bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐E‐J‐228: MRI‐Based Planning: Dosimetric Feasibility of Dose Painting for ADCDefined Intra‐Prostatic Tumor
Authors: Chen X , Dalah E , Prior P , Lawton C , Li X 
Published: June 2015

Purpose:Apparent diffusion coefficient (ADC) map may help to delineate the gross tumor volume (GTV) in prostate gland. Dose painting with external beam radiotherapy for GTV might increase the local tumor control. The purpose of this study is to explore the maximum boosting dose on GTV using VMAT without sacrificing sparing of organs at risk (OARs) in MRI based planning.Methods:VMAT plans for 5 prostate patients were generated following the commonly used dose volume (DV) criteria based on structures contoured on T2 weighted MRI with bulk electron density assignment using electron densities derived from ICRU46. GTV for each patient was manually delineated based on ADC maps and fused to T2‐weighted image set for planning study. A research planning system with Monte Carlo dose engine (Monaco, Elekta) was used to generate the VMAT plans with boosting dose on GTV gradually increased from 85Gy to 100Gy. DV parameters, including V(boosting‐dose) (volume covered by boosting dose) for GTV, V75.6Gy for PTV, V45Gy, V70Gy, V72Gy and D1cc (Maximum dose to 1cc volume) for rectum and bladder, were used to measure plan quality.Results:All cases achieve at least 99.0% coverage of V(boosting‐dose) on GTV and 95% coverage of V75.6Gy to the PTV. All the DV criteria, V45Gy≤50% and V70Gy≤15% for bladder and rectum, D1cc ≤77Gy (Rectum) and ≤80Gy (Bladder), V72Gy≤5% (rectum and bladder) were maintained when boosting GTV to 95Gy for all cases studied. Except for two patients, all the criteria were also met when the boosting dose goes to 100Gy. Conclusion: It is dosimetrically feasible safe to boost the dose to at least 95Gy to ADC defined GTV in prostate cancer using MRI guided VMAT delivery.Conclusion:It is dosimetrically feasible safe to boost the dose to at least 95Gy to ADC defined GTV in prostate cancer using MRI guided VMAT delivery.This research is partially supported by Elekta Inc.
SU‐E‐J‐242: Volume‐Dependence of Quantitative Imaging Features From CT and CE‐CT Images of NSCLC
Authors: Fave X , Fried D , Zhang L , Yang J , Balter P , Followill D , Gomez D , Jones A , Stingo F , Court L 
Published: June 2015

Purpose:To determine whether tumor volume plays a significant role in the values obtained for texture features when they are extracted from computed tomography (CT) images of non‐small cell lung cancer (NSCLC). We also sought to identify whether features can be reliably measured at all volumes or if a minimum volume threshold should be recommended.Methods:Eleven features were measured on 40 CT and 32 contrast‐enhanced CT (CECT) patient images for this study. Features were selected for their prognostic/diagnostic value in previous publications. Direct correlations between these textures and volume were evaluated using the Spearman correlation coefficient. Any texture that the Wilcoxon rank‐sum test was used to compare the variation above and below a volume cutoff. Four different volume thresholds (5, 10, 15, and 20 cm3) were tested.Results:Four textures were found to be significantly correlated with volume in both the CT and CE‐CT images. These were busyness, coarseness, gray‐level nonuniformity, and run‐length nonuniformity with correlation coefficients of 0.92, −0.96, 0.94, and 0.98 for the CT images and 0.95, −0.97, 0.98, and 0.98 for the CE‐CT images. After volume normalization, the correlation coefficients decreased substantially. For the data obtained from the CT images, the results of the Wilcoxon rank‐sum test were significant when volume thresholds of 5–15 cm3 were used. No volume threshold was shown to be significant for the CE‐CT data.Conclusion:Equations for four features that have been used in several published studies were found to be volume‐dependent. Future studies should consider implementing normalization factors or removing these features entirely to prevent this potential source of redundancy or bias.This work was supported in part by National Cancer Institute grant R03CA178495‐01. Xenia Fave is a recipient of the American Association of Physicists in Medicine Graduate Fellowship.
SU‐E‐J‐247: Time Evolution of Radiation‐Induced Lung Injury After Stereotactic Proton Therapy
Authors: Grassberger C , Sharp G , Fintelmann F , Paganetti H , Willers H 
Published: June 2015

