bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐E‐17A‐05: Complementary Prognostic Value of CT and 18F‐FDG PET Non‐Small Cell Lung Cancer Tumor Heterogeneity Features Quantified Through Texture Analysis
Authors: Desseroit M , Cheze Le Rest C , Tixier F , Majdoub M , Guillevin R , Perdrisot R , Visvikis D , Hatt M 
Published: July 2014

Purpose:Previous studies have shown that CT or 18F‐FDG PET intratumor heterogeneity features computed using texture analysis may have prognostic value in Non‐Small Cell Lung Cancer (NSCLC), but have been mostly investigated separately. The purpose of this study was to evaluate the potential added value with respect to prognosis regarding the combination of non‐enhanced CT and 18F‐FDG PET heterogeneity textural features on primary NSCLC tumors.Methods:One hundred patients with non‐metastatic NSCLC (stage I–III), treated with surgery and/or (chemo)radiotherapy, that underwent staging 18F‐FDG PET/CT images, were retrospectively included. Morphological tumor volumes were semi‐automatically delineated on non‐enhanced CT using 3D SlicerTM. Metabolically active tumor volumes (MATV) were automatically delineated on PET using the Fuzzy Locally Adaptive Bayesian (FLAB) method. Intratumoral tissue density and FDG uptake heterogeneities were quantified using texture parameters calculated from co‐occurrence, difference, and run‐length matrices. In addition to these textural features, first order histogram‐derived metrics were computed on the whole morphological CT tumor volume, as well as on sub‐volumes corresponding to fine, medium or coarse textures determined through various levels of LoG‐filtering. Association with survival regarding all extracted features was assessed using Cox regression for both univariate and multivariate analysis.Results:Several PET and CT heterogeneity features were prognostic factors of overall survival in the univariate analysis. CT histogram‐derived kurtosis and uniformity, as well as Low Grey‐level High Run Emphasis (LGHRE), and PET local entropy were independent prognostic factors. Combined with stage and MATV, they led to a powerful prognostic model (p<0.0001), with median survival of 49 vs. 12.6 months and a hazard ratio of 3.5.Conclusion:Intratumoral heterogeneity quantified through textural features extracted from both CT and FDG PET images have complementary and independent prognostic value in NSCLC.
WE‐E‐17A‐06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI
Authors: Gensheimer M , Trister A , Hawkins D , Ermoian R 
Published: July 2014

Purpose:In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence.Methods:The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segments at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse‐free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated.Results:The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast‐enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09‐0.90).Conclusion:SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was shorter when the primary tumor exhibited larger scale of heterogeneity on contrast‐enhanced MRI. If validated on a larger dataset, this imaging biomarker could be useful to help personalize treatment.
WE‐E‐17A‐07: Patient‐Specific Mathematical Neuro‐Oncology: Biologically‐Informed Radiation Therapy and Imaging Physics
Authors: Swanson K , Corwin D , Rockne R 
Published: July 2014

Purpose:To demonstrate a method of generating patient‐specific, biologically‐guided radiation therapy (RT) plans and to quantify and predict response to RT in glioblastoma. We investigate the biological correlates and imaging physics driving T2‐MRI based response to radiation therapy using an MRI simulator.Methods:We have integrated a patient‐specific biomathematical model of glioblastoma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity‐modulated RT optimization to construct individualized, biologically‐guided plans. Patient‐individualized simulations of the standard‐of‐care and optimized plans are compared in terms of several biological metrics quantified on MRI. An extension of the PI model is used to investigate the role of angiogenesis and its correlates in glioma response to therapy with the Proliferation‐Invasion‐Hypoxia‐ Necrosis‐Angiogenesis model (PIHNA). The PIHNA model is used with a brain tissue phantom to predict tumor‐induced vasogenic edema, tumor and tissue density that is used in a multi‐compartmental MRI signal equation for generation of simulated T2‐ weighted MRIs.Results:Applying a novel metric of treatment response (Days Gained) to the patient‐individualized simulation results predicted that the optimized RT plans would have a significant impact on delaying tumor progression, with Days Gained increases from 21% to 105%. For the T2‐ MRI simulations, initial validation tests compared average simulated T2 values for white matter, tumor, and peripheral edema to values cited in the literature. Simulated results closely match the characteristic T2 value for each tissue.Conclusion:Patient‐individualized simulations using the combination of a biomathematical model with an optimization algorithm for RT generated biologically‐guided doses that decreased normal tissue dose and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma. Simulated T2‐MRI is shown to be consistent with known physics of MRI and can be used to further investigate biological drivers of imaging‐based response to RT.
WE‐E‐17A‐09: Modulation of the Interstitial Fluid Pressure by High Intensity Focused Ultrasound as a Way to Alter Local Fluid and Solute Movement: Insights From a Mathematical Model
Authors: Sassaroli E , O' Neill B 
Published: July 2014

