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Showing papers in "Journal of Magnetic Resonance Imaging in 2018"


Journal ArticleDOI
TL;DR: There are concerns over gadolinium deposition from gadolinia‐based contrast agents (GBCA) administration.
Abstract: BACKGROUND There are concerns over gadolinium deposition from gadolinium-based contrast agents (GBCA) administration. PURPOSE To reduce gadolinium dose in contrast-enhanced brain MRI using a deep learning method. STUDY TYPE Retrospective, crossover. POPULATION Sixty patients receiving clinically indicated contrast-enhanced brain MRI. SEQUENCE 3D T1 -weighted inversion-recovery prepped fast-spoiled-gradient-echo (IR-FSPGR) imaging was acquired at both 1.5T and 3T. In 60 brain MRI exams, the IR-FSPGR sequence was obtained under three conditions: precontrast, postcontrast images with 10% low-dose (0.01mmol/kg) and 100% full-dose (0.1 mmol/kg) of gadobenate dimeglumine. We trained a deep learning model using the first 10 cases (with mixed indications) to approximate full-dose images from the precontrast and low-dose images. Synthesized full-dose images were created using the trained model in two test sets: 20 patients with mixed indications and 30 patients with glioma. ASSESSMENT For both test sets, low-dose, true full-dose, and the synthesized full-dose postcontrast image sets were compared quantitatively using peak-signal-to-noise-ratios (PSNR) and structural-similarity-index (SSIM). For the test set comprised of 20 patients with mixed indications, two neuroradiologists scored blindly and independently for the three postcontrast image sets, evaluating image quality, motion-artifact suppression, and contrast enhancement compared with precontrast images. STATISTICAL ANALYSIS Results were assessed using paired t-tests and noninferiority tests. RESULTS The proposed deep learning method yielded significant (n = 50, P 5 dB PSNR gains and >11.0% SSIM). Ratings on image quality (n = 20, P = 0.003) and contrast enhancement (n = 20, P < 0.001) were significantly increased. Compared to true full-dose images, the synthesized full-dose images have a slight but not significant reduction in image quality (n = 20, P = 0.083) and contrast enhancement (n = 20, P = 0.068). Slightly better (n = 20, P = 0.039) motion-artifact suppression was noted in the synthesized images. The noninferiority test rejects the inferiority of the synthesized to true full-dose images for image quality (95% CI: -14-9%), artifacts suppression (95% CI: -5-20%), and contrast enhancement (95% CI: -13-6%). DATA CONCLUSION With the proposed deep learning method, gadolinium dose can be reduced 10-fold while preserving contrast information and avoiding significant image quality degradation. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 5 J. MAGN. RESON. IMAGING 2018;48:330-340.

223 citations


Journal ArticleDOI
TL;DR: These CEST MRI tools show promise for contributing to assessments of cerebral ischemia, neurological disorders, lymphedema, osteoarthritis, muscle physiology, and solid tumors.
Abstract: Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has been developed and employed in multiple clinical imaging research centers worldwide. Selective radiofrequency (RF) saturation pulses with standard 2D and 3D MRI acquisition schemes are now routinely performed, and CEST MRI can produce semiquantitative results using magnetization transfer ratio asymmetry (MTRasym) analysis while accounting for B0 inhomogeneity. Faster clinical CEST MRI acquisition methods and more quantitative acquisition and analysis routines are under development. Endogenous biomolecules with amide, amine, and hydroxyl groups have been detected during clinical CEST MRI studies, and exogenous CEST agents have also been administered to patients. These CEST MRI tools show promise for contributing to assessments of cerebral ischemia, neurological disorders, lymphedema, osteoarthritis, muscle physiology, and solid tumors. This review summarizes the salient features of clinical CEST MRI protocols and critically evaluates the utility of CEST MRI for these clinical imaging applications. Level of Evidence: 5 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:11–27.

189 citations


Journal ArticleDOI
TL;DR: Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty, and more studies to date are small, heterogeneous, and retrospective in nature.
Abstract: This review describes the definition, incidence, clinical implications, and magnetic resonance imaging (MRI) findings of pseudoprogression of brain tumors, in particular, but not limited to, high‐grade glioma. Pseudoprogression is an important clinical problem after brain tumor treatment, interfering not only with day‐to‐day patient care but also the execution and interpretation of clinical trials. Radiologically, pseudoprogression is defined as a new or enlarging area(s) of contrast agent enhancement, in the absence of true tumor growth, which subsides or stabilizes without a change in therapy. The clinical definitions of pseudoprogression have been quite variable, which may explain some of the differences in reported incidences, which range from 9–30%. Conventional structural MRI is insufficient for distinguishing pseudoprogression from true progressive disease, and advanced imaging is needed to obtain higher levels of diagnostic certainty. Perfusion MRI is the most widely used imaging technique to diagnose pseudoprogression and has high reported diagnostic accuracy. Diagnostic performance of MR spectroscopy (MRS) appears to be somewhat higher, but MRS is less suitable for the routine and universal application in brain tumor follow‐up. The combination of MRS and diffusion‐weighted imaging and/or perfusion MRI seems to be particularly powerful, with diagnostic accuracy reaching up to or even greater than 90%. While diagnostic performance can be high with appropriate implementation and interpretation, even a combination of techniques, however, does not provide 100% accuracy. It should also be noted that most studies to date are small, heterogeneous, and retrospective in nature. Future improvements in diagnostic accuracy can be expected with harmonization of acquisition and postprocessing, quantitative MRI and computer‐aided diagnostic technology, and meticulous evaluation with clinical and pathological data. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:571–589.