Purpose:Quantitative metrics to assess patient‐specific radiation‐induced lung injury have the potential to guide individualization of therapy and be early indicators of recurrence. Here we investigate computed tomography (CT) density changes in normal lung after stereotactic Proton Therapy.Methods:Participants in a phase‐I clinical trial for stereotactic body radiation therapy (SBRT) with protons are analyzed on a rolling basis. The dataset includes 9 patients with 34 CT images to date. Follow‐up images are registered to the planning CT using deformable image registration and the change in CT density is correlated to the dose to examine the time‐evolution of Hounsfield Unit (HU) changes after large doses of proton radiation.Results:The lung density observed on the follow‐up images increases significantly with dose for all dose levels above 5 Gy(RBE) (p<0.001) for 8/9 patients. The change per unit dose [HU/Gy] varies significantly among the patients, from 0.1 (for the one patient without significant correlation) to 5.7 ΔHU/Gy(RBE). The current population average of ΔHU/Gy(RBE) is 2.1, i.e. a 1 Gy(RBE) increase in dose leads on average to a 2.1 HU increase in CT density. The slope of the dose‐response curve is constant for all timepoints investigated (from 3–24+ months). Additionally a pronounced non‐linearity in the dose response curve is noted for long follow‐up times (>18 months).Conclusion:CT density changes have a robust correlation with proton dose, quantitatively similar to photon dose, and may allow estimation of a patient's intrinsic radiosensitivity after proton therapy. The stability of the correlation with time however diverges from what is known about CT response after photon irradiation. This could have important implications for clinical decision‐making during proton therapy for lung cancer, especially for scheduling of follow‐up CT/PET imaging and diagnosis of recurrence.
SU‐E‐J‐253: The Radiomics Toolbox in the Computational Environment for Radiological Research (CERR)
Authors: Apte A , Veeraraghavan H , Oh J , Kijewski P , Deasy J 
Published: June 2015

Purpose:To present an open source and free platform to facilitate radiomics research — The “Radiomics toolbox” in CERR.Method:There is scarcity of open source tools that support end‐to‐end modeling of image features to predict patient outcomes. The “Radiomics toolbox” strives to fill the need for such a software platform. The platform supports (1) import of various kinds of image modalities like CT, PET, MR, SPECT, US. (2) Contouring tools to delineate structures of interest. (3) Extraction and storage of image based features like 1st order statistics, gray‐scale co‐occurrence and zonesize matrix based texture features and shape features and (4) Statistical Analysis. Statistical analysis of the extracted features is supported with basic functionality that includes univariate correlations, Kaplan‐Meir curves and advanced functionality that includes feature reduction and multivariate modeling. The graphical user interface and the data management are performed with Matlab for the ease of development and readability of code and features for wide audience. Open‐source software developed with other programming languages is integrated to enhance various components of this toolbox. For example: Java‐based DCM4CHE for import of DICOM, R for statistical analysis.Results:The Radiomics toolbox will be distributed as an open source, GNU copyrighted software. The toolbox was prototyped for modeling Oropharyngeal PET dataset at MSKCC. The analysis will be presented in a separate paper.Conclusion:The Radiomics Toolbox provides an extensible platform for extracting and modeling image features. To emphasize new uses of CERR for radiomics and image‐based research, we have changed the name from the “Computational Environment for Radiotherapy Research” to the “Computational Environment for Radiological Research”.
SU‐E‐J‐261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features
Authors: Zhang L , Fried D , Fave X , Mackin D , Yang J , Court L 
Published: June 2015

