bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐E‐J‐83: Ion Imaging to Better Estimate In‐Vivo Relative Stopping Powers Using X‐Ray CT Prior‐Knowledge Information
Authors: Dias M , Collins‐Fekete C , Riboldi M , Doolan P , Hansen D , Baroni G , Seco J 
Published: July 2014

Purpose:To reduce uncertainties in relative stopping power (RSP) estimates for ions (alpha and carbon) by using Ion radiographic‐imaging and X‐ray CT prior‐knowledge.Methods:A 36×36 phantom matrix composed of 9 materials with different thicknesses and randomly placed is generated. Theoretical RSPs are calculated using stopping power (SP) data from three references (Janni, ICRU49 and Bischel). We introduced an artificial systematic error (1.5%, 2.5% or 3.5%) and a random error (<0.5%) to the SP to simulated patient ion‐range errors present in clinic environment. Carbon/alpha final energy for each RSPs set (theoretical and from CT images) is obtained with a ray‐tracing algorithm. A gradient descent (GD) method is used to minimize the difference in exit particle energy, between theory and X‐ray CT RSP maps, by iteratively correcting the RSP map from X‐ray CT. Once a new set of RSPs is obtained for a direction a new optimization is done over other direction using the RSPs from the previous optimization. Theoretical RSPs are compared with experimental RSPs obtained with Gammex Phantom.Results:Preliminary results show that optimized RSP values can be obtained with smaller uncertainties (<1%) than clinical RSPs (1.5% to 3.5%). Theoretical values from three different references show uncertainties, up to 3% from experimental values. Further investigation will consider prior‐knowledge from RSP obtained with CT images and ion radiographies from Monte Carlo Simulations.Conclusion:GD and ray‐tracing methods have been implemented to reduce RSP uncertainties from values obtained for clinical treatment. Experimental RSPs will be obtained using carbon/alpha beams to consider the existence of material dependent systematic errors. Based on the results, it is hoped to show that using ray‐tracing optimization with ion radiography and prior knowledge on RPSs, treatment planning accuracy and cost‐effectiveness can be improved.
SU‐E‐J‐93: Parametrisation of Dose to the Mucosa of the Anterior Rectal Wall in Transrectal Ultrasound Guided High‐Dose‐Rate Brachytherapy of the Prostate
Authors: Aitkenhead A , Hamlett L , Wood D , Choudhury A 
Published: July 2014

Purpose:In high‐dose‐rate (HDR) brachytherapy of the prostate, radiation is delivered from a number of radioactive sources which are inserted via catheter into the target volume. The rectal mucosa also receives dose during the treatment, which may lead to late toxicity effects. To allow possible links between rectal dose and toxicity to be investigated, suitable methods of parametrising the rectal dose are needed.Methods:During treatment of a series of 95 patients, anatomy and catheter locations were monitored by transrectal ultrasound, and target volume positions were contoured on the ultrasound scan by the therapist. The anterior rectal mucosal wall was identified by contouring the transrectal ultrasound balloon within the ultrasound scan. Source positions and dwell times, along with the dose delivered to the patient were computed using the Oncentra Prostate treatment planning system (TPS). Data for the series of patients were exported from the TPS in Dicom format, and a series of parametrisation methods were developed in a Matlab environment to assess the rectal dose.Results:Contours of the anterior rectal mucosa were voxelised within Matlab to allow the dose to the rectal mucosa to be analysed directly from the 3D dose grid. Dose parametrisations based on dose‐surface (DSH) and dose‐line (DLH) histograms were obtained. Both lateral and longitudinal extents of the mucosal dose were parametrised using dose‐line histograms in the relevant directions.Conclusion:We have developed a series of dose parametrisations for quantifying the dose to the rectal mucosa during HDR prostate brachytherapy which are suitable for future studies investigating potential associations between mucosal dose and late toxicity effects. The geometry of the transrectal probe standardises the rectal anatomy, making this treatment technique particularly suited to studies of this nature.
SU‐E‐J‐95: Predicting Treatment Outcomes for Prostate Cancer: Irradiation Responses of Prostate Cancer Stem Cells
Authors: Wang K 
Published: July 2014