188 citations


Journal ArticleDOI
TL;DR: A large selection of studies published until March 2017 in proton‐based quantitative MRI and MRS of bone marrow affected by osteoporosis, fractures, metabolic diseases, multiple myeloma, and bone metastases are summarized.
Abstract: Bone marrow is one of the largest organs in the human body, enclosing adipocytes, hematopoietic stem cells, which are responsible for blood cell production, and mesenchymal stem cells, which are responsible for the production of adipocytes and bone cells Magnetic resonance imaging (MRI) is the ideal imaging modality to monitor bone marrow changes in healthy and pathological states, thanks to its inherent rich soft-tissue contrast Quantitative bone marrow MRI and magnetic resonance spectroscopy (MRS) techniques have been also developed in order to quantify changes in bone marrow water–fat composition, cellularity and perfusion in different pathologies, and to assist in understanding the role of bone marrow in the pathophysiology of systemic diseases (eg osteoporosis) The present review summarizes a large selection of studies published until March 2017 in proton-based quantitative MRI and MRS of bone marrow Some basic knowledge about bone marrow anatomy and physiology is first reviewed The most important technical aspects of quantitative MR methods measuring bone marrow water–fat composition, fatty acid composition, perfusion, and diffusion are then described Finally, previous MR studies are reviewed on the application of quantitative MR techniques in both healthy aging and diseased bone marrow affected by osteoporosis, fractures, metabolic diseases, multiple myeloma, and bone metastases Level of Evidence: 3 Technical Efficacy: Stage 2 J Magn Reson Imaging 2017

161 citations


Journal ArticleDOI
TL;DR: The objective of this study was to establish a baseline level for glioma grading and establish an upper bound on the number of cells that can be assigned as positive or negative “grade A” cells.
Abstract: BACKGROUND Accurate glioma grading plays an important role in the clinical management of patients and is also the basis of molecular stratification nowadays. PURPOSE/HYPOTHESIS To verify the superiority of radiomics features extracted from multiparametric MRI to glioma grading and evaluate the grading potential of different MRI sequences or parametric maps. STUDY TYPE Retrospective; radiomics. POPULATION A total of 153 patients including 42, 33, and 78 patients with Grades II, III, and IV gliomas, respectively. FIELD STRENGTH/SEQUENCE 3.0T MRI/T1 -weighted images before and after contrast-enhanced, T2 -weighted, multi-b-value diffusion-weighted and 3D arterial spin labeling images. ASSESSMENT After multiparametric MRI preprocessing, high-throughput features were derived from patients' volumes of interests (VOIs). The support vector machine-based recursive feature elimination was adopted to find the optimal features for low-grade glioma (LGG) vs. high-grade glioma (HGG), and Grade III vs. IV glioma classification tasks. Then support vector machine (SVM) classifiers were established using the optimal features. The accuracy and area under the curve (AUC) was used to assess the grading efficiency. STATISTICAL TESTS Student's t-test or a chi-square test were applied on different clinical characteristics to confirm whether intergroup significant differences exist. RESULTS Patients' ages between LGG and HGG groups were significantly different (P < 0.01). For each patient, 420 texture and 90 histogram parameters were derived from 10 VOIs of multiparametric MRI. SVM models were established using 30 and 28 optimal features for classifying LGGs from HGGs and grades III from IV, respectively. The accuracies/AUCs were 96.8%/0.987 for classifying LGGs from HGGs, and 98.1%/0.992 for classifying grades III from IV, which were more promising than using histogram parameters or using the single sequence MRI. DATA CONCLUSION Texture features were more effective for noninvasively grading gliomas than histogram parameters. The combined application of multiparametric MRI provided a higher grading efficiency. The proposed radiomic strategy could facilitate clinical decision-making for patients with varied glioma grades. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1518-1528.

146 citations


Journal ArticleDOI
TL;DR: Deep learning is the most promising methodology for automatic computer‐aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp‐MRI) with multi-parametric MRI.
Abstract: Background Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI). Purpose To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp-MRI. Study type Retrospective. Subjects In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively. Sequence T2 -weighted, diffusion-weighted, and apparent diffusion coefficient images. Assessment A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI-RADS) scores for each region. Inspired by VGG-Net, we designed a patch-based DCNN model to distinguish between PCa and NCs based on a combination of mp-MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI-RADS score to evaluate its clinical value using decision curve analysis. Statistical test Two-sided Wilcoxon signed-rank test with statistical significance set at 0.05. Results The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876-0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI-RADS and DCNN provided additional net benefits compared with the DCNN model and the PI-RADS scheme. Data conclusion The proposed DCNN-based model with enhanced prediction yielded high performance in statistical analysis, suggesting that DCNN could be used in computer-aided diagnosis (CAD) for PCa classification. Level of evidence 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1570-1577.

137 citations


Journal ArticleDOI
TL;DR: Radiogenomics aims to correlate imaging characteristics with gene expression patterns, gene mutations, and other genome‐related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity.
Abstract: With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors. Level of evidence 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

125 citations


Journal ArticleDOI
TL;DR: The usefulness of computer‐assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebralAneurysm.
Abstract: Background The usefulness of computer-assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms. Purpose To develop a CAD system for intracranial aneurysms on unenhanced magnetic resonance angiography (MRA) images based on a deep convolutional neural network (CNN) and a maximum intensity projection (MIP) algorithm, and to demonstrate the usefulness of the system by training and evaluating it using a large dataset. Study Type Retrospective study. Subjects There were 450 cases with intracranial aneurysms. The diagnoses of brain aneurysms were made on the basis of MRA, which was performed as part of a brain screening program. Field Strength/Sequence Noncontrast-enhanced 3D time-of-flight (TOF) MRA on 3T MR scanners. Assessment In our CAD, we used a CNN classifier that predicts whether each voxel is inside or outside aneurysms by inputting MIP images generated from a volume of interest (VOI) around the voxel. The CNN was trained in advance using manually inputted labels. We evaluated our method using 450 cases with intracranial aneurysms, 300 of which were used for training, 50 for parameter tuning, and 100 for the final evaluation. Statistical Tests Free-response receiver operating characteristic (FROC) analysis. Results Our CAD system detected 94.2% (98/104) of aneurysms with 2.9 false positives per case (FPs/case). At a sensitivity of 70%, the number of FPs/case was 0.26. Data Conclusion We showed that the combination of a CNN and an MIP algorithm is useful for the detection of intracranial aneurysms. Level of Evidence: 4 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2017.

