bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐E‐J‐265: Feasibility Study of Texture Analysis for Prognosis of Local Tumor Recurrence Within 5‐Years for Pharyngeal‐Laryngeal Carcinoma Patients Received Radiotherapy Treatment
Authors: Huang W , Tu S 
Published: June 2015

Purpose:Pharyngeal and laryngeal carcinomas (PLC) are among the top leading cancers in Asian populations. Typically the tumor may recur and progress in a short period of time if radiotherapy fails to deliver a successful treatment. Here we used image texture features extracted from images of computed tomography (CT) planning and conducted a retrospective study to evaluate whether texture analysis is a feasible approach to predict local tumor recurrence for PLC patients received radiotherapy treatment.Methods:CT planning images of 100 patients with PLC treated by radiotherapy at our facility between 2001 and 2010 are collected. These patients were received two separate CT scans, before and mid‐course of the treatment delivery. Before the radiotherapy, a CT scanning was used for the first treatment planning. A total of 30 fractions were used in the treatment and patients were scanned with a second CT around the end of the fifteenth delivery for an adaptive treatment planning. Only patients who were treated with intensity modulated radiation therapy and RapidArc were selected. Treatment planning software of Eclipse was used. The changes of texture parameters between two CT acquisitions were computed to determine whether they were correlated to the local tumor recurrence. The following texture parameters were used in the preliminary assessment: mean, variance, standard deviation, skewness, kurtosis, energy, entropy, inverse difference moment, cluster shade, inertia, cluster prominence, gray‐level co‐occurrence matrix, and gray‐level run‐length matrix. The study was reviewed and approved by the committee of our institutional review board.Results:Our calculations suggested the following texture parameters were correlated with the local tumor recurrence: skewness, kurtosis, entropy, and inertia (p<0.0.05).Conclusion:The preliminary results were positive. However some works remain crucial to be completed, including addition of texture parameters for different image features, sensitivity of tumor segmentation variations, and effect of image filtering.
SU‐E‐J‐267: Change in Mean CT Intensity of Lung Tumors During Radiation Treatment
Authors: Mahon R , Tennyson N , Weiss E , Hugo G 
Published: June 2015

Purpose:To evaluate CT intensity change of lung tumors during radiation therapy.Methods:Repeated 4D CT images were acquired on a CT simulator during the course of therapy for 27 lung cancer patients on IRB approved protocols. All subjects received definitive radiation treatment ± chemotherapy. CT scans were completed prior to treatment, and 2–7 times during the treatment course. Primary tumor was delineated by an experienced Radiation Oncologist. Contours were thresholded between −100 HU and 200 HU to remove airways and bone. Correlations between the change in the mean tumor intensity and initial tumor intensity, SUVmax, and tumor volume change rate were investigated. Reproducibility was assessed by evaluating the variation in mean intensity over all phases in 4DCT, for a subgroup of 19 subjects.Results:Reproducibility of tumor intensity between phases as characterized by the root mean square of standard deviation across 19 subjects was 1.8 HU. Subjects had a mean initial tumor intensity of 16.5 ± 11.6 HU and an overall reduction in HU by 10.3 ± 8.5 HU. Evaluation of the changes in tumor intensity during treatment showed a decrease of 0.3 ± 0.3 HU/day for all subjects, except three. No significant correlation was found between change in HU/day and initial HU intensity (p=0.53), initial PET SUVmax (p=0.69), or initial tumor volume (p=0.70). The rate of tumor volume change was weakly correlated (R^2=0.05) with HU change (p=0.01).Conclusion:Most lung cancer subjects showed a marked trend of decreasing mean tumor CT intensity throughout radiotherapy, including early in the treatment course. Change in HU/day is not correlated with other potential early predictors for response, such as SUV and tumor volume change. This Result supports future studies to evaluate change in tumor intensity on CT as an early predictor of response.
SU‐E‐J‐269: Tracking of Tumor Regression for Stage III Lung Cancer Using CBCT
Authors: Kang K , Biswas T , Podder T 
Published: June 2015