Purpose:Most prostate cancers are slow‐growing diseases but normally require much higher doses (80Gy) with conventional fractionation radiotherapy, comparing to other more aggressive cancers. This study is to disclose the radiobiological basis of this discrepancy by proposing the concept of prostate cancer stem cells (CSCs) and examining their specific irradiation responses.Methods:There are overwhelming evidences that CSC may keep their stemness, e.g. the competency of cell differentiation, in hypoxic microenvironments and hence become radiation resistive, though the probability is tiny for aggressiveness cancers. Tumor hypoxia used to be considered as an independent reason for poor treatment outcomes, and recent evidences showed that even prostate cancers were also hypoxic though they are very slow‐growing. In addition, to achieve comparable outcomes to other much more aggressive cancers, much higher doses (rather than lower doses) are always needed for prostate cancers, regardless of its non‐aggressiveness. All these abnormal facts can only be possibly interpreted by the irradiation responses characteristics of prostate CSCs.Results:Both normal cancer cells (NCCs) and CSCs exiting in tumors, in which NCCs are mainly for symptoms whereas killing all CSCs achieves disease‐free. Since prostate cancers are slow‐growing, the hypoxia in prostate cancers cannot possibly from NCCs, thus it is caused by hypoxic CSCs. However, single hypoxic cell cannot be imaged due to limitation of imaging techniques, unless a large group of hypoxic cells exist together, thus most of CSCs in prostate cancers are virtually hypoxic, i.e. not in working mode because CSCs in proliferating mode have to be normoxic, and this explains why prostate cancers are unaggressive.Conclusion:The fractional dose in conventional radiotherapy (∼2Gy) could only kill NCCs and CSCs in proliferating modes, whereas most CSCs survived fractional treatments since they were hypoxic, thus to eliminate all CSCs and achieve disease‐free, much more fractionations are needed.
SU‐E‐J‐96: Predictive Neural Network for Parotid Glands Deformation Using IGRT and Dose Warping Systems
Authors: Maffei N , Guidi G , Vecchi C , Baldazzi G , Costi T 
Published: July 2014

Purpose:Tumor shrinkage and modification of patient anatomy may occur within the weeks of therapy. Dose warping algorithm and IGRT can usefully determinate morphological variations occurring during treatments and predict possible challenge or requirement of re‐planning. Using metaanalysis and developing Predictive Neural Network (PNN) is possible to collect information for perspective treatment evaluation of inappropriate dose delivery or to re‐planning care course.Methods:23 H' N patients, treated using Tomotherapy Unit, were post‐processed using deformable hybrid algorithms to generate contours evolution throughout the course. 750 MVCT were elaborated focusing on parotids. For each deformable image registration were generated deformable ROIs and re‐mapped to dose grid to accumulate dose. A nomogram was developed to evaluate Volume (V) and Dose (D) variations. Using Moving Average (MA) function and PNN were identified the weeks when re‐plan should be predictable.Results:The weekly nomogram provided the percentage of patients affected by V and D deviation respect to the first day of treatment. During 1st week of treatment 95% of patients have a ΔV=10% and ΔD=2%. In the 6th week, only 70% of cases remain inside that range. The PNN using Cluster Analysis and Support Vector Machines, highlighted the requirement of warping methods in clinical practice. Validated by 8 patients test cases, PNN allow to classify 2 statistical cluster related to original plan and re‐plan requirements. MA with period of 3 and 5 days, predict days of treatment where to locate physics replan. Best statistical benefits can be achieved with evaluation in the 4th week.Conclusion:Body morphing and organ motion during therapy can affect to the dose distribution and induce unexpected or late toxicities. Hybrid deformable registration allows to study the time to re‐plan or to adapt treatments. Monitoring large patient's database during treatments, thereby rising accuracy in nomograms, PNN and reproducibility of treatments.The research is partially co‐funded by the MoH (GR‐2010‐2318757) and Tecnologie Avanzate S.r.1.(Italy)
SU‐E‐J‐107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis
Authors: Bredfeldt J , Liu Y , Conklin M , Keely P , Eliceiri K , Mackie T 
Published: July 2014