123 citations


Journal ArticleDOI
TL;DR: This review looks at the fundamental principles of an MRI RF coil from the perspective of clinicians and MR technicians and summarizes the current advances and developments in technology.
Abstract: Radiofrequency (RF) coils are an essential MRI hardware component. They directly impact the spatial and temporal resolution, sensitivity, and uniformity in MRI. Advances in RF hardware have resulted in a variety of designs optimized for specific clinical applications. RF coils are the “antennas” of the MRI system and have two functions: first, to excite the magnetization by broadcasting the RF power (Tx‐Coil) and second to receive the signal from the excited spins (Rx‐Coil). Transmit RF Coils emit magnetic field pulses ( B1+) to rotate the net magnetization away from its alignment with the main magnetic field (B0), resulting in a transverse precessing magnetization. Due to the precession around the static main magnetic field, the magnetic flux in the receive RF Coil ( B1−) changes, which generates a current I. This signal is “picked‐up” by an antenna and preamplified, usually mixed down to a lower frequency, digitized, and processed by a computer to finally reconstruct an image or a spectrum. Transmit and receive functionality can be combined in one RF Coil (Tx/Rx Coils). This review looks at the fundamental principles of an MRI RF coil from the perspective of clinicians and MR technicians and summarizes the current advances and developments in technology. Level of Evidence: 1 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;48:590–604.

118 citations


Journal ArticleDOI
TL;DR: In glioblastoma (GBM), promoter methylation of the DNA repair gene O ‐methylguanine‐DNA methyltransferase (MGMT) is associated with beneficial chemotherapy.
Abstract: Background In glioblastoma (GBM), promoter methylation of the DNA repair gene O[6]-methylguanine-DNA methyltransferase (MGMT) is associated with beneficial chemotherapy. Purpose/Hypothesis To analyze radiomics features for utilizing the full potential of medical imaging as biomarkers of MGMT promoter methylation. Study Type Retrospective. Population/Subjects In all, 98 GBM patients with known MGMT (48 methylated and 50 unmethylated tumors). Field Strength/Sequence 3.0T magnetic resonance (MR) images, containing T1-weighted image (T1WI), T2-weighted image (T2WI), and enhanced T1WI. Assessment A region of interest (ROI) of the tumor was delineated. A total of 1665 radiomics features were extracted and quantized, and were reduced using least absolute shrinkage and selection operator (LASSO) regularization. Statistical Testing After the support vector machine construction, accuracy, sensitivity, and specificity were computed for different sequences. An independent validation cohort containing 20 GBM patients was utilized to further evaluate the radiomics model performance. Results Radiomics features of T1WI reached an accuracy of 67.54%. Enhanced T1WI features reached an accuracy of 82.01%, while T2WI reached an accuracy of 69.25%. The best classification system for predicting MGMT promoter methylation status originated from the combination of 36 T1WI, T2WI, and enhanced T1WI images features, with an accuracy of 86.59%. Further validation on the independent cohort of 20 patients produced similar results, with an accuracy of 80%. Data Conclusion Our results provide further evidence that radiomics MR features could predict MGMT methylation status in preoperative GBM. Multiple imaging modalities together can yield putative noninvasive biomarkers for the identification of MGMT. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.

108 citations


Journal ArticleDOI
TL;DR: To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics), the objective was to establish an experimental procedure and demonstrate the ability to grade STSs using MRI techniques.
Abstract: Purpose To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics). Materials and Methods MRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features. Feature selection (sequential forward floating search) and classification (k-nearest neighbor classifier) were performed to distinguish intermediate- from high-grade STSs. Classification was performed using the three different sub-groups of features separately as well as all the features together. The entire dataset was divided in three subsets: the training, validation and test set, containing, respectively, 60, 30, and 10% of the data. Results Intermediate-grade lesions had a higher and less disperse ADC values compared with high-grade ones: most of FOS related to intensity are higher for the intermediate-grade STSs, while FOS related to signal variability were higher in the high grade (e.g., the feature variance is 2.6*105 ± 0.9*105 versus 3.3*105 ± 1.6*105, P = 0.3). The GLCM features related to entropy and dissimilarity were higher in the high-grade. When performing classification, the best accuracy is obtained with a maximum of three features for each subgroup, FOS features being those leading to the best classification (validation set: FOS accuracy 0.90 ± 0.11, area under the curve [AUC] 0.85 ± 0.16; test set: FOS accuracy 0.88 ± 0.25, AUC 0.87 ± 0.34). Conclusion Good accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.

Journal ArticleDOI
TL;DR: To compare the levels of gadolinium in the blood, cerebrum, cerebellum, liver, femur, kidneys, and skin after multiple exposure of rats to the macrocyclic gadolinia‐based contrast agents, GBCAs gadoterate, gadobutrol, and gadoteridol are compared.
Abstract: Purpose To compare the levels of gadolinium in the blood, cerebrum, cerebellum, liver, femur, kidneys, and skin after multiple exposure of rats to the macrocyclic gadolinium-based contrast agents (GBCAs) gadoterate, gadobutrol, and gadoteridol. Materials and Methods Fifty male Wistar Han rats were randomized to three exposure groups (n = 15 per group) and one control group (n = 5). Animals in the exposure groups received a total of 20 GBCA administrations (four administrations per week for 5 consecutive weeks) at a dose of 0.6 mmol/kg bodyweight. After a 28-day recovery period animals were sacrificed and the blood and tissues harvested for determination of gadolinium (Gd) levels. Gd determination was performed by inductively coupled plasma mass spectrometry (ICP-MS). Results After 28 days' recovery no Gd was found in the blood, liver, or skin of any animal in any group. Significantly lower levels of Gd were noted with gadoteridol compared to gadoterate and gadobutrol in the cerebellum (0.150 ± 0.022 vs. 0.292 ± 0.057 and 0.287 ± 0.056 nmol/g, respectively; P < 0.001), cerebrum (0.116 ± 0.036 vs. 0.250 ± 0.032 and 0.263 ± 0.045 nmol/g, respectively; P < 0.001), and kidneys (25 ± 13 vs. 139 ± 88 [P < 0.01] and 204 ± 109 [P < 0.001], respectively). Higher levels of Gd were noted in the femur (7.48 ± 1.37 vs. 5.69 ± 1.75 and 8.60 ± 2.04 nmol/g, respectively) with significantly less Gd determined for gadoterate than for gadobutrol (P < 0.001) and gadoteridol (P < 0.05). Conclusion Differences exist between macrocyclic agents in terms of their propensity to accumulate in tissues. The observed differences in Gd concentration point to differences in GBCA washout rates in this setting and in this experimental model, with gadoteridol being the GBCA that is most efficiently removed from both cerebral and renal tissues. Level of Evidence: 2 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017.

