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Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.

Tomography, Volume 2, Issue 4 (December 2016) – 26 articles

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2652 KiB  
Article
Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation
by Panagiotis Korfiatis, Timothy L. Kline, Zachary S. Kelm, Rickey E. Carter, Leland S. Hu and Bradley J. Erickson
Tomography 2016, 2(4), 448-456; https://doi.org/10.18383/j.tom.2016.00172 - 1 Dec 2016
Cited by 7 | Viewed by 801
Abstract
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 [...] Read more.
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. The entrance and exit bolus time points are automatically calculated using wavelet-based detection. The proposed tool is compared with 3 Food and Drug Administration-approved software packages, 1 automatic and 2 requiring user interaction, on a data set of 43 patients. We also evaluate manual and automated white matter (WM) selection for normalization of the cerebral blood volume maps. Our system showed good agreement with 2 of the 3 software packages. The intraclass correlation coefficient for all comparisons between the same software operated by different people was >0.880, except for FuncTool when operated by user 1 versus user 2. Little variability in agreement between software tools was observed when using different WM selection techniques. Our algorithm for automatic rCBV calculation with leakage correction and automated WM selection agrees well with 2 out of the 3 FDA-approved software packages. Full article
950 KiB  
Article
Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer
by Lisa J. Wilmes, Wen Li, Hee Jung Shin, David C. Newitt, Evelyn Proctor, Roy Harnish and Nola M. Hylton
Tomography 2016, 2(4), 438-447; https://doi.org/10.18383/j.tom.2016.00271 - 1 Dec 2016
Cited by 14 | Viewed by 742
Abstract
In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment [...] Read more.
In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment and after 3 cycles of taxane-based therapy (early treatment). Tumor fractional anisotropy (FA), principal eigenvalues (λ1, λ2, and λ3), and apparent diffusion coefficient (ADC) were estimated for tumor regions of interest drawn on DTI data. The association between DTI metrics and final tumor volume change was evaluated with Spearman rank correlation. DTI metrics were investigated as predictors of pathological complete response (pCR) by calculating the area under the receiver operating characteristic curve (AUC). Early changes in tumor FA and ADC significantly correlated with final tumor volume change post therapy (ρ = −0.38, P = .03 and ρ = −0.71, P < .001, respectively). Pretreatment tumor ADC was significantly lower in the pCR than in the non-pCR group (P = .04). At early treatment, patients with pCR had significantly higher percent changes of tumor λ1, λ2, λ3, and ADC than those without pCR. The AUCs for early percent changes in tumor FA and ADC were 0.60 and 0.83, respectively. The early percent changes in tumor eigenvalues and ADC were the strongest DTI-derived predictors of pCR. Although early percent change in tumor FA had a weak association with pCR, the significant correlation with final tumor volume change suggests that this metric changes with therapy and may merit further evaluation. Full article
923 KiB  
Article
Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features
by 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 and Dmitry Goldgof
Tomography 2016, 2(4), 430-437; https://doi.org/10.18383/j.tom.2016.00235 - 1 Dec 2016
Cited by 98 | Viewed by 2210
Abstract
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 [...] Read more.
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. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy. Full article
1886 KiB  
Article
Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study
by Kenny H. Cha, Lubomir M. Hadjiiski, Ravi K. Samala, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Chintana Paramagul, Ajjai Alva and Alon Z. Weizer
Tomography 2016, 2(4), 421-429; https://doi.org/10.18383/j.tom.2016.00184 - 1 Dec 2016
Cited by 54 | Viewed by 1230
Abstract
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 [...] Read more.
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. The results were compared to our previous AI-CALS method. For all lesions in the data set, the longest diameter and its perpendicular were measured by two radiologists, and 3D manual segmentation was obtained from one radiologist. The World Health Organization (WHO) criteria and the Response Evaluation Criteria In Solid Tumors (RECIST) were calculated, and the prediction accuracy of complete response to chemotherapy was estimated by the area under the receiver operating characteristic curve (AUC). The AUCs were 0.73 ± 0.06, 0.70 ± 0.07, and 0.70 ± 0.06, respectively, for the volume change calculated using DL-CNN segmentation, the AI-CALS and the manual contours. The differences did not achieve statistical significance. The AUCs using the WHO criteria were 0.63 ± 0.07 and 0.61 ± 0.06, while the AUCs using RECIST were 0.65 ± 007 and 0.63 ± 0.06 for the two radiologists, respectively. Our results indicate that DL-CNN can produce accurate bladder cancer segmentation for calculation of tumor size change in response to treatment. The volume change performed better than the estimations from the WHO criteria and RECIST for the prediction of complete response. Full article
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Article
Comparison between 3-Scan Trace and Diagonal Body Diffusion-Weighted Imaging Acquisitions: A Phantom and Volunteer Study
by Stefanie J. Hectors, Mathilde Wagner, Idoia Corcuera-Solano, Martin Kang, Alto Stemmer, Michael A. Boss and Bachir Taouli
Tomography 2016, 2(4), 411-420; https://doi.org/10.18383/j.tom.2016.00229 - 1 Dec 2016
Cited by 9 | Viewed by 858
Abstract
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 [...] Read more.