Purpose:Our goal is to develop and apply a set of optical and computational tools to enable large‐scale investigations of the interaction between collagen and tumor cells.Methods:We have built a novel imaging system for automating the capture of whole‐slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi‐automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups.Results:We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells.Conclusion:We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.
SU‐E‐J‐109: Testing the KV Imaging Center Congruence with Radiation Isocenter of Small MLC and SRS Cone Field On Two Machines
Authors: Fu  , Chen Y , Yu Y , Liu H 
Published: July 2014

Purpose:Orthogonal kV image pairs are used for target localization when fiducial markers are implanted. CBCT is used to verify cone SRS setup. Therefore it is necessary to evaluate the isocenter congruence between radiation fields and kV imaging center. This study used a simple method to evaluate the isocenter congruence, and compared the results for MLC and cone fields on two different Linacs.Methods:Varian OBI block was attached on the couch. It has a central 1mm BB with markers on three surfaces to align with laser. KV and MV images were taken at four cardinal angles. A 3×3cm2 MLC field and a 20mm cone field were irradiated respectively. On each kV image, the distance from BB center to the kV graticule center were measured. On the MV image of MLC field, the center of radiation field was determined manually, while for cone field, the Varian AM maintenance software was used to analyze the distance between BB and radiation field. The subtraction of the two distances gives the discrepancy between kV and radiation centers. Each procedure was repeated on five days at Trilogy and TrueBeam respectively.Results:The maximum discrepancy was found in the longitudinal direction at 180° gantry angel. It was 1.5±0.1mm for Trilogy and 0.6±0.1mm for TrueBeam. For Trilogy, although radiation center wobbled only 0.7mm and image center wobbled 0.8mm, they wobbled to the opposite direction. KV Pair using gantry 180° should be avoided in this case. Cone vs. kV isocenter has less discrepancy than MLC for Trilogy.Conclusion:Radiation isocenter of MLC and cone field is different, so is between Trilogy and TrueBeam. The method is simple and reproducible to check kV and radiation isocenter congruence.
SU‐E‐J‐110: TG 51 Dosimetry : With Or Without Lead
Authors: Shah M 
Published: July 2014

TG‐51 Dosimetry: With or Without Lead.Purpose:In this project, an analytical method has been introduced for adjustment of the TG‐51 recommended KQ in order to produce accurate dosimetric data for high energy photons without the lead foil.Methods:These investigations were performed using a 30 cm × 30 cm × 30 cm CIVCO water tank, A12 EXRADIN Water proof Farmer Chamber, a Standard Imaging MAX 4000 electrometer, and 1 mm thick lead foil from Standard Imaging. Complete TG‐51 was performed every month with and without lead. The results were analyzed and an analytical model has been developed for comparing the values of KQ. TG‐51 Table I was used to obtain KQ values.Results:The dosimetric evaluations were obtained for Varian Linear accelerators Model 21ix and 21ex. These results indicates that the measured data with lead foil in place as recommended by TG‐51 is in excellent agreement (within 0.1%) with the calculated data obtained by the new model, from our dosimetry data without‐lead. If equation 15 of the TG‐51 report is used without any adjustments, it will lead to differences of about 1.6 % (on the average) in relative data which will Resultin differences of about 0.3 % (on the average) in the KQ Values. The KQ value for 18 MV obtained consistently with the equation of TG‐51 “with lead” and “without lead” were 0.971 and 0.974, respectively. The 0.3 % higher results for KQ without lead eventually will lead to 0.3% larger output. However, by considering this model the KQ value was found to be 0.971 for dosimetry without lead.Conclusion:The analytical model that was introduced in this project was able to reproduce the dosimetric data of the high energy linear accelerators to within 0.1% without the use of the lead foil.
SU‐E‐J‐112: The Impact of Cine EPID Image Acquisition Frame Rate On Markerless Soft‐Tissue Tracking
Authors: Yip S , Rottmann J , Berbeco R 
Published: July 2014