Journal ArticleDOI
TL;DR: Multiparametric imaging with different functional MRI parameters (mpMRI) visualizes and quantifies the functional processes of cancer development and progression at multiple levels, and provides specific information about the hallmarks of cancer.
Abstract: During their development, cancers acquire several functional capabilities, which are defined as the hallmarks of cancer. For a deeper understanding of the hallmarks of cancer, and, consequently, improved personalized patient care, diagnostic tests must be multilayered and complex to identify the relevant underlying processes of cancer development and progression. In this context, magnetic resonance imaging (MRI) has emerged as an exceptionally powerful, versatile, and precise imaging technique. MRI of the breast is an essential tool in breast imaging, with multiple indications. Dynamic contrast-enhanced MRI (CE-MRI) is the most sensitive test for breast cancer detection, with a good specificity. CE-MRI provides mainly morphological, and, to some extent, functional information about tumor perfusion and vascularity. Recently, several functional imaging techniques in MRI, such as diffusion-weighted imaging and spectroscopy, have been assessed for breast imaging and this combined application is defined as multiparametric imaging. Furthermore, the application of higher field strengths (≥3T) has demonstrated improved sensitivity and specificity of breast cancer detection. Multiparametric imaging with different functional MRI parameters (mpMRI) visualizes and quantifies the functional processes of cancer development and progression at multiple levels, and provides specific information about the hallmarks of cancer. MpMRI of the breast improves diagnostic accuracy in breast cancer, obviates unnecessary breast biopsies, and enables an improved assessment and prediction of response to neoadjuvant therapy. This review will provide a comprehensive overview of the current possibilities and emerging techniques for mpMRI of the breast. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017.

Journal ArticleDOI
TL;DR: In this article, a set of radiomic features derived from pretreatment biparametric MRI (BPMRI) were used to predict biochemical recurrence (BCR) of prostate cancer.
Abstract: BACKGROUND Radiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa. PURPOSE To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR. STUDY TYPE Retrospective. SUBJECTS In all, 120 PCa patients from two institutions, I1 and I2 , partitioned into training set D1 (N = 70) from I1 and independent validation set D2 (N = 50) from I2 . All patients were followed for ≥3 years. SEQUENCE 3T, T2 -weighted (T2 WI) and apparent diffusion coefficient (ADC) maps derived from diffusion-weighted sequences. ASSESSMENT PCa regions of interest (ROIs) on T2 WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T2 WI and ADC maps) were extracted from the ROIs. A machine-learning classifier (CBCR ) was trained with the best discriminating set of radiomic features to predict BCR (pBCR ). STATISTICAL TESTS Wilcoxon rank-sum tests with P < 0.05 were considered statistically significant. Differences in BCR-free survival at 3 years using pBCR was assessed using the Kaplan-Meier method and compared with Gleason Score (GS), PSA, and PIRADS-v2. RESULTS Distribution statistics of co-occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T2 WI and ADC were associated with BCR (P < 0.05) on D1 . CBCR predictions resulted in a mean AUC = 0.84 on D1 and AUC = 0.73 on D2 . A significant difference in BCR-free survival between the predicted classes (BCR + and BCR-) was observed (P = 0.02) on D2 compared to those obtained from GS (P = 0.8), PSA (P = 0.93) and PIRADS-v2 (P = 0.23). DATA CONCLUSION Radiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626-1636.

Journal ArticleDOI
TL;DR: Risks originating from the main magnetic field, the excitation RF electromagnetic field, and switching of the imaging gradients will be presented in turn to help develop intuition about electromagnetism that might prove of practical value when working around MR scanners.
Abstract: The main risks associated with magnetic resonance imaging (MRI) have been extensively reported and studied; for example, everyday objects may turn into projectiles, energy deposition can cause burns, varying fields can induce nerve stimulation, and loud noises can lead to auditory loss. The present review article is geared toward providing intuition about the physical mechanisms that give rise to these risks. On the one hand, excellent literature already exists on the practical aspect of risk management, with clinical workflow and recommendations. On the other hand, excellent technical articles also exist that explain these risks from basic principles of electromagnetism. We felt that an underserved niche might be found between the two, ie, somewhere between basic science and practical advice, to help develop intuition about electromagnetism that might prove of practical value when working around MR scanners. Following a wide-ranging introduction, risks originating from the main magnetic field, the excitation RF electromagnetic field, and switching of the imaging gradients will be presented in turn. Level of Evidence: 5 Technical Efficacy: 1 J. Magn. Reson. Imaging 2018;47:28–43.