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/mm2) and in the abdomen (b = 50/400/800 s/mm2) and pelvis (b = 50/1000/1600 s/mm2) of 2 male volunteers at 1.5 T and 3.0 T. Phantom estimated signal-to-noise ratio (eSNR) was also measured. Two independent observers assessed the image quality on a 5-point scale. In the phantom, image quality was similar between tDWI and dDWI, with equivalent ADC quantification (mean coefficient of variation [CV] between sequences: 1.4% ± 1.2% at 1.5 T and 0.7% ± 0.7% at 3.0 T). Phantom eSNR was similar for both tDWI and dDWI, except for a significantly lower eSNR for b900 of dDWI at 3.0 T (P = .006). In the volunteers, the CV values between tDWI and dDWI were higher than those in the phantom (CV range: abdominal organs, 1.3%–13.3%; pelvic organs, 0.6%–5.7%). A trend toward significant better image quality for dDWI compared with tDWI was observed for b800 (abdomen) at 3.0 T and for b1000 and b1600 (pelvis) at 1.5 T (P = .063 to .066). Our data suggest that dDWI may provide better image quality than tDWI without affecting ADC quantification, needing confirmation in a future clinical study. Full article
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Article
A Response Assessment Platform for Development and Validation of Imaging Biomarkers in Oncology
by Hao Yang, Lawrence H. Schwartz and Binsheng Zhao
Tomography 2016, 2(4), 406-410; https://doi.org/10.18383/j.tom.2016.00223 - 1 Dec 2016
Cited by 18 | Viewed by 664
Abstract
Quantitative imaging biomarkers are increasingly used in both oncology clinical trials and clinical practice aid evaluation of tumor response to novel therapies. To obtain these biomarkers, and to ensure smooth clinical adoption once they have been validated, it is critical to develop reliable [...] Read more.
Quantitative imaging biomarkers are increasingly used in both oncology clinical trials and clinical practice aid evaluation of tumor response to novel therapies. To obtain these biomarkers, and to ensure smooth clinical adoption once they have been validated, it is critical to develop reliable computer-aided methods and a workflow-efficient imaging platform for integration in research and clinical settings. Here, we present a volumetric response assessment system developed based on an open-source image-viewing platform (WEASIS). Our response assessment system is designed using the Model–View–Controller concept, and it offers standard image-viewing and -manipulation functions, efficient tumor segmentation and quantification algorithms, and a reliable database containing tumor segmentation and measurement results. This prototype system is currently used in our research laboratory to foster the development and validation of new quantitative imaging biomarkers including the volumetric computed tomography technique as a more accurate and early assessment method of solid tumor response to targeted therapy and immunotherapy. Full article
1319 KiB  
Article
QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials
by Dariya I. Malyarenko, Lisa J. Wilmes, Lori R. Arlinghaus, Michael A. Jacobs, Wei Huang, Karl G. Helmer, Bachir Taouli, Thomas E. Yankeelov, David Newitt and Thomas L. Chenevert
Tomography 2016, 2(4), 396-405; https://doi.org/10.18383/j.tom.2016.00214 - 1 Dec 2016
Cited by 13 | Viewed by 728
Abstract
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 multi-site clinical trials is demonstrated [...] Read more.
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 multi-site 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. The precomputed three-dimensional GNL correctors were retrospectively applied to test DWI scans by the central analysis site. The correction was blinded to reference DWI of the agar phantom at magnet isocenter where the GNL bias is negligible. The performance was evaluated from changes in ADC region of interest histogram statistics before and after correction with respect to the unbiased reference ADC values provided by sites. Both absolute error and nonuniformity of the ADC map induced by GNL (median, 12%; range, −35% to +10%) were substantially reduced by correction (7-fold in median and 3-fold in range). The residual ADC nonuniformity errors were attributed to measurement noise and other non-GNL sources. Correction of systematic GNL bias resulted in a 2-fold decrease in technical variability across scanners (down to site temperature range). The described validation of GNL bias correction marks progress toward implementation of this technology in multicenter trials that utilize quantitative DWI. Full article
477 KiB  
Article
Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival among Patients with Lung Adenocarcinoma
by Rahul Paul, Samuel H. Hawkins, Yoganand Balagurunathan, Matthew Schabath, Robert J. Gillies, Lawrence O. Hall and Dmitry B. Goldgof
Tomography 2016, 2(4), 388-395; https://doi.org/10.18383/j.tom.2016.00211 - 1 Dec 2016
Cited by 105 | Viewed by 2333
Abstract
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 [...] Read more.
