from 1.5 (a/b = 10 Gy) to 1.9 (a/b = 1.5 Gy). Conclusion The biologically motivated approach provides a convenient and useful formalism to help incorporate tissue‐, fraction‐size and position‐dependent RBE information into proton therapytreatment planning..
TU‐C‐BRB‐06: Modeling Tumor‐Volume Variation During Fractionated Radiotherapy for Non‐Small Cell Lung Cancer36(2009); http://dx.doi.org/10.1118/1.3182331View Description Hide Description
Purpose: To validate the four‐level population tumormodel using tumor volumetric changes obtained using on‐board imaging techniques during fractionated radiotherapy for non‐small‐cell lungcancer.Method and Materials: The four‐level population tumormodel is based on separation of tumor cell population into four subpopulations: 1) oxygenated viable cells, 2) oxygenated lethally damaged cells, 3) hypoxic viable cells, and 4) hypoxic lethally damaged cells. The oxygenated lethally damaged cells are removed from tumor using an exponential decay model. The hypoxic lethally damaged cells stay in tumor for unlimited time; therefore, their removal is governed by reoxygenation process. The model utilizes the following six radiobiological parameters: alpha, beta, potential doubling time Tpot, half‐life T1/2 of lethally damaged cells, initial hypoxic fraction R and reoxygenation rate A. To test the model, we use the clinical data on volumetric tumor changes during fractionated radiotherapy for non‐small‐cell lungcancer obtained using Tomotherapy and Cone‐Beam CT at different institutions. Results: Our preliminary data indicate that adenocarcinoma and squamous cell carcinoma demonstrate different rate of tumor volume variation after irradiation; therefore only cases have been selected where adenocarcinoma o squamous cell carcinoma diagnosis was available. Another problem of accurate tumor‐volume simulation for lungtumors is a significant hypoxic tumor fraction according to the experimental data obtained using fluoromisonidazole PET imaging. The hypoxic tumor fraction can be between 1.3% and 94.7.9% with a median value of 47.6%. Our model with average values of radiobiological parameters describes majority of lung squamous carcinoma cases. However, significant discrepancies have been observed between the model and clinical data for lung adenocarcinoma. Conclusions: The proposed radiobiological model with average values of parameters can be used for simulation of tumor‐volume for lung squamous cell carcinoma with acceptable accuracy. However, this approach does not describe the tumor volume variation for significant fraction of lung adenocarcinoma cases.
36(2009); http://dx.doi.org/10.1118/1.3182332View Description Hide Description
Aim Controlling interaction between tumor growth and vasculature development are the basis of anti‐angiogenic therapies. A model simulating tumor growth and angiogenesis was developed and the tumor oxygenation status was evaluated with respect to two parameters: tumorproliferation rate (TPR) and endothelial cell proliferation rate (EPR).Methods and Materials Our previous image‐basedmodel of vasculature was expanded by incorporating angiogenic development. The tissue volume encapsulating the tumor and vasculature was simulated as a MATLAB matrix. Vessels were simulated to sprout from the existing vasculature towards the tumor driven by hypoxia‐induced growth factors. The relative magnitude of TPR and EPR determined the simulation time steps. Xenografted and spontaneous tumors were simulated and the tumor oxygenation status was evaluated with respect to TPR & EPR. The model was verified against the experimental data obtained from CE‐CT and Cu‐ATSM PET imaging.Results The time‐dependant growth of vessels towards hypoxic regions in the tumor was demonstrated. TPR was found to have the most profound impact on development of hypoxia and heterogeneity of the oxygen concentration inside the tumor. Higher TPR led to faster development of hypoxia. Due to the limited vasculature available, xenografted tumors become hypoxic faster than spontaneous tumors for similar TPRs. When comparing simulations to the experiments, the overall development of vasculature and consequent hypoxia matched well with the imaging data. Conclusions A model capable of simulating temporal development of tumor and vasculature has been developed and tested on the imaging‐derived experimental data. The presented model has a potential to study the response of tumor and the vasculature to therapy, which can be a valuable input for optimizing anti‐angiogenic therapies.