Journal ArticleDOI
TL;DR: To develop and test a deep learning approach named Convolutional Neural Network for automated screening of T2‐weighted (T2WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists.
Abstract: Purpose To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T2-weighted (T2WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists Materials and Methods We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 15T and 3T at our institution between November 2014 and May 2016 for CNN training and validation The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer 351 T2WI were anonymized for training Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology Another independently collected 171 cases were sequestered for a blind test These 171 T2WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic These 171 T2WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists Results There was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73% The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2 The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2) Conclusion We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T2WI of the liver Level of Evidence: 2 Technical Efficacy: Stage 2 J Magn Reson Imaging 2017

Journal ArticleDOI
TL;DR: Noninvasive detection of isocitrate dehydrogenase (IDH) and TP53 mutations are meaningful for molecular stratification of lower‐grade gliomas (LrGG).
Abstract: BACKGROUND Noninvasive detection of isocitrate dehydrogenase (IDH) and TP53 mutations are meaningful for molecular stratification of lower-grade gliomas (LrGG). PURPOSE To explore potential MRI features reflecting IDH and TP53 mutations of LrGG, and propose a radiomics strategy for detecting them. STUDY TYPE Retrospective, radiomics. POPULATION/SUBJECTS A total of 103 LrGG patients were separated into development (n = 73) and validation (n = 30) cohorts. FIELD STRENGTH/SEQUENCE T1 -weighted (before and after contrast-enhanced), T2 -weighted, and fluid-attenuation inversion recovery images from 1.5T (n = 37) or 3T (n = 66) scanners. ASSESSMENT After data preprocessing, high-throughput features were derived from patients' volumes of interests of different sequences. The support vector machine-based recursive feature elimination (SVM-RFE) was adopted to find the optimal features for IDH and TP53 mutation detection. SVM models were trained and tested on development and validation cohort. The commonly used metric was used for assessing the efficiency. STATISTICAL TESTS One-way analysis of variance (ANOVA), chi-square, or Fisher's exact test were applied on clinical characteristics to confirm whether significant differences exist between three molecular subtypes decided by IDH and TP53 status. Intraclass correlation coefficients were calculated to assess the robustness of the radiomics features. RESULTS The constituent ratio of histopathologic subtypes was significantly different among three molecular subtypes (P = 0.017). SVM models for detecting IDH and TP53 mutation were established using 12 and 22 optimal features selected by SVM-RFE. The accuracies and area under the curves for IDH and TP53 mutations on the development cohort were 84.9%, 0.830, and 92.0%, 0.949, while on the validation cohort were 80.0%, 0.792, and 85.0%, 0.869, respectively. Furthermore, the stratified accuracies of three subtypes were 72.8%, 71.9%, and 70%, respectively. DATA CONCLUSION Using a radiomics approach integrating SVM model and multimodal MRI features, molecular subtype stratification of LGG patients was implemented through detecting IDH and TP53 mutations. The results suggested that the proposed approach has promising detecting efficiency and T2 -weighted image features are more important than features from other images. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:916-926.

Journal ArticleDOI
TL;DR: Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer.
Abstract: Background Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer. Purpose To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high-risk histological subtype) and to outcome in endometrial cancer patients. Study type Prospective cohort study. Population/subjects In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017. Field strength/sequences Preoperative pelvic MRI including contrast-enhanced T1 -weighted (T1 c), T2 -weighted, and diffusion-weighted imaging at 1.5T. Assessment Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross-sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration-histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated. Statistical tests Associations between texture parameters and histological features were assessed by uni- and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis. Results High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio [OR] 3.2, P lt 0.001), and high MPP in T1 c images independently predicted high-risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T1 c images predicted reduced recurrence- and progression-free survival (hazard ratio [HR] 1.5, P lt 0.001) after adjusting for MRI-measured tumor volume and histological risk at biopsy. Data conclusion MRI-derived tumor texture parameters independently predicted deep myometrial invasion, high-risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer. Level of evidence 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1637-1647.

Journal ArticleDOI
TL;DR: The emerging role of MRI in radiation dose plans and delivery is highlighted, but more so for MR‐only treatment planning and delivery.
Abstract: Advances in multimodality imaging, providing accurate information of the irradiated target volume and the adjacent critical structures or organs at risk (OAR), has made significant improvements in delivery of the external beam radiation dose. Radiation therapy conventionally has used computed tomography (CT) imaging for treatment planning and dose delivery. However, magnetic resonance imaging (MRI) provides unique advantages: added contrast information that can improve segmentation of the areas of interest, motion information that can help to better target and deliver radiation therapy, and posttreatment outcome analysis to better understand the biologic effect of radiation. To take advantage of these and other potential advantages of MRI in radiation therapy, radiologists and MRI physicists will need to understand the current radiation therapy workflow and speak the same language as our radiation therapy colleagues. This review article highlights the emerging role of MRI in radiation dose planning and delivery, but more so for MR-only treatment planning and delivery. Some of the areas of interest and challenges in implementing MRI in radiation therapy workflow are also briefly discussed. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1468-1478.