Purpose:To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature's differentiating capability.Methods:Twenty‐seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre‐treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre‐treatment PET images (non‐necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between two groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2‐tailed T‐test was used to evaluate the differentiating capability of CT features on pre‐treatment PT necrosis.Result:Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary‐adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity was significantly lower in the necrosis group (p<0.05).Conclusion:Appropriate selection of image preprocessing techniques allows radiomics features to extract more useful information and thereby improve prediction models based on these features.
SU‐E‐J‐266: Cone Beam Computed Tomography (CBCT) Inter‐Scan and Inter‐Observer Tumor Volume Variability Assessment in Patients Treated with Stereotactic Body Radiation Therapy (SBRT) for Early Stage Non‐Small Cell Lung Cancer (NSCLC)
Authors: Hou Y , Aileen C , Kozono D , Killoran J , Wagar M , Lee S , Hacker F , Aerts H , Lewis J , Mak R 
Published: June 2015

Purpose:Quantification of volume changes on CBCT during SBRT for NSCLC may provide a useful radiological marker for radiation response and adaptive treatment planning, but the reproducibility of CBCT volume delineation is a concern. This study is to quantify inter‐scan/inter‐observer variability in tumor volume delineation on CBCT.Methods:Twenty earlystage (stage I and II) NSCLC patients were included in this analysis. All patients were treated with SBRT with a median dose of 54 Gy in 3 to 5 fractions. Two physicians independently manually contoured the primary gross tumor volume on CBCTs taken immediately before SBRT treatment (Pre) and after the same SBRT treatment (Post). Absolute volume differences (AVD) were calculated between the Pre and Post CBCTs for a given treatment to quantify inter‐scan variability, and then between the two observers for a given CBCT to quantify inter‐observer variability. AVD was also normalized with respect to average volume to obtain relative volume differences (RVD). Bland‐Altman approach was used to evaluate variability. All statistics were calculated with SAS version 9.4.Results:The 95% limit of agreement (mean ± 2SD) on AVD and RVD measurements between Pre and Post scans were −0.32cc to 0.32cc and −0.5% to 0.5% versus −1.9 cc to 1.8 cc and −15.9% to 15.3% for the two observers respectively. The 95% limit of agreement of AVD and RVD between the two observers were −3.3 cc to 2.3 cc and −42.4% to 28.2% respectively. The greatest variability in inter‐scan RVD was observed with very small tumors (< 5 cc).Conclusion:Inter‐scan variability in RVD is greatest with small tumors. Inter‐observer variability was larger than inter‐scan variability. The 95% limit of agreement for inter‐observer and inter‐scan variability (∼15–30%) helps define a threshold for clinically meaningful change in tumor volume to assess SBRT response, with larger thresholds needed for very small tumors.Part of the work was funded by a Kaye award; Disclosure/Conflict of interest: Raymond H. Mak: Stock ownership: Celgene, Inc. Consulting: Boehringer‐Ingelheim, Inc.
SU‐E‐J‐268: Change of CT Number During the Course of Chemoradiation Therapy for Pancreatic Cancer
Authors: Chen X , Zhang J , Dalah E , Liu F , Li X 
Published: June 2015

Purpose:It has been observed radiation can induce changes in CT number (CTN) inside tumor during the course of radiation therapy (RT) for several tumor sites including lung and head and neck, suggesting that the CTN change may be potentially used to assess RT response. In this study, we investigate the CTN changes inside tumor during the course of chemoradiation therapy (CRT) for pancreatic cancer.Methods:Daily diagnostic‐quality CT data acquired during IGRT for 17 pancreatic head cancer patients using an in‐room CT (CTVision, Siemens) were analyzed. All patients were treated with a radiation dose of 50.4 in 1.8 Gy per fraction. On each daily CT set, The contour of the pancreatic head, included in the treatment target, was generated by populating the pancreatic head contour from the planning CT or MRI using an auto‐segmentation tool based on deformable registration (ABAS, Elekta) with manual editing if necessary. The CTN at each voxel in the pancreatic head contour was extracted and the 3D distribution of the CTNs was processed using MATLAB. The mean value of CTN distribution was used to quantify the daily CTN change in the pancreatic head.Results:Reduction of CTN in pancreatic head was observed during the CRT delivery in 14 out the 17 (82%) patients studied. Although the average reduction is only 3.5 Houncefield Unit (HU), this change is significant (p<0.01). Among them, there are 7 patients who had a CTN drop larger than 5 HU, ranging from 6.0 to 11.8 HU. In contrast to this trend, CTN was increased in 3 patients.Conclusion:Measurable changes in the CT number in tumor target were observed during the course of chemoradiation therapy for the pancreas cancer patients, indicating this radiation‐induced CTN change may be used to assess treatment response.
SU‐E‐J‐272: Auto‐Segmentation of Regions with Differentiating CT Numbers for Treatment Response Assessment
Authors: Yang C , Gilat‐Schmidt T , Noid G , Dalah E , Paulson E , Li X 
Published: June 2015