Purpose:To investigate and quantify the mechanisms responsible for the increased uptake of macromolecules observed in tissue exposed to pulsed high‐intensity focused ultrasound (HIFU) guided by MRI in preclinical studies.Methods:A mathematical model describing the transport of fluid and macromolecules in normal and tumor tissue is presented. The tissue is treated as a porous‐permeable, isotropic and linearly elastic medium. The model calculates the changes in the interstitial fluid pressure (IFP) and the interstitial fluid velocity (IFV) immediately after HIFU exposure. T2 weighted MR images taken a few minutes after a pulsed HIFU exposure of a rabbit thigh have been employed to estimate the fluid content in the tissue. A typical HIFU exposure is about 60s long and results in a temperature increase (hottest pixel) of about 140C as measured by MRI thermometry. The IFP and the IVF are then inserted into a macroscopic solute transport equation to determine the tissue concentration profiles of macromolecules. The model equations have been solved numerically using a finite element technique.Results:In both normal and tumor tissue, the model shows that in the focal region, immediately after HIFU exposure, there is a decrease in the IFP and the IVF is directed inward. Furthermore, the solution of the solute transport equation predicts a significant accumulation in the interstitium of large macromolecule solutes such as IgG which has a molecular weight of 150,000 Da.Conclusion:Efficient delivery of therapeutic agents in targeted tissues still remains a significant challenge in medicine. HIFU guided by MRI has been shown to improve targeted drug delivery in preclinical studies. The results of our simulation suggest improved fluid convection and macromolecule distribution during the initial lowering of the IFP resulting from HIFU exposure. This model offers valuable guidance for developing strategies that employ MRI‐guided HIFU for improving targeted drug delivery.
WE‐E‐17A‐10: Singular Value Decomposition Identifies Transcript Length Distribution Functions from DNA Microarray Data and Reveals Evolutionary Forces Globally Affecting Glioblastoma Multiforme Metabolism
Authors: Alter O 
Published: July 2014

Purpose:To search for evolutionary forces that might act upon eukaryotic mRNA transcript length, e.g., as a normal cell is transformed to a tumor cell.Methods:We use the singular value decomposition (SVD) to identify the length distribution functions of sets and subsets of human and yeast transcripts from profiles of mRNA abundance levels across gel electrophoresis migration distances that were previously measured by DNA microarrays. We show that the SVD identifies the transcript length distribution functions as “asymmetric generalized coherent states” from the DNA microarray data and with no a‐priori assumptions [Alter and Golub, Proc. Natl. Acad. Sci. USA 103 (32), pp. 11828‐11833 (2006); doi: 10.1073/pnas.0604756103].Results:Comparing subsets of human and yeast transcripts of the same gene ontology annotations, we find that in both disparate eukaryotes, transcripts involved in protein synthesis or mitochondrial metabolism are significantly shorter than typical, and in particular, significantly shorter than those involved in glucose metabolism. Comparing the subsets of human transcripts that are overexpressed in glioblastoma multiforme (GBM) or normal brain tissue samples from The Cancer Genome Atlas, we find that GBM maintains normal brain overexpression of significantly short transcripts, enriched in transcripts that are involved in protein synthesis or mitochondrial metabolism, but suppresses normal overexpression of significantly longer transcripts, enriched in transcripts that are involved in glucose metabolism and brain activity [Bertagnolli, Drake, Tennessen and Alter, PLoS One 8 (11), article e78913 (2013); doi: 10.1371/journal.pone.0078913].Conclusion:These global relations among transcript length, cellular metabolism and tumor development suggest a previously unrecognized physical mode for tumor and normal cells to differentially regulate metabolism in a transcript length‐dependent manner. The identified distribution functions support a previous hypothesis from mathematical modeling of evolutionary forces that act upon transcript length in the manner of the restoring force of the harmonic oscillator.This research was supported by the Utah Science, Technology and Research (USTAR) Initiative, National Human Genome Research Institute (NHGRI) R01 Grant HG‐004302 and National Science Foundation (NSF) CAREER Award DMS‐0847173
WE‐E‐18A‐01: Large Area Avalanche Amorphous Selenium Sensors for Low Dose X‐Ray Imaging
Authors: Scheuermann J , Goldan A , Tousignant O , Leveille S , Zhao W 
Published: July 2014