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. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier. Full article
1689 KiB  
Article
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
by Wen Li, Vignesh Arasu, David C. Newitt, Ella F. Jones, Lisa Wilmes, Jessica Gibbs, John Kornak, Bonnie N. Joe, Laura J. Esserman and Nola M. Hylton
Tomography 2016, 2(4), 378-387; https://doi.org/10.18383/j.tom.2016.00247 - 1 Dec 2016
Cited by 18 | Viewed by 832
Abstract
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 [...] Read more.
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. Early percent enhancement threshold (PEt) and signal enhancement ratio threshold (SERt) were varied. The predictive performance of resulting FTV predictors was evaluated using the area under the receiver operating characteristic curve. A total number of 116 patients were studied both as a full cohort and in the following groups defined by hormone receptor (HR) and HER2 receptor subtype: 45 HR+/HER2−, 39 HER2+, and 30 triple negatives. High AUCs were found at different ranges of PEt and SERt levels in different subtypes. Findings from this study suggest that the predictive performance to treatment response by MRI varies by contrast thresholds, and that pathologic complete response prediction may be improved through subtype-specific contrast enhancement thresholds. A validation study is underway with a larger patient population. Full article
631 KiB  
Article
An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features
by Ravindra Patil, Geetha Mahadevaiah and Andre Dekker
Tomography 2016, 2(4), 374-377; https://doi.org/10.18383/j.tom.2016.00244 - 1 Dec 2016
Cited by 28 | Viewed by 949
Abstract
Non–small cell lung cancer contributes toward 85% of all lung cancer burden. Tumor histology (squamous cell carcinoma, large cell carcinoma, and adenocarcinoma and “not otherwise specified”) has prognostic significance, and it is therefore imperative to identify tumor histology for personalized medicine; however, biopsies [...] Read more.
Non–small cell lung cancer contributes toward 85% of all lung cancer burden. Tumor histology (squamous cell carcinoma, large cell carcinoma, and adenocarcinoma and “not otherwise specified”) has prognostic significance, and it is therefore imperative to identify tumor histology for personalized medicine; however, biopsies are not always possible and carry significant risk of complications. Here, we have used Radiomics, which provides an exhaustive number of informative features, to aid in diagnosis and therapeutic outcome of tumor characteristics in a noninvasive manner. This study evaluated radiomic features of non–small cell lung cancer to identify tumor histopathology. We included 317 subjects and classified the underlying tumor histopathology into its 4 main subtypes. The performance of the current approach was determined to be 20% more accurate than that of an approach considering only the volumetric- and shape-based features. Full article
1746 KiB  
Article
Simulating the Effect of Spectroscopic MRI as a Metric for Radiation Therapy Planning in Patients with Glioblastoma
by 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 and Hui-Kuo Shu
Tomography 2016, 2(4), 366-373; https://doi.org/10.18383/j.tom.2016.00187 - 1 Dec 2016
Cited by 19 | Viewed by 823
Abstract
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 [...] Read more.
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. Maps depicting choline to N-Acetylaspartate (Cho/NAA) ratios were registered to contrast-enhanced T1-weighted RT planning MRI for each patient. Volumes depicting metabolic abnormalities (1.5-, 1.75-, and 2.0-fold increases in Cho/NAA ratios) were compared with conventional target volumes and contrast-enhancing tumor at recurrence. sMRI-modified RT plans were generated to evaluate target volume coverage and organ-at-risk dose constraints. Conventional clinical target volumes and Cho/NAA abnormalities identified significantly different regions of microscopic infiltration with substantial Cho/NAA abnormalities falling outside of the conventional 60 Gy isodose line (41.1, 22.2, and 12.7 cm3, respectively). Clinical target volumes using Cho/NAA thresholds exhibited significantly higher coverage of contrast enhancement at recurrence on average (92.4%, 90.5%, and 88.6%, respectively) than conventional plans (82.5%). sMRI-based plans targeting tumor infiltration met planning objectives in all cases with no significant change in target coverage. In 2 cases, the sMRI-modified plan exhibited better coverage of contrast-enhancing tumor at recurrence than the original plan. Integration of the high-resolution, whole-brain sMRI into RT planning is feasible, resulting in RT target volumes that can effectively target tumor infiltration while adhering to conventional constraints. Full article
595 KiB  
Article
Test–Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific?