Purpose:It has been reported recently that the change of CT number (CTN) during and after radiation therapy (RT) may be used to assess RT response. The purpose of this work is to develop a tool to automatically segment the regions with differentiating CTN and/or with change of CTN in a series of CTs.Methods:A software tool was developed to identify regions with differentiating CTN using K‐mean Cluster of CT numbers and to automatically delineate these regions using convex hull enclosing method. Pre‐ and Post‐RT CT, PET, or MRI images acquired for sample lung and pancreatic cancer cases were used to test the software tool. K‐mean cluster of CT numbers within the gross tumor volumes (GTVs) delineated based on PET SUV (standard uptake value of fludeoxyglucose) and/or MRI ADC (apparent diffusion coefficient) map was analyzed. The cluster centers with higher value were considered as active tumor volumes (ATV). The convex hull contours enclosing preset clusters were used to delineate these ATVs with color washed displays. The CTN defined ATVs were compared with the SUV‐ or ADC‐defined ATVs.Results:CTN stability of the CT scanner used to acquire the CTs in this work is less than 1.5 Hounsfield Unit (HU) variation annually. K‐mean cluster centers in the GTV have difference of ∼20 HU, much larger than variation due to CTN stability, for the lung cancer cases studied. The dice coefficient between the ATVs delineated based on convex hull enclosure of high CTN centers and the PET defined GTVs based on SUV cutoff value of 2.5 was 90(±5)%.Conclusion:A software tool was developed using K‐mean cluster and convex hull contour to automatically segment high CTN regions which may not be identifiable using a simple threshold method. These CTN regions were reasonably overlapped with the PET or MRI defined GTVs.
SU‐E‐J‐274: Responses of Medulloblastoma Cells to Radiation Dosimetric Parameters in Intensity‐Modulated Radiation Therapy
Authors: Park J , Park J , Rogalla S , Woo D , Lee D , Park H , Contag C , Suh T 
Published: June 2015

Purpose:To evaluate radiation responses of the medulloblastoma cell line Daoy in intensity‐modulated radiation therapy (IMRT), quantitative variations to variable radiation dosimetic parameters were tracked by bioluminescent images (BLIs).Methods:The luciferase and green fluorescent protein positive Daoy cells were cultured on dishes. The medulloblastoma cells irradiated to different dose rate, interval of fractionated doses, field margin and misalignment, and dose uniformity in IMRT were monitored using bioluminescent images. The cultured cells were placed into a dedicated acrylic phantom to deliver intensity‐modulated fluences and calculate accurate predicted dose distribution. The radiation with dose rate from 0.5 Gy/min to 15 Gy/min was irradiated by adjusting monitor unit per minute and source‐to‐surface distances. The intervals of fractionated dose delivery were changed considering the repair time of double strand breaks (DSB) revealed by straining of gamma‐H2AX.The effect of non‐uniform doses on the cells were visualized by registering dose distributions and BLIs. The viability according to dosimetric parameters was correlated with bioluminescent intensities for cross‐check of radiation responses.Results:The DSB and cell responses due to the first fractionated dose delivery significantly affected final tumor control rather than other parameters. The missing tumor volumes due to the smaller field margin than the tumor periphery or field misalignment caused relapse of cell responses on BLIs. The dose rate and gradient had effect on initial responses but could not bring out the distinguishable killing effect on cancer cells.Conclusion:Visualized and quantified bioluminescent images were useful to correlate the dose distributions with spatial radiation effects on cells. This would derive the effective combination of dose delivery parameters and fractionation. Radiation responses in particular IMRT configuration could be reflected to image based‐dose re‐optimization.
SU‐E‐T‐03: 3 Year Clinical Experience with the IBA Proton Eyeline
Authors: Kryck E , Mamalui‐Hunter M , Li Z , Slopsema R 
Published: June 2015

