bluuebunny/crossref_metadata_2025_split 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.