by Janna E. van Timmeren, Ralph T.H. Leijenaar, Wouter van Elmpt, Jiazhou Wang, Zhen Zhang, André Dekker and Philippe Lambin
Tomography 2016, 2(4), 361-365; https://doi.org/10.18383/j.tom.2016.00208 - 1 Dec 2016
Cited by 128 | Viewed by 1972
Abstract
Radiomics is an objective method for extracting quantitative information from medical images. However, in radiomics, standardization, overfitting, and generalization are major challenges to be overcome. Test–retest experiments can be used to select robust radiomic features that have minimal variation. Currently, it is unknown [...] Read more.
Radiomics is an objective method for extracting quantitative information from medical images. However, in radiomics, standardization, overfitting, and generalization are major challenges to be overcome. Test–retest experiments can be used to select robust radiomic features that have minimal variation. Currently, it is unknown whether they should be identified for each disease (disease specific) or are only imaging device-specific (computed tomography [CT]-specific). Here, we performed a test–retest analysis on CT scans of 40 patients with rectal cancer in a clinical setting. Correlation between radiomic features was assessed using the concordance correlation coefficient (CCC). In total, only 9/542 features have a CCC > 0.85. Furthermore, results were compared with the test–retest results on CT scans of 27 patients with lung cancer with a 15-minute interval. Results show that 446/542 features have a higher CCC for the test–retest analysis of the data set of patients with lung cancer than for patients with rectal cancer. The importance of controlling factors such as scanners, imaging protocol, reconstruction methods, and time points in a radiomics analysis is shown. Moreover, the results imply that test–retest analyses should be performed before each radiomics study. More research is required to independently evaluate the effect of each factor. Full article
1097 KiB  
Article
Evaluation of Cross-Calibrated 68Ge/68Ga Phantoms for Assessing PET/CT Measurement Bias in Oncology Imaging for Single- and Multicenter Trials
by Darrin W. Byrd, Robert K. Doot, Keith C. Allberg, Lawrence R. MacDonald, Wendy A. McDougald, Brian F. Elston, Hannah M. Linden and Paul E. Kinahan
Tomography 2016, 2(4), 353-360; https://doi.org/10.18383/j.tom.2016.00205 - 1 Dec 2016
Cited by 16 | Viewed by 731
Abstract
Quantitative PET imaging is an important tool for clinical trials evaluating the response of cancers to investigational therapies. The standardized uptake value, used as a quantitative imaging biomarker, is dependent on multiple parameters that may contribute bias and variability. The use of long-lived, [...] Read more.
Quantitative PET imaging is an important tool for clinical trials evaluating the response of cancers to investigational therapies. The standardized uptake value, used as a quantitative imaging biomarker, is dependent on multiple parameters that may contribute bias and variability. The use of long-lived, sealed PET calibration phantoms offers the advantages of known radioactivity activity concentration and simpler use than aqueous phantoms. We evaluated scanner and dose calibrator sources from two batches of commercially available kits, together at a single site and distributed across a local multicenter PET imaging network. We found that radioactivity concentration was uniform within the phantoms. Within the regions of interest drawn in the phantom images, coefficients of variation of voxel values were less than 2%. Across phantoms, coefficients of variation for mean signal were close to 1%. Biases of the standardized uptake value estimated with the kits varied by site and were seen to change in time by approximately ±5%. We conclude that these biases cannot be assumed constant over time. The kits provide a robust method to monitor PET scanner and dose calibrator biases, and resulting biases in standardized uptake values. Full article
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Article
Temporal Feature Extraction from DCE-MRI to Identify Poorly Perfused Subvolumes of Tumors Related to Outcomes of Radiation Therapy in Head and Neck Cancer
by Daekeun You, Madhava Aryal, Stuart E. Samuels, Avraham Eisbruch and Yue Cao
Tomography 2016, 2(4), 341-352; https://doi.org/10.18383/j.tom.2016.00199 - 1 Dec 2016
Cited by 7 | Viewed by 718
Abstract
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 [...] Read more.
