Dynamic Contrast Material-enhanced Magnetic Resonance Imaging Research Articles

Computerized Segmentation Method for Nonmasses on Breast DCE-MRI Images Using ResUNet++ with Slice Sequence Learning and Cross-Phase Convolution

The purpose of this study was to develop a computerized segmentation method for nonmasses using ResUNet++ with a slice sequence learning and cross-phase convolution to analyze temporal information in breast dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) images. The dataset consisted of a series of DCE-MRI examinations from 54 patients, each containing three-phase images, which included one image that was acquired before contrast injection and two images that were acquired after contrast injection. In the proposed method, the region of interest (ROI) slice images are first extracted from each phase image. The slice images at the same position in each ROI are stacked to generate a three-dimensional (3D) tensor. A cross-phase convolution generates feature maps with the 3D tensor to incorporate the temporal information. Subsequently, the feature maps are used as the input layers for ResUNet++. New feature maps are extracted from the input data using the ResUNet++ encoders, following which the nonmass regions are segmented by a decoder. A convolutional long short-term memory layer is introduced into the decoder to analyze a sequence of slice images. When using the proposed method, the average detection accuracy of nonmasses, number of false positives, Jaccard coefficient, Dice similarity coefficient, positive predictive value, and sensitivity were 90.5%, 1.91, 0.563, 0.712, 0.714, and 0.727, respectively, larger than those obtained using 3D U-Net, V-Net, and nnFormer. The proposed method achieves high detection and shape accuracies and will be useful in differential diagnoses of nonmasses.

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization.

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.

Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.

Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient treatment. Purpose To evaluate whether the diagnostic performance of radiologists in the differentiation of cancer from noncancer at dynamic contrast material-enhanced (DCE) breast MRI is improved when using an AI system compared with conventionally available software. Materials and Methods In a retrospective clinical reader study, images from breast DCE MRI examinations were interpreted by 19 breast imaging radiologists from eight academic and 11 private practices. Readers interpreted each examination twice. In the "first read," they were provided with conventionally available computer-aided evaluation software, including kinetic maps. In the "second read," they were also provided with AI analytics through computer-aided diagnosis software. Reader diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distinguishing between malignant and benign lesions. The primary study end point was the difference in AUC between the first-read and the second-read conditions. Results One hundred eleven women (mean age, 52 years ± 13 [standard deviation]) were evaluated with a total of 111 breast DCE MRI examinations (54 malignant and 57 nonmalignant lesions). The average AUC of all readers improved from 0.71 to 0.76 (P = .04) when using the AI system. The average sensitivity improved when Breast Imaging Reporting and Data System (BI-RADS) category 3 was used as the cut point (from 90% to 94%; 95% confidence interval [CI] for the change: 0.8%, 7.4%) but not when using BI-RADS category 4a (from 80% to 85%; 95% CI: -0.9%, 11%). The average specificity showed no difference when using either BI-RADS category 4a or category 3 as the cut point (52% and 52% [95% CI: -7.3%, 6.0%], and from 29% to 28% [95% CI: -6.4%, 4.3%], respectively). Conclusion Use of an artificial intelligence system improves radiologists' performance in the task of differentiating benign and malignant MRI breast lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Krupinski in this issue.

Assessment of Hepatocellular Carcinoma Response to 90Y Radioembolization Using Dynamic Contrast Material-enhanced MRI and Intravoxel Incoherent Motion Diffusion-weighted Imaging.

To quantify diffusion and perfusion changes in hepatocellular carcinoma (HCC) induced by yttrium 90 (90Y) radioembolization and to assess the value of dynamic contrast material-enhanced (DCE) MRI and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for predicting HCC response. Institutional review board approval was obtained for this prospective study (clinical trial registry NCT01871545). Twenty-four participants with HCC (mean age, 69 years ± 9 [standard deviation], 18 men) underwent multiparametric MRI, including IVIM DWI and gadoxetic acid DCE MRI before (n = 24) and 6 weeks (n = 21) after radioembolization. IVIM DWI and DCE MRI histogram parameters were quantified in HCCs and liver parenchyma. HCC response was assessed by using modified Response Evaluation Criteria in Solid Tumors at 6 weeks and 6-12 months after radioembolization. Logistic regression analysis was used to evaluate the diagnostic performance of baseline MRI and clinical parameters for prediction of response. Twenty-five HCCs were analyzed (mean size, 3.6 cm ± 1.9). Radioembolization resulted in significantly decreased perfusion (DCE MRI arterial flow, P = .002; IVIM pseudodiffusion coefficient [D*], P = .014). Multivariate logistic regression selected combined serum α-fetoprotein and portal flow (F p ) skewness (area under the curve [AUC] = 0.924) and combined D* standard deviation and F p kurtosis (AUC = 0.916) for prediction of objective and complete response at 6 weeks, respectively. Standard deviation of DCE MRI parameter arterial fraction was selected as the optimal predictor for complete response at 6-12 months (AUC = 0.857). Diffusion and perfusion MRI can be used to evaluate the response of HCC to radioembolization. Pretreatment DCE MRI histogram parameters may be useful for radioembolization treatment stratification. Supplemental material is available for this article. © RSNA, 2020.