Purpose:This study is to evaluate the tumor regression over the course of EBRT treatment and to determine the difference of tumor reduction for stage III lung squamous cell cancer (SCC) and adenocarcinoma using CBCT.Methods:Twenty three stage III lung cancer patients treated in our clinic who had daily cone beam CT (CBCT) were selected for this study (16 adenocarcinoma and 7 SCC cases). Patients received prescription dose in the range of 50Gy–71.4Gy (mean =60.3Gy, median =50Gy) at 1.8Gy or 2Gy per fraction. Treatments spanned over a minimum of five weeks. Initial mean volume of the gross tumor volume (GTV) was 123cc (range = 14.7cc–353.3cc). For this study, we choose six sets of CBCTs at an interval of one week, starting from the first fraction of treatment. Daily CBCTs from treatment linac computer were transferred to MIM Software version 6.0. An experienced physician contoured the primary GTV on each slices of the CBCT for these patients.Results:A consistent regression of the GTVs was observed in all patients, except in one patient (adeno case) where GTV did not change. Weekly volumetric reduction was in the range of 11.2%–16.6%. Maximum reductions were noticed in the first two weeks of the treatment cycle; mean overall (for adeno+SCC) reductions were 16.6%, 14.2% in week‐1 and week‐2, respectively. Mean reduction over five weeks of treatment was 49.8% (range = 0.1%–75.5%). Higher reduction was observed in SCC patients as compare to adenocarcinoma cases (54.9% vs. 47.6%); however, the difference was not statistically significant (p‐value > 0.05).Conclusion:Large regression of tumors over the course of EBRT for stage III lung cancer patients was observed. Both SCC and adenocarcinoma responded well; overall reduction for SCC cases was higher. A future study is warranted for determining the co‐relation between tumor volume reduction and treatment outcome.
SU‐E‐J‐270: Repeated 18F‐FDG PET/CTs Based Feature Analysis for the Predication of Anal Cancer Recurrence
Authors: Wang J , Chuong M , Latifi K , Saeed N , Tan S , Choi W , Hoffe S , Shridhar R , Moros E , Lu W 
Published: June 2015

Purpose:To identify PET/CT based imaging predictors of anal cancer recurrence and evaluate baseline vs. mid‐treatment vs. post‐treatment PET/CT scans in the tumor recurrence prediction.Methods:FDG‐PET/CT scans were obtained at baseline, during chemoradiotherapy (CRT, midtreatment), and after CRT (post‐treatment) in 17 patients of anal cancer. Four patients had tumor recurrence. For each patient, the mid‐treatment and post‐treatment scans were respectively aligned to the baseline scan by a rigid registration followed by a deformable registration. PET/CT image features were computed within the manually delineated tumor volume of each scan to characterize the intensity histogram, spatial patterns (texture), and shape of the tumors, as well as the changes of these features resulting from CRT. A total of 335 image features were extracted. An Exact Logistic Regression model was employed to analyze these PET/CT image features in order to identify potential predictors for tumor recurrence.Results:Eleven potential predictors of cancer recurrence were identified with p < 0.10, including five shape features, five statistical texture features, and one CT intensity histogram feature. Six features were indentified from posttreatment scans, 3 from mid‐treatment scans, and 2 from baseline scans. These features indicated that there were differences in shape, intensity, and spatial pattern between tumors with and without recurrence. Recurrent tumors tended to have more compact shape (higher roundness and lower elongation) and larger intensity difference between baseline and follow‐up scans, compared to non‐recurrent tumors.Conclusion:PET/CT based anal cancer recurrence predictors were identified. The post‐CRT PET/CT is the most important scan for the prediction of cancer recurrence. The baseline and mid‐CRT PET/CT also showed value in the prediction and would be more useful for the predication of tumor recurrence in early stage of CRT.This work was supported in part by the National Cancer Institute Grant R01CA172638.
SU‐E‐J‐271: Correlation of CT Number Change with Radiation Treatment Response for Pancreatic Cancer
Authors: Dalah E , Tai A , Oshima K , Hall W , Knechtges P , Erickson B , Li X 
Published: June 2015