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. Subvolumes with LBV were then assembled from the classified voxels with LBV. The model was trained and validated on independent datasets created from 456 873 DCE curves. The resultant subvolumes were compared to ones derived by a 2-step method via pharmacokinetic modeling of blood volume, and evaluated for classification accuracy and volumetric similarity by DSC. The proposed model achieved an average voxel-level classification accuracy and DSC of 82% and 0.72, respectively. Also, the model showed tolerance on different acquisition parameters of DCE-MRI. The model could be directly used for outcome prediction and therapy assessment in radiation therapy of HN cancers, or even supporting boost target definition in adaptive clinical trials with further validation. The model is fully automatable, extendable, and scalable to extract temporal features of DCE-MRI in other tumors. Full article
1327 KiB  
Article
Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning
by Panagiotis Korfiatis, Timothy L. Kline and Bradley J. Erickson
Tomography 2016, 2(4), 334-340; https://doi.org/10.18383/j.tom.2016.00166 - 1 Dec 2016
Cited by 45 | Viewed by 1053
Abstract
We present a deep convolutional neural network application based on autoencoders aimed at segmentation of increased signal regions in fluid-attenuated inversion recovery magnetic resonance imaging images. The convolutional autoencoders were trained on the publicly available Brain Tumor Image Segmentation Benchmark (BRATS) data set, [...] Read more.
We present a deep convolutional neural network application based on autoencoders aimed at segmentation of increased signal regions in fluid-attenuated inversion recovery magnetic resonance imaging images. The convolutional autoencoders were trained on the publicly available Brain Tumor Image Segmentation Benchmark (BRATS) data set, and the accuracy was evaluated on a data set where 3 expert segmentations were available. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to provide the ground truth for comparison, and Dice coefficient, Jaccard coefficient, true positive fraction, and false negative fraction were calculated. The proposed technique was within the interobserver variability with respect to Dice, Jaccard, and true positive fraction. The developed method can be used to produce automatic segmentations of tumor regions corresponding to signal-increased fluid-attenuated inversion recovery regions. Full article
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Article
Comparison of Voxel-Wise Tumor Perfusion Changes Measured with Dynamic Contrast-Enhanced (DCE) MRI and Volumetric DCE CT in Patients with Metastatic Brain Cancer Treated with Radiosurgery
by Catherine Coolens, Brandon Driscoll, Warren Foltz, Carly Pellow, Cynthia Menard and Caroline Chung
Tomography 2016, 2(4), 325-333; https://doi.org/10.18383/j.tom.2016.00178 - 1 Dec 2016
Cited by 13 | Viewed by 663
Abstract
Dynamic contrast-enhanced (DCE)-MRI metrics are evaluated against volumetric DCE-CT quantitative parameters as a standard for tracer-kinetic validation using a common 4-dimensional temporal dynamic analysis platform in tumor perfusion measurements following stereotactic radiosurgery (SRS) for brain metastases. Patients treated with SRS as part of [...] Read more.
Dynamic contrast-enhanced (DCE)-MRI metrics are evaluated against volumetric DCE-CT quantitative parameters as a standard for tracer-kinetic validation using a common 4-dimensional temporal dynamic analysis platform in tumor perfusion measurements following stereotactic radiosurgery (SRS) for brain metastases. Patients treated with SRS as part of Research Ethics Board-approved clinical trials underwent volumetric DCE-CT and DCE-MRI at baseline, then at 7 and 21 days after SRS. Temporal dynamic analysis was used to create 3-dimensional pharmacokinetic parameter maps for both modalities. Individual vascular input functions were selected for DCE-CT and a population function was used for DCE-MRI. Semiquantitative and pharmacokinetic DCE parameters were assessed using a modified Tofts model within each tumor at every time point for both modalities for characterization of perfusion and capillary permeability, as well as their dependency on precontrast relaxation times (TRs), T10, and input function. Direct voxel-to-voxel Pearson analysis showed statistically significant correlations between CT and magnetic resonance which peaked at day 7 for Ktrans (R = 0.74, P ≤ .0001). The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxel-wise T10 maps (R = 0.575, P < .001) instead of assigning a fixed T10 value. Comparison of histogram features showed statistically significant correlations between modalities over all tumors for median Ktrans (R = 0.42, P = .01), median area under the enhancement curve (iAUC90) (R = 0.55, P < .01), and median iAUC90 skewness (R = 0.34, P = .03). Statistically significant, strong correlations were found for voxel-wise Ktrans, iAUC90, and ve values between DCE-CT and DCE-MRI. For DCE-MRI, the implementation of voxel-wise T10 maps plays a key role in ensuring the accuracy of heterogeneous pharmacokinetic maps. Full article
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Article
[18F]ML-10 PET: Initial Experience in Glioblastoma Multiforme Therapy Response Assessment
by Matthew J. Oborski, Charles M. Laymon, Frank S. Lieberman, Yongxian Qian, Jan Drappatz and James M. Mountz
Tomography 2016, 2(4), 317-324; https://doi.org/10.18383/j.tom.2016.00175 - 1 Dec 2016
Cited by 6 | Viewed by 574
Abstract
The ability to assess tumor apoptotic response to therapy could provide a direct and prompt measure of therapeutic efficacy. 18F-labeled 2-(5-fluoro-pentyl)-2-methyl-malonic acid ([18F]ML-10) is proposed as a positron emission tomography (PET) apoptosis imaging radiotracer. This manuscript presents initial experience using [...] Read more.
