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Tomography: a Journal for Imaging Research

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https://www.readbyqxmd.com/read/28691102/dce-mri-texture-features-for-early-prediction-of-breast-cancer-therapy-response
#1
Guillaume Thibault, Alina Tudorica, Aneela Afzal, Stephen Y-C Chui, Arpana Naik, Megan L Troxell, Kathleen A Kemmer, Karen Y Oh, Nicole Roy, Neda Jafarian, Megan L Holtorf, Wei Huang, Xubo Song
This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6-8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion...
March 2017: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28649664/quantitative-analysis-of-the-spatial-distribution-of-metastatic-brain-lesions
#2
Ted K Yanagihara, Albert Lee, Tony J C Wang
Brain metastases (BMs) are the most common intracranial malignancy and afflict ~10%-20% of patients with cancer. BMs tend to present at the boundaries of gray and white matter because of the distribution of small vessels. In addition, metastases may not be randomly distributed across gross anatomical regions of the brain, but this has not previously been quantified. We retrospectively analyzed a series of 28 patients with recurrent BMs with a total of 150 lesions. Each lesion was manually defined based on T1 gadolinium-enhanced imaging...
March 2017: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28626797/intrathoracic-fat-measurements-using-multidetector-computed-tomography-mdct-feasibility-and-reproducibility
#3
Jadranka Stojanovska, El-Sayed H Ibrahim, Aamer R Chughtai, Elizabeth A Jackson, Barry H Gross, Jon A Jacobson, Alexander Tsodikov, Brian Daneshvar, Benjamin D Long, Thomas L Chenevert, Ella A Kazerooni
Intrathoracic fat volume, more specifically, epicardial fat volume, is an emerging imaging biomarker of adverse cardiovascular events. The purpose of this work is to show the feasibility and reproducibility of intrathoracic fat volume measurement applied to contrast-enhanced multidetector computed tomography images. A retrospective cohort study of 62 subjects free of cardiovascular disease (55% females, age = 49 ± 11 years) conducted from 2008 to 2011 formed the study group. Intrathoracic fat volume was defined as all fat voxels measuring -50 to -250 Hounsfield Unit within the intrathoracic cavity from the level of the pulmonary artery bifurcation to the heart apex...
March 2017: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28584878/a-population-based-digital-reference-object-dro-for-optimizing-dynamic-susceptibility-contrast-dsc-mri-methods-for-clinical-trials
#4
Natenael B Semmineh, Ashley M Stokes, Laura C Bell, Jerrold L Boxerman, C Chad Quarles
The standardization and broad-scale integration of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, we developed a digital reference object (DRO), using physiological and kinetic parameters derived from in vivo data, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach, aimed at validating image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas...
March 2017: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28553660/mri-guided-stereotactic-biopsy-of-murine-gbm-for-spatiotemporal-molecular-genomic-assessment
#5
Stefanie Galbán, Wajd N Al-Holou, Hanxiao Wang, Amanda R Welton, Kevin Heist, Xin Kathy Hu, Roeland Gw Verhaak, Yuan Zhu, Carlos Espinoza, Thomas L Chenevert, Ben A Hoff, Craig J Galbán, Brian D Ross
Brain tumor biopsies that are routinely performed in clinical settings significantly aid in diagnosis and staging. The aim of this study is to develop and evaluate a methodological image-guided approach that would allow for routine sampling of glioma tissue from orthotopic mouse brain tumor models. A magnetic resonance imaging-guided biopsy method is presented to allow for spatially precise stereotaxic sampling of a murine glioma coupled with genome-scale technology to provide unbiased characterization of intra- and intertumoral clonal heterogeneity...