Purpose:Over the past 3 years, more than 50 patients with ocular disease have been treated with a prototype commercial eyeline developed by Ion Beam Applications (IBA). It is particularly crucial for the dosimetry of the machine to be accurate, precise, and reproducible because of the small treatment volumes and several surrounding critical structures. A quality assurance (QA) program has been developed in order to regularly check and record the machine's dosimetry over time. In this study we have summarized and evaluated three years of patient and system QA data for the eyeline prototype.Methods:In order to accurately deliver the prescribed dose to the patient's treatment site, an output model was created based on several machine parameters. This output model, along with the requested system parameters, are regularly checked and recorded by a QA program. 10 standard proton fields have been created which have regular QA performed monthly with a water tank and parallel‐plate chamber, and recorded in a database. QA has additionally been performed for each patient‐specific treatment field. This is to verify and evaluate the patient output model as well as the system parameters for range&modulation combinations different than the standard QA fields.Results:140 beam measurements have been analyzed for range, modulation, and output. The range is typically within tolerance, although there are about 6 cases where the system did not perform as expected. The modulation appears to be very stable, always being within tolerance. Output for the standard QA fields are typically within 2.5% from the baseline value. The patient output measurements agree within 2.5% from the model.Conclusion:Overall the eyeline performs well within the tolerances set, and the patient output measurements agree within 2% of the model.
SU‐E‐T‐06: 4D Particle Swarm Optimization to Enable Lung SBRT in Patients with Central And/or Large Tumors
Authors: Modiri A , Gu X , Hagan A , Sawant A 
Published: June 2015

Purpose:Patients presenting with large and/or centrally‐located lung tumors are currently considered ineligible for highly potent regimens such as SBRT due to concerns of toxicity to normal tissues and organs‐at‐risk (OARs). We present a particle swarm optimization (PSO)‐based 4D planning technique, designed for MLC tracking delivery, that exploits the temporal dimension as an additional degree of freedom to significantly improve OAR‐sparing and reduce toxicity to levels clinically considered as acceptable for SBRT administration.Methods:Two early‐stage SBRT‐ineligible NSCLC patients were considered, presenting with tumors of maximum dimensions of 7.4cm (central‐right lobe; 1.5cm motion) and 11.9cm (upper‐right lobe; 1cm motion). In each case, the target and normal structures were manually contoured on each of the ten 4DCT phases. Corresponding ten initial 3D‐conformal plans (Pt#1: 7‐beams; Pt#2: 9‐beams) were generated using the Eclipse planning system. Using 4D‐PSO, fluence weights were optimized over all beams and all phases (70 and 90 apertures for Pt1&2, respectively). Doses to normal tissues and OARs were compared with clinicallyestablished lung SBRT guidelines based on RTOG‐0236.Results:The PSO‐based 4D SBRT plan yielded tumor coverage and dose—sparing for parallel and serial OARs within the SBRT guidelines for both patients. The dose‐sparing compared to the clinically‐delivered conventionallyfractionated plan for Patient 1 (Patient 2) was: heart Dmean = 11% (33%); lung V20 = 16% (21%); lung Dmean = 7% (20%); spinal cord Dmax = 5% (16%); spinal cord Dmean = 7% (33%); esophagus Dmax = 0% (18%).Conclusion:Truly 4D planning can significantly reduce dose to normal tissues and OARs. Such sparing opens up the possibility of using highly potent and effective regimens such as lung SBRT for patients who were conventionally considered SBRT non‐eligible. Given the large, non‐convex solution space, PSO represents an attractive, parallelizable tool to successfully achieve a globally optimal solution for this problem.This work was supported through funding from the National Institutes of Health and Varian Medical Systems.