Purpose:It has been reported recently that radiation can induce CT number (CTN) change during radiation therapy (RT) delivery. In the effort to explore whether CTN can be used to assess RT response, we analyze the relationship between the pathological treatment response (PTR) and the changes of CTN, MRI, and PET before and after the neoadjuvant chemoradiation (nCR) for pancreatic adenocarcinoma.Methods:The preand post‐nCR CT, MRI, and PET data for a total of 8 patients with resectable, or borderline resectable pancreatic head adenocarcinoma treated with nCR were retrospectively analyzed. Radiographic characteristics were correlated to PTR data. The histograms, means and standard derivations (SD) of the CTNs in pancreatic head (CTNPH), the GTV defined by ADC (CTNGTV), and the rest of pancreatic head (CTNPH‐CTNGTV) were compared. Changes before and after nCR were correlated with the corresponding changes of ADC, lean body mass normalized SUV (SUVlb), and PTR using Pearson’ s correlation coefficient test.Results:The average mean and SD in CTPH for all the patients analyzed were higher in post‐nCR (53.17 ± 31.05 HU) compared to those at pre‐nCR (28.09 ± 4.253 HU). The CTNGTV were generally higher than CTNPH and CTNPH‐CTNGTV, though the differences were not significant. The post‐nCR changes of mean CTN, ADC, and SUVlb values in pancreatic head were correlated with PTR (R=0.3273/P=0.5357, R=−0.5455/P<0.0001, and R=0.7638/P=0.0357, respectively). The mean difference in the maximum tumor dimension measured from CTN, ADC, and SUVlb as compared with pathological measurements was −2.1, −0.5, and 0.22 cm, respectively.Conclusion:The radiation‐induced change of CTN in pancreas head after chemoradiation therapy of pancreatic cancer was observed, which may be related to treatment responses as assessed by biological imaging and pathology. More data are needed to determine whether the CTN can be used as a quantitative biomarker for response to neoadjuvant therapy.
SU‐E‐T‐01: (In)dependence of Plan Quality On Treatment Modalities and Target‐To‐Critical Structure Geometry for Brain Tumor
Authors: Ruan D , Shao W , Low D , Kupelian P , Qi S X 
Published: June 2015

Purpose:To evaluate and test the hypothesis that plan quality may be systematically affected by treatment delivery techniques and target‐tocritical structure geometric relationship in radiotherapy for brain tumor.Methods:Thirty‐four consecutive brain tumor patients treated between 2011–2014 were analyzed. Among this cohort, 10 were planned with 3DCRT, 11 with RadipArc, and 13 with helical IMRT on TomoTherapy. The selected dosimetric endpoints (i.e., PTV V100, maximum brainstem/chiasm/ optic nerve doses) were considered as a vector in a highdimensional space. A Pareto analysis was performed to identify the subset of Pareto‐efficient plans.The geometric relationships, specifically the overlapping volume and centroid‐of‐mass distance between each critical structure to the PTV were extracted as potential geometric features. The classification‐tree analyses were repeated using these geometric features with and without the treatment modality as an additional categorical predictor. In both scenarios, the dominant features to prognosticate the Pareto membership were identified and the tree structures to provide optimal inference were recorded. The classification performance was further analyzed to determine the role of treatment modality in affecting plan quality.Results:Seven Pareto‐efficient plans were identified based on dosimetric endpoints (3 from 3DCRT, 3 from RapicArc, 1 from Tomo), which implies that the evaluated treatment modality may have a minor influence on plan quality. Classification trees with/without the treatment modality as a predictor both achieved accuracy of 88.2%: with 100% sensitivity and 87.1% specificity for the former, and 66.7% sensitivity and 96.0% specificity for the latter. The coincidence of accuracy from both analyses further indicates no‐to‐weak dependence of plan quality on treatment modality. Both analyses have identified the brainstem to PTV distance as the primary predictive feature for Pareto‐efficiency.Conclusion:Pareto evaluation and classification‐tree analyses have indicated that plan quality depends strongly on geometry for brain tumor, specifically PTV‐tobrain‐stem‐distance but minimally on treatment modality.
SU‐E‐T‐02: 90Y Microspheres Dosimetry Calculation with Voxel‐S‐Value Method: A Simple Use in the Clinic
Authors: Maneru F , Otal A , Gracia M , Gallardo N , Olasolo J , Fuentemilla N , Bragado L , Martin‐Albina M , Lozares S , Pellejero S , Miquelez S , Rubio A 
Published: June 2015