The ability to assess tumor apoptotic response to therapy could provide a direct and prompt measure of therapeutic efficacy. 18F-labeled 2-(5-fluoro-pentyl)-2-methyl-malonic acid ([18F]ML-10) is proposed as a positron emission tomography (PET) apoptosis imaging radiotracer. This manuscript presents initial experience using [18F]ML-10 PET to predict therapeutic response in 4 patients with human glioblastoma multiforme. Each patient underwent [18F]ML-10 PET and contrast-enhanced magnetic resonance imaging (MRI) before (baseline) and at ∼2–3 weeks after therapy (early-therapy assessment). All PET and MRI data were acquired using a Siemens BioGraph mMR integrated PET/MRI scanner. PET acquisitions commenced 120 minutes after injection with 10 mCi of [18F]ML-10. Changes in [18F]ML-10 standard uptake values were assessed in conjunction with MRI changes. Time-to-progression was used as the outcome measure. One patient, ML-10 #4, underwent additional sodium-23 (23Na) MRI at baseline and early-therapy assessment. Siemens 3 T Magnetom Tim Trio scanner with a dual-tuned (1H-23Na) head coil was used for 23Na-MRI, acquiring two three-dimensional single-quantum sodium images at two echo times (TE). Volume-fraction-weighted bound sodium concentration was quantified through pixel-by-pixel subtraction of the two single-quantum sodium images. In the cases presented, [18F]ML-10 uptake changes were not clearly related to time-to-progression. We suggest that this may be because the tumors are undergoing varying rates of cell death and growth. Acquisition of complementary measures of tumor cell proliferation or viability may aid in the interpretation of PET apoptosis imaging. Full article
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Article
Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging
by Wei Huang, Brooke R. Beckett, Alina Tudorica, Janelle M. Meyer, Aneela Afzal, Yiyi Chen, Atiya Mansoor, James B. Hayden, Yee-Cheen Doung, Arthur Y. Hung, Megan L. Holtorf, Torrie J. Aston and Christopher W. Ryan
Tomography 2016, 2(4), 308-316; https://doi.org/10.18383/j.tom.2016.00202 - 1 Dec 2016
Cited by 22 | Viewed by 799
Abstract
This study aims to assess the utility of quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters in comparison with imaging tumor size for early prediction and evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy. In total, 20 patients with intermediate- to [...] Read more.
This study aims to assess the utility of quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters in comparison with imaging tumor size for early prediction and evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy. In total, 20 patients with intermediate- to high-grade soft tissue sarcomas received either a phase I trial regimen of sorafenib + chemoradiotherapy (n = 8) or chemoradiotherapy only (n = 12), and underwent DCE-MRI at baseline, after 2 weeks of treatment with sorafenib or after the first chemotherapy cycle, and after therapy completion. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed using the Shutter-Speed model. After only 2 weeks of treatment with sorafenib or after 1 chemotherapy cycle, Ktrans (rate constant for plasma/interstitium contrast agent transfer) and its percent change were good early predictors of optimal versus suboptimal pathological response with univariate logistic regression C statistics values of 0.90 and 0.80, respectively, whereas RECIST LD percent change was only a fair predictor (C = 0.72). Post-therapy Ktrans, ve (extravascular and extracellular volume fraction), and kep (intravasation rate constant), not RECIST LD, were excellent (C > 0.90) markers of therapy response. Several DCE-MRI parameters before, during, and after therapy showed significant (P < .05) correlations with percent necrosis of resected tumor specimens. In conclusion, absolute values and percent changes of quantitative DCE-MRI parameters provide better early prediction and evaluation of the pathological response of soft tissue sarcoma to preoperative chemoradiotherapy than the conventional measurement of imaging tumor size change. Full article
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Article
Spiral Perfusion Imaging with Consecutive Echoes (SPICE™) for the Simultaneous Mapping of DSC- and DCE-MRI Parameters in Brain Tumor Patients: Theory and Initial Feasibility
by Eric S. Paulson, Douglas E. Prah and Kathleen M. Schmainda
Tomography 2016, 2(4), 295-307; https://doi.org/10.18383/j.tom.2016.00217 - 1 Dec 2016
Cited by 26 | Viewed by 1003
Abstract
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 [...] Read more.