TU‐C‐BRB‐08: Validating Normal Tissue Complication Probability Models: A Study of Generalizability and Datapooling for Predictive Radiation Pneumonitis Modeling36(2009); http://dx.doi.org/10.1118/1.3182333View Description Hide Description
Purpose: NTCP models usually do not undergo needed tests of external validation or generalizability for eventual clinical use. Here we test and generalize an NTCP model for predicting radiation pneumonitis (RP) using data‐pooling with institutional data and a multi‐institutional dataset (RTOG 93‐11 data). As accurate dosimetry is unavailable for the RTOG dataset, non‐heterogeneity corrected dose distributions were used throughout. Methods and Materials: Data consisted of 313 patients who received definitive conformal radiotherapy for non‐small‐cell lungcancer (209 from WUSTL, 104 from the RTOG 93‐11 trial). For each individual subset patient groups, heart and lungdosimetric variables (abstracted from corresponding treatment planningarchives), existing clinical factors, and tumor/high‐dose positional parameters are included in multivariate logistic regression modeling to obtain the most predictive multi‐variable NTCP model. Cross‐validation and bootstrapping methods were used confirm internal model validity, model stability, and optimal model size. The derived best model for each dataset was tested against the other dataset. Finally, modeling over the combined multi‐instructional dataset (RTOG 93‐11 trial and WUSTL datasets) was conducted and the resulting model was tested against each individual subset of data. Results:Models derived on the separate datasets performed poorly on the other dataset. However, the best model derived on the full dataset included D10_heart, D10_lung, and center‐of‐GTV‐mass‐superior‐inferior. which performed well over each individual subset. Conclusion: These results demonstrate the unique role that full 3‐D data‐pooling can play: models can be derived that work across a wide spectrum of patient datasets, but the actual target populations should be included in the analysis.
Supported by NIH grant R01 CA85181.
TU‐C‐BRB‐09: Estimate of the Uncertainty in Relative Secondary Cancer Risk Calculations Following Proton Therapy and Intensity Modulated X‐Ray Therapy36(2009); http://dx.doi.org/10.1118/1.3182334View Description Hide Description
Purpose: To determine the uncertainty in calculations of the ratio of relative risk (RRR) of developing a secondary malignant neoplasm (SMN) from proton therapy compared to that of IMXT for prostate cancer by examining the sensitivity to key input parameters. Methods and Materials: The RRR and associated uncertainties were examined for a typical patient treated for early‐stage prostate cancer.Proton therapy and IMXT plans were developed using clinical protocols. Baseline risk of SMN was estimated using primary doses from treatment planning calculations, stray doses from Monte Carlo simulations and available data, radiation weighting factors (w R) values from ICRP Publication 92, and the linear no‐threshold model for SMN risk. The sensitivity of RRR to w R was estimated by re‐computing the RRR using various scaled w R values. The influence of uncertainties in the dose‐responsemodel was estimated by re‐computing the RRR with various available models. The influence of inter‐patient variation in primary dose was determined using the standard deviation of mean dose in organs at risk over a patient population, while variation in stray dose was determined with Monte Carlo simulations.Results: The baseline RRR for the selected patient was 0.66, suggesting that proton therapy can reduce the calculated incidence of SMN by 34% compared with IMXT. Changes in w R and the shape of the dose‐responsemodel introduced uncertainties of ±10%, and ±7%, respectively. Inter‐patient variations in primary and stray dose produced RRR values that deviated greatly from the baseline value; however, a strong positive correlation coefficient was observed, resulting in a net uncertainty in the baseline RRR of ±30%. Thus, the total uncertainty in the baseline RRR was 0.22 (2‐σ). Conclusion: Proton therapy can reduce the incidence of SMN compared with IMXT for a population of prostate patients, independent of uncertainties in w R and the shape of the dose response model.