March 2017: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28612050/a-rapid-segmentation-insensitive-digital-biopsy-method-for-radiomic-feature-extraction-method-and-pilot-study-using-ct-images-of-non-small-cell-lung-cancer
#6
Sebastian Echegaray, Viswam Nair, Michael Kadoch, Ann Leung, Daniel Rubin, Olivier Gevaert, Sandy Napel
Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28480331/comparison-between-3-scan-trace-and-diagonal-body-diffusion-weighted-imaging-acquisitions-a-phantom-and-volunteer-study
#7
Stefanie J Hectors, Mathilde Wagner, Idoia Corcuera-Solano, Martin Kang, Alto Stemmer, Michael A Boss, Bachir Taouli
Diagonal diffusion-weighted imaging (dDWI) uses simultaneous maximized application of 3 orthogonal gradient systems as opposed to sequential acquisition in 3 directions in conventional 3-scan trace DWI (tDWI). Several theoretical advantages of dDWI vs. tDWI include reduced artifacts and increased sharpness. We compared apparent diffusion coefficient (ADC) quantification and image quality between monopolar dDWI and tDWI in a dedicated diffusion phantom (b = 0/500/900/2000 s/mm(2)) and in the abdomen (b = 50/400/800 s/mm(2)) and pelvis (b = 50/1000/1600 s/mm(2)) of 2 male volunteers at 1...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28286871/semiautomated-workflow-for-clinically-streamlined-glioma-parametric-response-mapping
#8
Lauren Keith, Brian D Ross, Craig J Galbán, Gary D Luker, Stefanie Galbán, Binsheng Zhao, Xiaotao Guo, Thomas L Chenevert, Benjamin A Hoff
Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28149958/radiomics-of-lung-nodules-a-multi-institutional-study-of-robustness-and-agreement-of-quantitative-imaging-features
#9
Jayashree Kalpathy-Cramer, Artem Mamomov, Binsheng Zhao, Lin Lu, Dmitry Cherezov, Sandy Napel, Sebastian Echegaray, Daniel Rubin, Michael McNitt-Gray, Pechin Lo, Jessica C Sieren, Johanna Uthoff, Samantha K N Dilger, Brandan Driscoll, Ivan Yeung, Lubomir Hadjiiski, Kenny Cha, Yoganand Balagurunathan, Robert Gillies, Dmitry Goldgof
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28127586/accrual-patterns-for-clinical-studies-involving-quantitative-imaging-results-of-an-nci-quantitative-imaging-network-qin-survey
#10
Brenda F Kurland, Sameer Aggarwal, Thomas E Yankeelov, Elizabeth R Gerstner, James M Mountz, Hannah M Linden, Ella F Jones, Kellie L Bodeker, John M Buatti
Patient accrual is essential for the success of oncology clinical trials. Recruitment for trials involving the development of quantitative imaging biomarkers may face different challenges than treatment trials. This study surveyed investigators and study personnel for evaluating accrual performance and perceived barriers to accrual and for soliciting solutions to these accrual challenges that are specific to quantitative imaging-based trials. Responses for 25 prospective studies were received from 12 sites...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28111634/temporal-feature-extraction-from-dce-mri-to-identify-poorly-perfused-subvolumes-of-tumors-related-to-outcomes-of-radiation-therapy-in-head-and-neck-cancer
#11
Daekeun You, Madhava Aryal, Stuart E Samuels, Avraham Eisbruch, Yue Cao
This study aimed to develop an automated model to extract temporal features from DCE-MRI in head-and-neck (HN) cancers to localize significant tumor subvolumes having low blood volume (LBV) for predicting local and regional failure after chemoradiation therapy. Temporal features were extracted from time-intensity curves to build classification model for differentiating voxels with LBV from those with high BV. Support vector machine (SVM) classification was trained on the extracted features for voxel classification...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28105470/bladder-cancer-segmentation-in-ct-for-treatment-response-assessment-application-of-deep-learning-convolution-neural-network-a-pilot-study
#12
COMMENT
Kenny H Cha, Lubomir M Hadjiiski, Ravi K Samala, Heang-Ping Chan, Richard H Cohan, Elaine M Caoili, Chintana Paramagul, Ajjai Alva, Alon Z Weizer
Assessing the response of bladder cancer to neoadjuvant chemotherapy is crucial for reducing morbidity and increasing quality of life of patients. Changes in tumor volume during treatment is generally used to predict treatment outcome. We are developing a method for bladder cancer segmentation in CT using a pilot data set of 62 cases. 65 000 regions of interests were extracted from pre-treatment CT images to train a deep-learning convolution neural network (DL-CNN) for tumor boundary detection using leave-one-case-out cross-validation...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28105469/qin-dawg-validation-of-gradient-nonlinearity-bias-correction-workflow-for-quantitative-diffusion-weighted-imaging-in-multicenter-trials
#13
Dariya I Malyarenko, Lisa J Wilmes, Lori R Arlinghaus, Michael A Jacobs, Wei Huang, Karl G Helmer, Bachir Taouli, Thomas E Yankeelov, David Newitt, Thomas L Chenevert
Previous research has shown that system-dependent gradient nonlinearity (GNL) introduces a significant spatial bias (nonuniformity) in apparent diffusion coefficient (ADC) maps. Here, the feasibility of centralized retrospective system-specific correction of GNL bias for quantitative diffusion-weighted imaging (DWI) in multisite clinical trials is demonstrated across diverse scanners independent of the scanned object. Using corrector maps generated from system characterization by ice-water phantom measurement completed in the previous project phase, GNL bias correction was performed for test ADC measurements from an independent DWI phantom (room temperature agar) at two offset locations in the bore...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28105468/simulating-the-effect-of-spectroscopic-mri-as-a-metric-for-radiation-therapy-planning-in-patients-with-glioblastoma