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. In particular, residual susceptibility effects compete with T1 shortening, which can confound DCE-MRI parameters, whereas dipolar T1 and T2 leakage and residual susceptibility effects can confound DSC-MRI parameters. We introduce here a novel perfusion imaging acquisition and postprocessing method termed Spiral Perfusion Imaging with Consecutive Echoes (SPICE) that can be used to simultaneously acquire DCE- and DSC-MRI data, which requires only a single dose of the Gd contrast agent, does not require the collection of a precontrast T1 map for DCE-MRI processing, and eliminates the confounding contrast agent effects due to contrast extravasation. A detailed mathematical description of SPICE is provided here along with a demonstration of its utility in patients with high-grade glioma. Full article
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Article
A Rapid Segmentation-Insensitive “Digital Biopsy” Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non–Small Cell Lung Cancer
by Sebastian Echegaray, Viswam Nair, Michael Kadoch, Ann Leung, Daniel Rubin, Olivier Gevaert and Sandy Napel
Tomography 2016, 2(4), 283-294; https://doi.org/10.18383/j.tom.2016.00163 - 1 Dec 2016
Cited by 19 | Viewed by 1077
Abstract
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 [...] Read more.
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. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of 0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required. Full article
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Article
Accrual Patterns for Clinical Studies Involving Quantitative Imaging: Results of an NCI Quantitative Imaging Network (QIN) Survey
by Brenda F. Kurland, Sameer Aggarwal, Thomas E. Yankeelov, Elizabeth R. Gerstner, James M. Mountz, Hannah M. Linden, Ella F. Jones, Kellie L. Bodeker and John M. Buatti
Tomography 2016, 2(4), 276-282; https://doi.org/10.18383/j.tom.2016.00169 - 1 Dec 2016
Cited by 1 | Viewed by 596
Abstract
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 [...] Read more.
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. The median percent annual accrual attained was 94.5% (range, 3%–350%). The most commonly selected barrier to recruitment (n = 11/25, 44%) was that “patients decline participation,” followed by “too few eligible patients” (n = 10/25, 40%). In a forced choice for the single greatest recruitment challenge, “too few eligible patients” was the most common response (n = 8/25, 32%). Quantitative analysis and qualitative responses suggested that interactions among institutional, physician, and patient factors contributed to accrual success and challenges. Multidisciplinary collaboration in trial design and execution is essential to accrual success, with attention paid to ensuring and communicating potential trial benefits to enrolled and future patients. Full article
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Article
Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping
by Lauren Keith, Brian D. Ross, Craig J. Galbán, Gary D. Luker, Stefanie Galbán, Binsheng Zhao, Xiaotao Guo, Thomas L. Chenevert and Benjamin A. Hoff
Tomography 2016, 2(4), 267-275; https://doi.org/10.18383/j.tom.2016.00181 - 1 Dec 2016
Cited by 3 | Viewed by 593
Abstract
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 [...] Read more.
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. Development of the parametric response mapping (PRM) applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor. Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for patient management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans. The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice. Full article
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Article
Quantitative Magnetization Transfer Imaging of the Breast at 3.0 T: Reproducibility in Healthy Volunteers
by Lori R. Arlinghaus, Richard D. Dortch, Jennifer G. Whisenant, Hakmook Kang, Richard G. Abramson and Thomas E. Yankeelov
Tomography 2016, 2(4), 260-266; https://doi.org/10.18383/j.tom.2016.00142 - 1 Dec 2016
Cited by 9 | Viewed by 684
Abstract
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 [...] Read more.