- Measurements I
TU‐D‐BRB‐01: Comparison of the Response of EBT Emulsion to 90Sr/Y Beta Particles and 60Co Gamma Rays36(2009); http://dx.doi.org/10.1118/1.3182367View Description Hide Description
Purpose: To test the hypothesis that GAFCHROMIC EBT emulsion responds the same to beta particles from and gamma rays from . Method and Materials: Single emulsion layer EBT film with minimal covering layer was obtained and 1 cm2 pieces were irradiated, 1) with the center of the film emulsion at a depth of 7 mg/cm2 in a reference radiation field of known absorbed dose rate (0.0911 mGy/s), or 2) with the films located at a depth of 5 cm in water in a gamma ray beam of known absorbed dose rate (2.67 mGy/s). Six samples were irradiated in each radiation field at 15 logarithmically evenly spaced absorbed dose levels ranging from 30 mGy to 7 Gy. For the films irradiated in water, a commercially available food sealer was used to vacuum seal films in water‐proof packs which were held perpendicular to the beam axis with a spring‐loaded mounting jig. Irradiated films were read out between 6 and 9 days post irradiation in a 48‐bit color photo scanner and the red component of the TIFF image data was extracted for analysis of the average optical density. Results: The average net optical density change per unit of absorbed dose delivered was evaluated for each of the 15 dose levels used in the study. Using the uncertainty in the delivered absorbed dose for each radiation (1.5% for and 1% for , both 1σ) and the statistics of the six film replicates, a two‐sided Student's t test was applied and no difference in the means in the net responses per unit absorbed dose was found at any of the applied absorbed dose levels. Conclusion: Within the measurement uncertainties (∼4% at 1σ), the response of the EBT emulsion to beta particles from and gamma rays from is the same.
TU‐D‐BRB‐02: Advanced Techniques to Determine Plan‐Class Specific Reference Field Correction Factors for Accurate Dosimetry of Nonstandard Beams36(2009); http://dx.doi.org/10.1118/1.3182368View Description Hide Description
Purpose: To establish experimental reference dosimetry techniques for measuring plan‐class specific reference field correction factors for the proposed new formalism for reference dosimetry in nonstandard fields. Methods and Materials: A Lucite cylindrical phantom filled with water was constructed in the center of which reference absorbed dose to water for an IMRT delivery was measured. A plan‐class specific reference (pcsr) field for a typical head and neck IMRT delivery was created on CTimages of the phantom. The absorbed dose in the pcsr field normalized to that in a 10×10 cm2 field was measured using three reference dosimeters: Gafchromic® EBT films, a diamonddetector, and an in‐house developed guarded liquid ionization chamber (GLIC‐03). Pcsr correction factors were determined for five air‐filled ionization chambers (Exradin A1SL, Exradin A12, Exradin A14, PinPoint® 31006, and NE2571) in a fully‐rotated delivery and in a delivery from a single angle (collapsed delivery). Results: The relative dose measurement accuracy of the three dosimeters was 0.56%, 0.10%, and 0.29% for the film,diamonddetector, and GLIC‐03, respectively. The combined relative standard uncertainty in measuring using the three techniques was 0.3%. For all chambers and the pcsr field selected, was unity to within ±1% and in the range of 0.990–0.993 and 0.990–1.003 in the fully‐rotated and collapsed deliveries, respectively. The correction factors were the same for the chambers in the fully‐rotated delivery. In the collapsed delivery, the Farmer‐type chambers (Exradin A12 and NE2571) had a larger but consistent correction factor (0.990 and 0.991, respectively). The correction factors for the smaller chambers were close to unity but showed chamber type dependence. Conclusions: Our techniques provide a potential to improve the dosimetric accuracy in suitable plan‐class specific reference fields. The techniques of determining the correction factor will be extremely valuable for other nonstandard field deliveries.