Purpose:Although reduction of the cine EPID acquisition frame rate through multiple frame averaging may reduce hardware memory burden and decrease image noise, it can hinder the continuity of soft‐tissue motion leading to poor auto‐tracking results. The impact of motion blurring and image noise on the tracking performance was investigated.Methods:Phantom and patient images were acquired at a frame rate of 12.87Hz on an AS1000 portal imager. Low frame rate images were obtained by continuous frame averaging. A previously validated tracking algorithm was employed for auto‐tracking. The difference between the programmed and auto‐tracked positions of a Las Vegas phantom moving in the superior‐inferior direction defined the tracking error (δ). Motion blurring was assessed by measuring the area change of the circle with the greatest depth. Additionally, lung tumors on 1747 frames acquired at eleven field angles from four radiotherapy patients are manually and automatically tracked with varying frame averaging. δ was defined by the position difference of the two tracking methods. Image noise was defined as the standard deviation of the background intensity. Motion blurring and image noise were correlated with δ using Pearson correlation coefficient (R).Results:For both phantom and patient studies, the auto‐tracking errors increased at frame rates lower than 4.29Hz. Above 4.29Hz, changes in errors were negligible with δ<1.60mm. Motion blurring and image noise were observed to increase and decrease with frame averaging, respectively. Motion blurring and tracking errors were significantly correlated for the phantom (R=0.94) and patient studies (R=0.72). Moderate to poor correlation was found between image noise and tracking error with R ‐0.58 and ‐0.19 for both studies, respectively.Conclusion:An image acquisition frame rate of at least 4.29Hz is recommended for cine EPID tracking. Motion blurring in images with frame rates below 4.39Hz can substantially reduce the accuracy of auto‐tracking.This work is supported in part by the Varian Medical Systems, Inc.
SU‐E‐J‐113: The Influence of Optimizing Pediatric CT Simulator Protocols On the Treatment Dose Calculation in Radiotherapy
Authors: Zhang Y , Deng J , Zhang J , Hu Q , Tie J , Wu H 
Published: July 2014

Purpose:To investigate the possibility of applying optimized scanning protocols for pediatric CT simulation by quantifying the dosimetric inaccuracy introduced by using a fixed HU to density conversion.Methods:The images of a CIRS electron density reference phantom (Model 062) were acquired by a Siemens CT simulator (Sensation Open) using the following settings of tube voltage and beam current: 120 kV/190mA (the reference protocol used to calibrate CT for our treatment planning system (TPS)); Fixed 190mA combined with all available kV: 80, 100, and 140; fixed 120 kV and various current from 37 to 444 mA (scanner extremes) with interval of 30 mA. To avoid the HU uncertainty of point sampling in the various inserts of known electron densities, the mean CT numbers of the central cylindrical volume were calculated using DICOMan software. The doses per 100 MU to the reference point (SAD=100cm, Depth=10cm, Field=10×10cm, 6MV photon beam) in a virtual cubic phantom (30×30×30cm) were calculated using Eclipse TPS (calculation model: AcurosXB_11031) by assigning the CT numbers to HU of typical materials acquired by various protocols.Results:For the inserts of densities less than muscle, CT number fluctuations of all protocols were within the tolerance of 10 HU as accepted by AAPM‐TG66. For more condensed materials, fixed kV yielded stable HU with any mA combination where largest disparities were found in 1750mg/cc insert: HUreference=1801(106.6cGy), HUminimum=1799 (106.6cGy, errordose=0.00%), HUmaximum=1815 (106.8cGy, errordose=0.19%). Yet greater disagreements were observed with increasing density when kV was modified: HUminimum=1646 (104.5cGy, errordose=‐ 1.97%), HUmaximum=2487 (116.4cGy, errordose=9.19%) in 1750mg/cc insert.Conclusion:Without affecting treatment dose calculation, personalized mA optimization of CT simulator can be conducted by fixing kV for a better cost‐effectiveness of imaging dose and quality especially for children. Unless recalibrated, kV should be constant for all anatomical sites if diagnostic CT scanner is used as a simulator.This work was partially supported by Capital Medical Development Scientific Research Fund of China.
SU‐E‐J‐115: Using Markov Chain Modeling to Elucidate Patterns in Breast Cancer Metastasis Over Time and Space
Authors: Comen E , Mason J , Nieva J , Newton P , Kuhn P , Norton L , Venkatappa N , Jochelson M 
Published: July 2014