#14
J Scott Cordova, Shravan Kandula, Saumya Gurbani, Jim Zhong, Mital Tejani, Oluwatosin Kayode, Kirtesh Patel, Roshan Prabhu, Eduard Schreibmann, Ian Crocker, Chad A Holder, Hyunsuk Shim, Hui-Kuo Shu
Due to glioblastoma's infiltrative nature, an optimal radiation therapy (RT) plan requires targeting infiltration not identified by anatomical magnetic resonance imaging (MRI). Here, high-resolution, whole-brain spectroscopic MRI (sMRI) is used to describe tumor infiltration alongside anatomical MRI and simulate the degree to which it modifies RT target planning. In 11 patients with glioblastoma, data from preRT sMRI scans were processed to give high-resolution, whole-brain metabolite maps normalized by contralateral white matter...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28090589/spiral-perfusion-imaging-with-consecutive-echoes-spice%C3%A2-for-the-simultaneous-mapping-of-dsc-and-dce-mri-parameters-in-brain-tumor-patients-theory-and-initial-feasibility
#15
Eric S Paulson, Douglas E Prah, Kathleen M Schmainda
Dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) are the perfusion imaging techniques most frequently used to probe the angiogenic character of brain neoplasms. With these methods, T1- and T2/T2*-weighted imaging sequences are used to image the distribution of gadolinium (Gd)-based contrast agents. However, it is well known that Gd exhibits combined T1, T2, and T2* shortening effects in tissue, and therefore, the results of both DCE- and DSC-MRI can be confounded by these opposing effects...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28090588/quantitative-magnetization-transfer-imaging-of-the-breast-at-3-0-t-reproducibility-in-healthy-volunteers
#16
Lori R Arlinghaus, Richard D Dortch, Jennifer G Whisenant, Hakmook Kang, Richard G Abramson, Thomas E Yankeelov
Quantitative magnetization transfer magnetic resonance imaging provides a means for indirectly detecting changes in the macromolecular content of tissue noninvasively. A potential application is the diagnosis and assessment of treatment response in breast cancer; however, before quantitative magnetization transfer imaging can be reliably used in such settings, the technique's reproducibility in healthy breast tissue must be established. Thus, this study aims to establish the reproducibility of the measurement of the macromolecular-to-free water proton pool size ratio (PSR) in healthy fibroglandular (FG) breast tissue...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28083563/the-quantitative-imaging-network-in-precision-medicine
#17
Robert J Nordstrom
Precision medicine is a healthcare model that seeks to incorporate a wealth of patient information to identify and classify disease progression and to provide tailored therapeutic solutions for individual patients. Interventions are based on knowledge of molecular and mechanistic causes, pathogenesis and pathology of disease. Individual characteristics of the patients are then used to select appropriate healthcare options. Imaging is playing an increasingly important role in identifying relevant characteristics that help to stratify patients for different interventions...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066810/dynamic-susceptibility-contrast-mri-quantification-software-tool-development-and-evaluation
#18
Panagiotis Korfiatis, Timothy L Kline, Zachary S Kelm, Rickey E Carter, Leland S Hu, Bradley J Erickson
Relative cerebral blood volume (rCBV) is a magnetic resonance imaging biomarker that is used to differentiate progression from pseudoprogression in patients with glioblastoma multiforme, the most common primary brain tumor. However, calculated rCBV depends considerably on the software used. Automating all steps required for rCBV calculation is important, as user interaction can lead to increased variability and possible inaccuracies in clinical decision-making. Here, we present an automated tool for computing rCBV from dynamic susceptibility contrast-magnetic resonance imaging that includes leakage correction...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066809/deep-feature-transfer-learning-in-combination-with-traditional-features-predicts-survival-among-patients-with-lung-adenocarcinoma
#19
Rahul Paul, Samuel H Hawkins, Yoganand Balagurunathan, Matthew B Schabath, Robert J Gillies, Lawrence O Hall, Dmitry B Goldgof
Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28066808/effect-of-mr-imaging-contrast-thresholds-on-prediction-of-neoadjuvant-chemotherapy-response-in-breast-cancer-subtypes-a-subgroup-analysis-of-the-acrin-6657-i-spy-1-trial
#20
Wen Li, Vignesh Arasu, David C Newitt, Ella F Jones, Lisa Wilmes, Jessica Gibbs, John Kornak, Bonnie N Joe, Laura J Esserman, Nola M Hylton
Functional tumor volume (FTV) measurements by dynamic contrast-enhanced magnetic resonance imaging can predict treatment outcomes for women receiving neoadjuvant chemotherapy for breast cancer. Here, we explore whether the contrast thresholds used to define FTV could be adjusted by breast cancer subtype to improve predictive performance. Absolute FTV and percent change in FTV (ΔFTV) at sequential time-points during treatment were calculated and investigated as predictors of pathologic complete response at surgery...
December 2016: Tomography: a Journal for Imaging Research
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