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. Thirteen women with no history of breast disease were scanned twice within a single scanning session, with repositioning between scans. Eleven women had appreciable FG tissue for test–retest measurements. Mean PSR values for the FG tissue ranged from 9.5% to 16.7%. The absolute value of the difference between 2 mean PSR measurements for each volunteer ranged from 0.1% to 2.1%. The 95% confidence interval for the mean difference was ±0.75%, and the repeatability value was 2.39%. These results indicate that the expected measurement variability would be ±0.75% for a cohort of a similar size and would be ±2.39% for an individual, suggesting that future studies of change in PSR in patients with breast cancer are feasible. Full article
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Article
Bloch–Siegert B1-Mapping Improves Accuracy and Precision of Longitudinal Relaxation Measurements in the Breast at 3 T
by Jennifer G. Whisenant, Richard D. Dortch, William Grissom, Hakmook Kang, Lori R. Arlinghaus and Thomas E. Yankeelov
Tomography 2016, 2(4), 250-259; https://doi.org/10.18383/j.tom.2016.00133 - 1 Dec 2016
Cited by 12 | Viewed by 683
Abstract
Variable flip angle (VFA) sequences are a popular method of calculating T1 values, which are required in a quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). B1 inhomogeneities are substantial in the breast at 3 T, and these errors [...] Read more.
Variable flip angle (VFA) sequences are a popular method of calculating T1 values, which are required in a quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). B1 inhomogeneities are substantial in the breast at 3 T, and these errors negatively impact the accuracy of the VFA approach, thus leading to large errors in the DCE-MRI parameters that could limit clinical adoption of the technique. This study evaluated the ability of Bloch–Siegert B1 mapping to improve the accuracy and precision of VFA-derived T1 measurements in the breast. Test–retest MRI sessions were performed on 16 women with no history of breast disease. T1 was calculated using the VFA sequence, and B1 field variations were measured using the Bloch–Siegert methodology. As a gold standard, inversion recovery (IR) measurements of T1 were performed. Fibroglandular tissue and adipose tissue from each breast were segmented using the IR images, and the mean T1 was calculated for each tissue. Accuracy was evaluated by percent error (%err). Reproducibility was assessed via the 95% confidence interval (CI) of the mean difference and repeatability coefficient (r). After B1 correction, %err significantly (P < 0.001) decreased from 17% to 8.6%, and the 95% CI and r decreased from ±94 to ±38 milliseconds and from 276 to 111 milliseconds, respectively. Similar accuracy and reproducibility results were observed in the adipose tissue of the right breast and in both tissues of the left breast. Our data show that Bloch–Siegert B1 mapping improves accuracy and precision of VFA-derived T1 measurements in the breast. Full article
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Article
Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network
by Keyvan Farahani, Jayashree Kalpathy-Cramer, Thomas L. Chenevert, Daniel L. Rubin, John J. Sunderland, Robert J. Nordstrom, John Buatti and Nola Hylton
Tomography 2016, 2(4), 242-249; https://doi.org/10.18383/j.tom.2016.00265 - 1 Dec 2016
Cited by 12 | Viewed by 691
Abstract
The Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI) conducts research in development and validation of imaging tools and methods for predicting and evaluating clinical response to cancer therapy. Members of the network are involved in examining various imaging and image [...] Read more.
The Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI) conducts research in development and validation of imaging tools and methods for predicting and evaluating clinical response to cancer therapy. Members of the network are involved in examining various imaging and image assessment parameters through network-wide cooperative projects. To more effectively use the cooperative power of the network in conducting computational challenges in benchmarking of tools and methods and collaborative projects in analytical assessment of imaging technologies, the QIN Challenge Task Force has developed policies and procedures to enhance the value of these activities by developing guidelines and leveraging NCI resources to help their administration and manage dissemination of results. Challenges and Collaborative Projects (CCPs) are further divided into technical and clinical CCPs. As the first NCI network to engage in CCPs, we anticipate a variety of CCPs to be conducted by QIN teams in the coming years. These will be aimed to benchmark advanced software tools for clinical decision support, explore new imaging biomarkers for therapeutic assessment, and establish consensus on a range of methods and protocols in support of the use of quantitative imaging to predict and assess response to cancer therapy. Full article
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Article
The Quantitative Imaging Network in Precision Medicine
by Robert J. Nordstrom
Tomography 2016, 2(4), 239-241; https://doi.org/10.18383/j.tom.2016.00190 - 1 Dec 2016
Cited by 20 | Viewed by 727
Abstract
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 [...] Read more.
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. However, lack of standards, limitations in image-processing interoperability, and errors in data collection can limit the applicability of imaging in clinical decision support. Quantitative imaging is the attempt to extract reliable, numerical information from images to eliminate qualitative judgments and errors for providing accurate measures of tumor response to therapy or for predicting future response. This issue of Tomography reports quantitative imaging developments made by several members of the National Cancer Institute Quantitative Imaging Network, a program dedicated to the promotion of quantitative imaging methods for clinical decision support. Full article
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