Purpose:To present a simple and feasible method of voxel‐S‐value (VSV) dosimetry calculation for daily clinical use in radioembolization (RE) with 90Y microspheres. Dose distributions are obtained and visualized over CT images.Methods:Spatial dose distributions and dose in liver and tumor are calculated for RE patients treated with Sirtex Medical miscrospheres at our center. Data obtained from the previous simulation of treatment were the basis for calculations: Tc‐99m maggregated albumin SPECT‐CT study in a gammacamera (Infinia, General Electric Healthcare.). Attenuation correction and ordered‐subsets expectation maximization (OSEM) algorithm were applied.For VSV calculations, both SPECT and CT were exported from the gammacamera workstation and registered with the radiotherapy treatment planning system (Eclipse, Varian Medical systems). Convolution of activity matrix and local dose deposition kernel (S values) was implemented with an in‐house developed software based on Python code. The kernel was downloaded from www.medphys.it. Final dose distribution was evaluated with the free software Dicompyler.Results:Liver mean dose is consistent with Partition method calculations (accepted as a good standard). Tumor dose has not been evaluated due to the high dependence on its contouring. Small lesion size, hot spots in health tissue and blurred limits can affect a lot the dose distribution in tumors. Extra work includes: export and import of images and other dicom files, create and calculate a dummy plan of external radiotherapy, convolution calculation and evaluation of the dose distribution with dicompyler. Total time spent is less than 2 hours.Conclusion:VSV calculations do not require any extra appointment or any uncomfortable process for patient. The total process is short enough to carry it out the same day of simulation and to contribute to prescription decisions prior to treatment. Three‐dimensional dose knowledge provides much more information than other methods of dose calculation usually applied in the clinic.
SU‐E‐T‐04: 3D Dose Based Patient Compensator QA Procedure for Proton Radiotherapy
Authors: Zou W , Reyhan M , Zhang M , Davis R , Jabbour S , Khan A , Yue N 
Published: June 2015

Purpose:In proton double‐scattering radiotherapy, compensators are the essential patient specific devices to contour the distal dose distribution to the tumor target. Traditional compensator QA is limited to checking the drilled surface profiles against the plan. In our work, a compensator QA process was established that assess the entire compensator including its internal structure for patient 3D dose verification.Methods:The fabricated patient compensators were CT scanned. Through mathematical image processing and geometric transformations, the CT images of the proton compensator were combined with the patient simulation CT images into a new series of CT images, in which the imaged compensator is placed at the planned location along the corresponding beam line. The new CT images were input into the Eclipse treatment planning system. The original plan was calculated to the combined CT image series without the plan compensator. The newly computed patient 3D dose from the combined patientcompensator images was verified against the original plan dose. Test plans include the compensators with defects intentionally created inside the fabricated compensators.Results:The calculated 3D dose with the combined compensator and patient CT images reflects the impact of the fabricated compensator to the patient. For the test cases in which no defects were created, the dose distributions were in agreement between our method and the corresponding original plans. For the compensator with the defects, the purposely changed material and a purposely created internal defect were successfully detected while not possible with just the traditional compensator profiles detection methods.Conclusion:We present here a 3D dose verification process to qualify the fabricated proton double‐scattering compensator. Such compensator detection process assesses the patient 3D impact of the fabricated compensator surface profile as well as the compensator internal material and structure changes.This research receives funding support from CURA Medical Technologies.
SU‐E‐T‐07: 4DCT Robust Optimization for Esophageal Cancer Using Intensity Modulated Proton Therapy
Authors: Liao L , Yu J , Li Y , Zhu X , Li H , Lim G , Zhang X 
Published: June 2015