Journal ArticleDOI
TL;DR: There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS).
Abstract: BACKGROUND Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS). PURPOSE To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients. STUDY TYPE Retrospective. SUBJECTS MODEL MRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy. FIELD STRENGTH/SEQUENCE 3T, T2 -weighted (T2 w) and diffusion-weighted (DW) MRI. ASSESSMENT A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T2 w-MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI-negative-biopsy-negative (Group 1, N = 15), MRI-positive-biopsy-positive (Group 2, N = 16), MRI-negative-biopsy-positive (Group 3, N = 10), and MRI-positive-biopsy-negative (Group 4, N = 15). In all, 308 radiomic features (First-order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T2 w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum-redundancy-maximum-relevance and used to construct three machine-learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease. STATISTICAL TESTS Wilcoxon rank-sum tests with P < 0.05 considered statistically significant. RESULTS Seven T2 w-based (First-order Statistics, Haralick, Laws, and Gabor) and three ADC-based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences (P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone. DATA CONCLUSION Radiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI-TRUS prostate biopsies. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Journal ArticleDOI
TL;DR: Quantitative T2 measurements are sensitive to intra‐ and extracellular water accumulation and myelin loss and promise to be a good biomarker of disease, however, they require long acquisition times.
Abstract: BACKGROUND Quantitative T2 measurements are sensitive to intra- and extracellular water accumulation and myelin loss. Therefore, quantitative T2 promises to be a good biomarker of disease. However, T2 measurements require long acquisition times. PURPOSE To accelerate T2 quantification and subsequent generation of synthetic T2 -weighted (T2 -w) image contrast for clinical research and routine. To that end, a recently developed model-based approach for rapid T2 and M0 quantification (MARTINI) based on undersampling k-space, was extended by parallel imaging (GRAPPA) to enable high-resolution T2 mapping with access to T2 -w images in less than 2 minutes acquisition time for the entire brain. STUDY TYPE Prospective cross-sectional study. SUBJECTS/PHANTOM Fourteen healthy subjects and a multipurpose phantom. FIELD STRENGTH/SEQUENCE Carr-Purcell-Meiboom-Gill sequence at a 3T scanner. ASSESSMENT The accuracy and reproducibility of the accelerated T2 quantification was assessed. Validations comprised MRI studies on a phantom as well as the brain, knee, prostate, and liver from healthy volunteers. Synthetic T2 -w images were generated from computed T2 and M0 maps and compared to conventional fast spin-echo (SE) images. STATISTICAL TESTS Root mean square distance (RMSD) to the reference method and region of interest analysis. RESULTS The combination of MARTINI and GRAPPA (GRAPPATINI) lead to a 10-fold accelerated T2 mapping protocol with 1:44 minutes acquisition time and full brain coverage. The RMSD of GRAPPATINI increases less (4.3%) than a 10-fold MARTINI reconstruction (37.6%) in comparison to the reference. Reproducibility tests showed low standard deviation (SD) of T2 values in regions of interest between scan and rescan (<0.4 msec) and across subjects (<4 msec). DATA CONCLUSION GRAPPATINI provides highly reproducible and fast whole-brain T2 maps and arbitrary synthetic T2 -w images in clinically compatible acquisition times of less than 2 minutes. These abilities are expected to support more widespread clinical applications of quantitative T2 mapping. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:359-368.

Journal ArticleDOI
TL;DR: Robust approaches to quantify tumor heterogeneity are needed to provide early decision support for precise individualized therapy in patients with high tumor heterogeneity.
Abstract: Background Robust approaches to quantify tumor heterogeneity are needed to provide early decision support for precise individualized therapy. Purpose To conduct a technical exploration of longitudinal changes in tumor heterogeneity patterns on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI) and FDG positron emission tomography / computed tomography (PET/CT), and their association to radiation therapy (RT) response in cervical cancer. Study Type Prospective observational study with longitudinal MRI and PET/CT pre-RT, early-RT (2 weeks), and mid-RT (5 weeks). Population Twenty-one FIGO IB2-IVA cervical cancer patients receiving definitive external beam RT and brachytherapy. Field Strength/Sequence 1.5T, precontrast axial T1-weighted, axial and sagittal T2-weighted, sagittal DWI (multi-b values), sagittal DCE MRI (<10 sec temporal resolution), postcontrast axial T1-weighted. Assessment Response assessment 1 month after completion of treatment by a board-certified radiation oncologist from manually delineated tumor volume changes. Statistical Tests Intensity histogram (IH) quantiles (DCE SI10% and DWI ADC10%, FDG-PET SUVmax) and distribution moments (mean, variance, skewness, kurtosis) were extracted. Differences in IH features between timepoints and modalities were evaluated by Skillings–Mack tests with Holm's correction. Area under receiver-operating characteristic curve (AUC) and Mann–Whitney testing was performed to discriminate treatment response using IH features. Results Tumor IH means and quantiles varied significantly during RT (SUVmean: ↓28–47%, SUVmax: ↓30–59%, SImean: ↑8–30%, SI10%: ↑8–19%, ADCmean: ↑16%, P < 0.02 for each). Among IH heterogeneity features, FDG-PET SUVCoV (↓16–30%, P = 0.011) and DW-MRI ADCskewness decreased (P = 0.001). FDG-PET SUVCoV was higher than DCE-MRI SICoV and DW-MRI ADCCoV at baseline (P < 0.001) and 2 weeks (P = 0.010). FDG-PET SUVkurtosis was lower than DCE-MRI SIkurtosis and DW-MRI ADCkurtosis at baseline (P = 0.001). Some IH features appeared to associate with favorable tumor response, including large early RT changes in DW-MRI ADCskewness (AUC = 0.86). Data Conclusion Preliminary findings show tumor heterogeneity was variable between patients, modalities, and timepoints. Radiomic assessment of changing tumor heterogeneity has the potential to personalize treatment and power outcome prediction. Level of Evidence: 2 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2017.

Journal ArticleDOI
TL;DR: Technical issues related to DTI, particularly when trying to apply DTI in the clinical setting, are provided, and potential solutions are offered.
Abstract: Diffusion tensor imaging (DTI) is a noninvasive magnetic resonance imaging (MRI) technique that measures the extent of restricted water diffusion and anisotropy in biological tissue. Although DTI has been widely applied in the brain, more recently researchers have used it to characterize nerve pathology in the setting of entrapment neuropathy, traumatic injury, and tumor. DTI artifacts are exacerbated when imaging off isocenter in the body. Anecdotally, the most significant artifacts in peripheral nerve DTI include magnetic field inhomogeneity, motion, incomplete fat suppression, aliasing, and distortion. High spatial resolution is also required to reliably evaluate smaller peripheral nerves. This article provides an overview of such technical issues, particularly when trying to apply DTI in the clinical setting, and offers potential solutions. LEVEL OF EVIDENCE 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1171-1189.