Purpose:A large area indirect flat panel imager (FPI) with avalanche gain is being developed to achieve x‐ray quantum noise limited low dose imaging. It uses a thin optical sensing layer of amorphous selenium (a‐Se), known as High‐Gain Avalanche Rushing Photoconductor (HARP), to detect optical photons generated from a high resolution x‐ray scintillator. We will report initial results in the fabrication of a solid‐state HARP structure suitable for a large area FPI. Our objective is to establish the blocking layer structures and defect suppression mechanisms that provide stable and uniform avalanche gain.Methods:Samples were fabricated as follows: (1) ITO signal electrode. (2) Electron blocking layer. (3) A 15 micron layer of intrinsic a‐Se. (4) Transparent hole blocking layer. (5) Multiple semitransparent bias electrodes to investigate avalanche gain uniformity over a large area. The sample was exposed to 50ps optical excitation pulses through the bias electrode. Transient time of flight (TOF) and integrated charge was measured. A charge transport simulation was developed to investigate the effects of varying blocking layer charge carrier mobility on defect suppression, avalanche gain and temporal performance.Results:Avalanche gain of ∼200 was achieved experimentally with our multi‐layer HARP samples. Simulations using the experimental sensor structure produced the same magnitude of gain as a function of electric field. The simulation predicted that the high dark current at a point defect can be reduced by two orders of magnitude by blocking layer optimization which can prevent irreversible damage while normal operation remained unaffected.Conclusion:We presented the first solid state HARP structure directly scalable to a large area FPI. We have shown reproducible and uniform avalanche gain of 200. By reducing mobility of the blocking layers we can suppress defects and maintain stable avalanche. Future work will optimize the blocking layers to prevent lag and ghosting.
WE‐G‐18A‐06: Sinogram Restoration in Helical Cone‐Beam CT
Authors: Little K , Riviere P La 
Published: July 2014

Purpose:To extend CT sinogram restoration, which has been shown in 2D to reduce noise and to correct for geometric effects and other degradations at a low computational cost, from 2D to a 3D helical cone‐beam geometry.Methods:A method for calculating sinogram degradation coefficients for a helical cone‐beam geometry was proposed. These values were used to perform penalized‐likelihood sinogram restoration on simulated data that were generated from the FORBILD thorax phantom. Sinogram restorations were performed using both a quadratic penalty and the edge‐preserving Huber penalty. After sinogram restoration, Fourier‐based analytical methods were used to obtain reconstructions. Resolution‐variance trade‐offs were investigated for several locations within the reconstructions for the purpose of comparing sinogram restoration to no restoration. In order to compare potential differences, reconstructions were performed using different groups of neighbors in the penalty, two analytical reconstruction methods (Katsevich and single‐slice rebinning), and differing helical pitches.Results:The resolution‐variance properties of reconstructions restored using sinogram restoration with a Huber penalty outperformed those of reconstructions with no restoration. However, the use of a quadratic sinogram restoration penalty did not lead to an improvement over performing no restoration at the outer regions of the phantom. Application of the Huber penalty to neighbors both within a view and across views did not perform as well as only applying the penalty to neighbors within a view. General improvements in resolution‐variance properties using sinogram restoration with the Huber penalty were not dependent on the reconstruction method used or the magnitude of the helical pitch.Conclusion:Sinogram restoration for noise and degradation effects for helical cone‐beam CT is feasible and should be able to be applied to clinical data. When applied with the edge‐preserving Huber penalty, sinogram restoration leads to an improvement in resolution‐variance tradeoffs.
TH‐A‐16A‐01: Image Quality for the Radiation Oncology Physicist: Review of the Fundamentals and Implementation
Authors: Seibert J , Imbergamo P 
Published: July 2014

The expansion and integration of diagnostic imaging technologies such as On Board Imaging (OBI) and Cone Beam Computed Tomography (CBCT) into radiation oncology has required radiation oncology physicists to be responsible for and become familiar with assessing image quality. Unfortunately many radiation oncology physicists have had little or no training or experience in measuring and assessing image quality. Many physicists have turned to automated QA analysis software without having a fundamental understanding of image quality measures. This session will review the basic image quality measures of imaging technologies used in the radiation oncology clinic, such as low contrast resolution, high contrast resolution, uniformity, noise, and contrast scale, and how to measure and assess them in a meaningful way. Additionally a discussion of the implementation of an image quality assurance program in compliance with Task Group recommendations will be presented along with the advantages and disadvantages of automated analysis methods.Learning Objectives:

Review and understanding of the fundamentals of image quality.
Review and understanding of the basic image quality measures of imaging modalities used in the radiation oncology clinic.
Understand how to implement an image quality assurance program and to assess basic image quality measures in a meaningful way.
TH‐A‐BRF‐03: Evaluation of Synthetic CTs Generated Using MR‐SIM Data
Authors: Kim J , Glide‐Hurst C , Doemer A , Wen N , Chett I 
Published: July 2014

Purpose:To describe and evaluate a novel algorithm for generating synthetic CT images from MR‐SIM data for dose calculations in MR‐only treatment planning.Methods:A voxel‐based weighted summation method was implemented to generate synthetic CT (synCT) images. MR data were acquired using Philips 1.0T Panorama high‐field open MR‐SIM. Retrospective patient data from seven prostate patients and one brain patient (three lesions) enrolled in an IRB‐approved study were used. 3D T1‐weighted fast field echo and 3D T2‐weighted turbo spin echo sequences were utilized for all patients. A 3D balanced turbo field echo sequence using spectral presaturation with inversion recovery was acquired for prostate patients, but 3D ultra‐short echo time (UTE)‐DIXON was instead acquired for the brain patient to amplify bone signal for semi‐automatic bone segmentation. Weight optimization was performed using a training subset of patients. HU value differences between planning CT and synCTs were analyzed using mean absolute error (MAE). Original patient CT‐based treatment plans were mapped onto synCTs, dose was recalculated using original leaf motion and MU values, and DRRs were generated. Dosevolume metrics and gamma analysis were used for dosimetric evaluation.Results:Average whole‐body MAE of synCTs across all patients was 75+12 HU. In prostate cancer patients, average HU difference between planning and synCTs was 0.9±1.0% for soft tissue structures and 4.3±2.5% for bony structures. DRRs were generated from synCTs and qualitatively showed good geometric agreement with planning CT‐generated DRRs. D99, mean dose, and maximum dose to CTV calculated using the synCT remained within 1.2% of planning CT‐based dose calculations. All gamma analysis evaluated at 2%/2mm dose difference/distance to agreement) pass rates were greater than 95% with an average of 99.9±0.1% for prostate patients and 98.4±2.2% for three brain lesions.Conclusion:SynCTs were generated with clinically acceptable accuracy comparable to planning CTs, enabling dose computations for MR‐only simulation.Research supported in part by a grant from Philips HealthCare (Best, Netherlands).
TH‐A‐BRF‐06: Optimal Timing for Ultrasound‐Activated Drug Delivery for Treatment of Prostate Cancer Under MR Guidance
Authors: Gupta R , Cvetkovic D , Wang B , Ma C , Chen L 
Published: July 2014

Purpose:To determine optimal timing for applying tumor targeted focused ultrasound (FUS) under MR guidance to release drugs from nanodroplets (ultrasound responsive, drug delivery vehicles) for effective treatment of prostate cancer.Methods:Poly lactide (PDLA) nanodroplets loaded with model drug {fluorescently labeled paclitaxel (FPTX)} were prepared using solid dispersion technique. Human prostate cancer, LNCaP cells were implanted orthotopically in prostates of male nude mice. Tumor bearing mice (n=3) were injected with 125 microlitre of 0.1% FPTX, 2% PDLA nanodroplets via tail‐vein. At 30min, 2h, 6h and 24h animals were anesthetized, blood was collected by eye bleed, then animals were sacrificed, tumor and organs were excised and weighted. One cut portion of the organ was homogenized, incubated in lysis buffer and centrifuged. Lysates were read using fluorescence spectrophotometer. Other portion was cut into sections for qualitative analysis using fluorescence microscope. Mice (n=3) were also injected with 0.1% FPTX solution (non encapsulated drug form) and mice (n=3) with no injections were used as controls.Results:Drug‐loaded nanodroplets shows more drug accumulation in tumor with maximum concentration of one third of injected dose after 2h of injection when compared with maximum concentration of less than one tenth of injected dose after 2h of injection for free drug. The time range 2h to 6h is considered optimum for applying FUS to activate drug release from nanodroplets, when maximum drug concentration is seen in tumor while low concentration is found in blood thus minimizing potential damage to blood vessels. Microscopy studies show consistency with quantitative data.Conclusion:Our prior in vivo studies have showed significant tumor growth delay when FUS is applied under MR imaging in combination with docetaxel loaded nanodroplets. Present work with model drug loaded nanodroplets indicates the potential for increasing prostate cancer treatment efficacy and providing preclinical data for using FUS‐mediated drug delivery under MR guidance.