Purpose:To develop a 4DCT robust optimization method to reduce the dosimetric impact from respiratory motion in intensity modulated proton therapy (IMPT) for esophageal cancer.Methods:Four esophageal cancer patients were selected for this study. The different phases of CT from a set of 4DCT were incorporated into the worst‐case dose distribution robust optimization algorithm. 4DCT robust treatment plans were designed and compared with the conventional non‐robust plans. Result doses were calculated on the average and maximum inhale/exhale phases of 4DCT. Dose volume histogram (DVH) band graphic and ΔD95%, ΔD98%, ΔD5%, ΔD2% of CTV between different phases were used to evaluate the robustness of the plans.Results:Compare to the IMPT plans optimized using conventional methods, the 4DCT robust IMPT plans can achieve the same quality in nominal cases, while yield a better robustness to breathing motion. The mean ΔD95%, ΔD98%, ΔD5% and ΔD2% of CTV are 6%, 3.2%, 0.9% and 1% for the robustly optimized plans vs. 16.2%, 11.8%, 1.6% and 3.3% from the conventional non‐robust plans.Conclusion:A 4DCT robust optimization method was proposed for esophageal cancer using IMPT. We demonstrate that the 4DCT robust optimization can mitigate the dose deviation caused by the diaphragm motion.
SU‐E‐T‐09: A Clinical Implementation and Optimized Dosimetry Study of Freiberg Flap Skin Surface Treatment in High Dose Rate Brachytherapy
Authors: Syh J , Syh J , Patel B , Wu H , Durci M 
Published: June 2015

Purpose:This case study was designated to confirm the optimized plan was used to treat skin surface of left leg in three stages. 1. To evaluate dose distribution and plan quality by alternating of the source loading catheters pattern in flexible Freiberg Flap skin surface (FFSS) applicator. 2. To investigate any impact on Dose Volume Histogram (DVH) of large superficial surface target volume coverage. 3. To compare the dose distribution if it was treated with electron beam.Methods:The Freiburg Flap is a flexible mesh style surface mold for skin radiation or intraoperative surface treatments. The Freiburg Flap consists of multiple spheres that are attached to each other, holding and guiding up to 18 treatment catheters. The Freiburg Flap also ensures a constant distance of 5mm from the treatment catheter to the surface. Three treatment trials with individual planning optimization were employed: 18 channels, 9 channels of FF and 6 MeV electron beam. The comparisons were highlighted in target coverage, dose conformity and dose sparing of surrounding tissues.Results:The first 18 channels brachytherapy plan was generated with 18 catheters inside the skin‐wrapped up flap (Figure 1A). A second 9 catheters plan was generated associated with the same calculation points which were assigned to match prescription for target coverage as 18 catheters plan (Figure 1B). The optimized inverse plan was employed to reduce the dose to adjacent structures such as tibia or fibula. The comparison of DVH's was depicted on Figure 2. External beam of electron RT plan was depicted in Figure 3. Overcall comparisons among these three were illustrated inConclusion:The 9‐channel Freiburg flap flexible skin applicator offers a reasonably acceptable plan without compromising the coverage. Electron beam was discouraged to use to treat curved skin surface because of low target coverage and high dose in adjacent tissues.
SU‐E‐T‐11: A Cloud Based CT and LINAC QA Data Management System
Authors: Wiersma R , Grelewicz Z , Belcher A , Liu X 
Published: June 2015

Purpose:The current status quo of QA data management consists of a mixture of paper‐based forms and spreadsheets for recording the results of daily, monthly, and yearly QA tests for both CT scanners and LINACs. Unfortunately, such systems suffer from a host of problems as, (1) records can be easily lost or destroyed, (2) data is difficult to access — one must physically hunt down records, (3) poor or no means of historical data analysis, and (4) no remote monitoring of machine performance off‐site. To address these issues, a cloud based QA data management system was developed and implemented.Methods:A responsive tablet interface that optimizes clinic workflow with an easy‐to‐navigate interface accessible from any web browser was implemented in HTML/javascript/CSS to allow user mobility when entering QA data. Automated image QA was performed using a phantom QA kit developed in Python that is applicable to any phantom and is currently being used with the Gammex ACR, Las Vegas, Leeds, and Catphan phantoms for performing automated CT, MV, kV, and CBCT QAs, respectively. A Python based resource management system was used to distribute and manage intensive CPU tasks such as QA phantom image analysis or LaTeX‐to‐PDF QA report generation to independent process threads or different servers such that website performance is not affected.Results:To date the cloud QA system has performed approximately 185 QA procedures. Approximately 200 QA parameters are being actively tracked by the system on a monthly basis. Electronic access to historical QA parameter information was successful in proactively identifying a Linac CBCT scanner's performance degradation.Conclusion:A fully comprehensive cloud based QA data management system was successfully implemented for the first time. Potential machine performance issues were proactively identified that would have been otherwise missed by a paper or spreadsheet based QA system.