Journal ArticleDOI
TL;DR: Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols and fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment.
Abstract: BACKGROUND Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment. PURPOSE To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans. STUDY TYPE Cross-sectional survey; diagnostic accuracy. SUBJECTS In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database. FIELD STRENGTH/SEQUENCE 1.5T, steady-state free precession. ASSESSMENT Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume. STATISTICAL TESTS Paired t-tests compared to previous work. RESULTS Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and -19.8 ± 18.8 mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase. DATA CONCLUSION A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Journal ArticleDOI
TL;DR: To support translational lung MRI research with hyperpolarized 129Xe gas, comprehensive evaluation of derived quantitative lung function measures against established measures from 3He MRI is required.
Abstract: BACKGROUND: To support translational lung MRI research with hyperpolarized129Xe gas, comprehensive evaluation of derived quantitative lung function measures against established measures from3He MRI is required. Few comparative studies have been performed to date, only at 3T, and multisession repeatability of129Xe functional metrics have not been reported. PURPOSE/HYPOTHESIS: To compare hyperpolarized129Xe and3He MRI-derived quantitative metrics of lung ventilation and microstructure, and their repeatability, at 1.5T. STUDY TYPE: Retrospective. POPULATION: Fourteen healthy nonsmokers (HN), five exsmokers (ES), five patients with chronic obstructive pulmonary disease (COPD), and 16 patients with nonsmall-cell lung cancer (NSCLC). FIELD STRENGTH/SEQUENCE: 1.5T. NSCLC, COPD patients and selected HN subjects underwent 3D balanced steady-state free-precession lung ventilation MRI using both3He and129Xe. Selected HN, all ES, and COPD patients underwent 2D multislice spoiled gradient-echo diffusion-weighted lung MRI using both hyperpolarized gas nuclei. ASSESSMENT: Ventilated volume percentages (VV%) and mean apparent diffusion coefficients (ADC) were derived from imaging. COPD patients performed the whole MR protocol in four separate scan sessions to assess repeatability. Same-day pulmonary function tests were performed. STATISTICAL TESTS: Intermetric correlations: Spearman's coefficient. Intergroup/internuclei differences: analysis of variance / Wilcoxon's signed rank. Repeatability: coefficient of variation (CV), intraclass correlation (ICC) coefficient. RESULTS: A significant positive correlation between3He and129Xe VV% was observed (r = 0.860, P < 0.001). VV% was larger for3He than129Xe (P = 0.001); average bias, 8.79%. A strong correlation between mean3He and129Xe ADC was obtained (r = 0.922, P < 0.001). MR parameters exhibited good correlations with pulmonary function tests. In COPD patients, mean CV of3He and129Xe VV% was 4.08% and 13.01%, respectively, with ICC coefficients of 0.541 (P = 0.061) and 0.458 (P = 0.095). Mean3He and129Xe ADC values were highly repeatable (mean CV: 2.98%, 2.77%, respectively; ICC: 0.995, P < 0.001; 0.936, P < 0.001). DATA CONCLUSION:129Xe lung MRI provides near-equivalent information to3He for quantitative lung ventilation and microstructural MRI at 1.5T. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage 2

Journal ArticleDOI
TL;DR: Imaging studies, including computed tomography, magnetic resonance imaging (MRI), and positron emission tomography (PET), have an essential role in the detection and localization of colorectal liver metastasis (CRLM).
Abstract: Background Imaging studies, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), have an essential role in the detection and localization of colorectal liver metastasis (CRLM). Purpose To systematically determine the diagnostic accuracy of multidetector row CT (MDCT), gadoxetate disodium-enhanced MRI, and PET/CT for diagnosing CRLM and the sources of heterogeneity between the reported results. Study Type Systematic review and meta-analysis. Subjects In all, 2151 lesions in CT studies, 2301 lesions in MRI studies, 1846 lesions in PET/CT studies, Field Strength 1.5T and 3.0T. Assessment We identified research studies that investigated MDCT, gadoxetate disodium-enhanced MRI, and PET/CT to diagnose CRLM by performing a systematic search of PubMed MEDLINE and EMBASE. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Statistical Tests According to the types of imaging tests, study heterogeneity and the threshold effect were analyzed and the meta-analytic summary of sensitivity and specificity were estimated. Meta-regression analysis was performed to further investigate study heterogeneity. Results Of the 860 articles screened, we found 36 studies from 24 articles reporting a diagnosis of CRLM (11 CT studies, 12 MRI studies, and 13 PET/CT studies). The meta-analytic summary sensitivity for CT, MRI, and PET/CT were 82.1% (95% confidence interval [CI], 74.0–88.1%), 93.1% (95% CI, 88.4–96.0%), and 74.1% (95% CI, 62.1–83.3%), respectively. The meta-analytic summary specificity for CT, MRI, and PET/CT were 73.5% (95% CI, 53.7–86.9%), 87.3% (95% CI, 77.5–93.2%), and 93.9% (95% CI, 83.9–97.8%), respectively. There was no threshold effect in any of the imaging tests. Neoadjuvant chemotherapy significantly decreased the sensitivity of CT and MRI (P < 0.01), although it did not significantly affect the sensitivity of PET/CT. The study design, type of reference standard, and study quality also affected the diagnostic performances of imaging studies. Data Conclusion Despite the heterogeneous accuracy between studies, gadoxetate disodium-enhanced MRI showed the highest sensitivity, and gadoxetate disodium-enhanced MRI and PET/CT had similar specificities for diagnosing CRLM. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.

Journal ArticleDOI
TL;DR: Cognitive decline in normal elderly individuals, with the goal of identifying subjects who are most susceptible to imminent cognitive impairment, is studied.
Abstract: Background With the disappointing outcomes of clinical trials on patients with Alzheimer's disease or mild cognitive impairment (MCI), there is increasing attention to understanding cognitive decline in normal elderly individuals, with the goal of identifying subjects who are most susceptible to imminent cognitive impairment. Purpose/hypothesis To evaluate the potential of cerebral blood flow (CBF) as a biomarker by investigating the relationship between CBF at baseline and cognition at follow-up. Study type Prospective longitudinal study with a 4-year time interval. Population 309 healthy subjects aged 20-89 years old. Field strength/sequence 3T pseudo-continuous-arterial-spin-labeling MRI. Assessment CBF at baseline and cognitive assessment at both baseline and follow-up. Statistical tests Linear regression analyses with age, systolic blood pressure, physical activity, and baseline cognition as covariates. Results Linear regression analyses revealed that whole-brain CBF at baseline was predictive of general fluid cognition at follow-up. This effect was observed in the older group (age ≥54 years, β = 0.221, P = 0.004), but not in younger or entire sample (β = 0.018, P = 0.867 and β = 0.089, P = 0.098, respectively). Among major brain lobes, frontal CBF had the highest sensitivity in predicting future cognition, with a significant effect observed for fluid cognition (β = 0.244 P = 0.001), episodic memory (β = 0.294, P = 0.001), and reasoning (β = 0.186, P = 0.027). These associations remained significant after accounting for baseline cognition. Voxelwise analysis revealed that medial frontal cortex and anterior cingulate cortex, part of the default mode network (DMN), are among the most important regions in predicting fluid cognition. Data conclusion In a healthy aging cohort, CBF can predict general cognitive ability as well as specific domains of cognitive function. Level of evidence 1 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2018;48:449-458.