Purpose:Traditionally, breast cancer metastasis is described as a process wherein cancer cells spread from the breast to multiple organ systems via hematogenous and lymphatic routes. Mapping organ specific patterns of cancer spread over time is essential to understanding metastatic progression. In order to better predict sites of metastases, here we demonstrate modeling of the patterned migration of metastasis.Methods:We reviewed the clinical history of 453 breast cancer patients from Memorial Sloan Kettering Cancer Center who were non‐metastatic at diagnosis but developed metastasis over time. We used the variables of organ site of metastases as well as time to create a Markov chain model of metastasis. We illustrate the probabilities of metastasis occurring at a given anatomic site together with the probability of spread to additional sites.Results:Based on the clinical histories of 453 breast cancer patients who developed metastasis, we have learned (i) how to create the Markov transition matrix governing the probabilities of cancer progression from site to site; (ii) how to create a systemic network diagram governing disease progression modeled as a random walk on a directed graph; (iii) how to classify metastatic sites as ‘sponges’ that tend to only receive cancer cells or ‘spreaders’ that receive and release them; (iv) how to model the time‐scales of disease progression as a Weibull probability distribution function; (v) how to perform Monte Carlo simulations of disease progression; and (vi) how to interpret disease progression as an entropy‐increasing stochastic process.Conclusion:Based on our modeling, metastatic spread may follow predictable pathways. Mapping metastasis not simply by organ site, but by function as either a ‘spreader’ or ‘sponge’ fundamentally reframes our understanding of metastatic processes. This model serves as a novel platform from which we may integrate the evolving genomic landscape that drives cancer metastasis. PS‐OC Trans‐Network Project Grant Award for “Data Assimilation and ensemble statistical forecasting methods applied to the MSKCC longitudinal metastatic breast cancer cohort.”
SU‐E‐J‐116: Validation of Deformable Image Registration Within the 4D Lung Using Two Commercial Algorithms and the User Guided Tool Reg Refine
Authors: Johnson P , Padgett K , Dogan N 
Published: July 2014

Purpose:To quantify the registration error between different phases of breathing using two commercially available deformable image registration algorithms. A secondary goal was to better understand the Reg Refine tool developed by MIM software and provide practical recommendations for its use.Methods:The 4D patient data sets of the POPI model were used as phantoms for this work. DIR was performed using MIM Maestro and the SmartAdapt module available within Eclipse. For each of six patients, 100 landmarks were deformed between inhalation and exhalation with their final location compared to the ground truth previously established by expert identification. The base rigid registration was varied to assess the effect on the DIR. For MIM, the Reg Refine tool was also used to try and improve the registration. The number of locked points was varied in addition to varying their location and the size of the window used for local box based alignment.Results:The average displacement for 5 of 6 patients was 6 – 8 mm. For these patients the mean error was low (1 – 1.5 mm) no matter which algorithm was used. SmartAdapt was less sensitive to the initial rigid registration, although both algorithms showed improvement when rigidly aligned to the base of the lung versus the apex. Reg Refine further improved accuracy albeit marginally. For the remaining patient the average displacement was larger (14 mm) and the Reg Refine tool showed marked improvement in reducing the mean and maximum errors from 5 and 35.5 mm to 1.1 and 4.8 mm using as little as ten points.Conclusion:The residual registration error using both SmartAdapt and MIM Maestro for 4D lung patients has been quantified on the order of 1 – 1.5 mm using the POPI model. Reg Refine was also shown effective at improving registrations within MIM Maestro.