Journal ArticleDOI
TL;DR: Volumetric 4D flow MRI has potential for quantitatively grading the severity of inlet valve regurgitation in adult patients.
Abstract: Author(s): Feneis, Jennifer F; Kyubwa, Espoir; Atianzar, Kimberly; Cheng, Joseph Y; Alley, Marcus T; Vasanawala, Shreyas S; Demaria, Anthony N; Hsiao, Albert | Abstract: BackgroundIn patients with mitral or tricuspid valve regurgitation, evaluation of regurgitant severity is essential for determining the need for surgery. While transthoracic echocardiography is widely accessible, it has limited reproducibility for grading inlet valve regurgitation. Multiplanar cardiac MRI is the quantitative standard but requires specialized local expertise, and is thus not widely available. Volumetric 4D flow MRI has potential for quantitatively grading the severity of inlet valve regurgitation in adult patients.PurposeTo evaluate the accuracy and reproducibility of volumetric 4D flow MRI for quantification of inlet valvular regurgitation compared to conventional multiplanar MRI, which may simplify and improve accessibility of cardiac MRI.Study typeThis retrospective, HIPAA-compliant imaging-based comparison study was conducted at a single institution.SubjectsTwenty-one patients who underwent concurrent multiplanar and 4D flow cardiac MRI between April 2015 and January 2017.Field strength/sequences3T; steady-state free-precession (SSFP), 2D phase contrast (2D-PC), and postcontrast 4D flow.AssessmentWe evaluated the intertechnique (4D flow vs. 2D-PC), intermethod (direct vs. indirect measurement), interobserver and intraobserver reproducibility of measurements of regurgitant flow volume (RFV), fraction (RF), and volume (RVol).Statistical testsStatistical analysis included Pearson correlation, Bland-Altman statistics, and intraclass correlation coefficients.ResultsThere was high concordance between 4D flow and multiplanar MRI, whether using direct or indirect methods of quantifying regurgitation (r = 0.813-0.985). Direct interrogation of the regurgitant jet with 4D flow showed high intraobserver consistency (r = 0.976-0.999) and interobserver consistency (r = 0.861-0.992), and correlated well with traditional indirect measurements obtained as the difference between stroke volume and forward outlet valve flow.Data conclusion4D flow MRI provides highly reproducible measurements of mitral and tricuspid regurgitant volume, and may be used in place of conventional multiplanar MRI.Level of evidence4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1147-1158.

Journal ArticleDOI
TL;DR: More efficient prognostic biomarkers are needed for improved risk stratification of LARC patients, personalized treatment, and prognostication in locally advanced rectal cancer patients.
Abstract: BACKGROUND Locally advanced rectal cancer (LARC) patient stratification by clinicoradiologic factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification of LARC patients, personalized treatment, and prognostication. PURPOSE/HYPOTHESIS To compare the ability of a radiomic signature to predict disease-free survival (DFS) with that of a clinicoradiologic risk model in individual patients with LARC. STUDY TYPE Retrospective study. POPULATION In all, 108 consecutive patients (allocated to a training and validation set with a 1:1 ratio) with LARC treated with neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision (TME). FIELD STRENGTH/SEQUENCE Axial 3D LAVA multienhanced MR sequence at 3T. ASSESSMENT ITK-SNAP software was used for manual segmentation of 3D pre-nCRT MR images. All manual tumor segmentations were performed by a gastrointestinal tract radiologist, and validated by a senior radiologist. The clinicoradiologic risk factors with potential prognostic outcomes were identified in univariate analysis based on the Cox regression model for the whole set. The results showed that ypT, ypN, EMVI, and MRF were potential clinicoradiologic risk factors. Interestingly, only ypN and MRF were identified as independent predictors in multivariate analysis based on the Cox regression model. STATISTICAL TESTS A radiomic signature based on 485 3D features was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association of the radiomic signature with DFS was investigated by Kaplan-Meier survival curves. Survival curves were compared by the log-rank test. Three models were built and assessed for their predictive values, using the Harrell concordance index and integrated time-dependent area under the curve. RESULTS The novel radiomic signature stratified patients into low- and high-risk groups for DFS in the training set (hazard ratio [HR] = 6.83; P < 0.001), and was successfully validated in the validation set (HR = 2.92; P < 0.001). The model combining the radiomic signature and clinicoradiologic findings had the best performance (C index = 0.788, 95% confidence interval [CI] 0.72-0.86; integrated time-dependent area under the curve of 0.837 at 3 years). DATA CONCLUSION The novel radiomic signature could be used to predict DFS in patients with LARC. Furthermore, combining this radiomic signature with clinicoradiologic features significantly improved the ability to estimate DFS (P = 0.001, 0.005 in training set and in validation set, respectively), and may help guide individualized treatment in such patients. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.

Journal ArticleDOI
TL;DR: Diffusion‐weighted MRI (DWI) is currently one of the fastest developing MRI‐based techniques in oncology and can potentially be improved by machine learning.
Abstract: Background Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. Purpose To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Study Type Prospective. SUBJECTS Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Field Strength/Sequence Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Assessment Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Statistical Tests Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann–Whitney tests were performed for univariate comparisons. Results For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Data Conclusion Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.