Diagnostic Performance of MRI, Molecular Breast Imaging, and Contrast-enhanced Mammography in Women with Newly Diagnosed Breast Cancer.

Background Staging newly diagnosed breast cancer by using dynamic contrast material-enhanced MRI is limited by access, high cost, and false-positive findings. The utility of contrast-enhanced mammography (CEM) and 99mTc sestamibi-based molecular breast imaging (MBI) in this setting is largely unknown. Purpose To compare extent-of-disease assessments by using MRI, CEM, and MBI versus pathology in women with breast cancer. Materials and Methods In this HIPAA-compliant prospective study, women with biopsy-proven breast cancer underwent MRI, CEM, and MBI between October 2014 and April 2018. Eight radiologists independently interpreted each examination result prospectively and were blinded to interpretations of findings with the other modalities. Visibility of index malignancies, lesion size, and additional suspicious lesions (malignant or benign) were compared during pathology review. Accuracy of index lesion sizing and detection of additional lesions in women without neoadjuvant chemotherapy were compared. Results A total of 102 women were enrolled and 99 completed the study protocol (mean age, 51 years ± 11 [standard deviation]; range, 32-77 years). Lumpectomy or mastectomy was performed in 71 women (79 index malignancies) without neoadjuvant chemotherapy and in 28 women (31 index malignancies) with neoadjuvant chemotherapy. Of the 110 index malignancies, MRI, CEM, and MBI depicted 102 (93%; 95% confidence interval [CI]: 86%, 97%), 100 (91%; 95% CI: 84%, 96%), and 101 (92%; 95% CI: 85%, 96%) malignancies, respectively. In patients without neoadjuvant chemotherapy, pathologic size of index malignancies was overestimated with all modalities (P = .02). MRI led to overestimation of 24% (17 of 72) of malignancies by more than 1.5 cm compared with 11% (eight of 70) with CEM and 15% (11 of 72) with MBI. MRI depicted more (P = .007) nonindex lesions, with sensitivity similar to that of CEM or MBI, resulting in lower positive predictive value of additional biopsies (13 of 46 [28%; 95% CI: 17%, 44%] for MRI; 14 of 27 [52%; 95% CI: 32%, 71%] for CEM; and 11 of 25 [44%; 95% CI: 24%, 65%] for MBI (overall P = .01). Conclusion Contrast-enhanced mammography, molecular breast imaging, and MRI showed similar detection of all malignancies. MRI depicted more nonindex suspicious benign lesions than did contrast-enhanced mammography or molecular breast imaging, leading to lower positive predictive value of additional biopsies. All three modalities led to overestimation of index tumor size, particularly MRI. © RSNA, 2019 Online supplemental material is available for this article.

Diffusion-weighted MRI for Unenhanced Breast Cancer Screening.

Diffusion-weighted (DW) MRI is a rapid technique that measures the mobility of water molecules within tissue, reflecting the cellular microenvironment. At DW MRI, breast cancers typically exhibit reduced diffusivity and appear hyperintense to surrounding tissues. On the basis of this characteristic, DW MRI may offer an unenhanced method to detect breast cancer without the costs and safety concerns associated with dynamic contrast material-enhanced MRI, the current reference standard in the setting of high-risk screening. This application of DW MRI has not been widely explored but is particularly timely given the growing health concerns related to the long-term use of gadolinium-based contrast material. Moreover, increasing breast density notification legislation across the United States is raising awareness of the limitations of mammography in women with dense breasts, emphasizing the need for additional cost-effective supplemental screening examinations. Preliminary studies suggest unenhanced MRI with DW MRI may provide higher sensitivity than screening mammography for the detection of breast malignancies. Larger prospective multicenter trials are needed to validate single-center findings and assess the performance of DW MRI for generalized breast cancer screening. Standardization of DW MRI acquisition and interpretation is essential to ensure reliable sensitivity and specificity, and an optimal approach for screening using readily available techniques is proposed here.

Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi-ethnic study of atherosclerosis COPD study.

Chronic obstructive pulmonary disease (COPD) is associated with high morbidity and mortality. Identification of imaging biomarkers for phenotyping is necessary for future treatment and therapy monitoring. However, translation of visual analytic pipelines into clinics or their use in large-scale studies is significantly slowed by time-consuming postprocessing steps. To implement an automated tool chain for regional quantification of pulmonary microvascular blood flow in order to reduce analysis time and user variability. Prospective. In all, 90 MRI scans of 63 patients, of which 31 had a COPD with a mean Global Initiative for Chronic Obstructive Lung Disease status of 1.9 ± 0.64 (μ ± σ). 1.5T dynamic gadolinium-enhanced MRI measurement using 4D dynamic contrast material-enhanced (DCE) time-resolved angiography acquired in a single breath-hold in inspiration. [Correction added on August 20, 2019, after first online publication: The field strength in the preceding sentence was corrected.] ASSESSMENT: We built a 3D convolutional neural network for semantic segmentation using 29 manually segmented perfusion maps. All five lobes of the lung are denoted, including the middle lobe. Evaluation was performed on 61 independent cases from two sites of the Multi-Ethnic Study of Arteriosclerosis (MESA)-COPD study. We publish our implementation of a model-free deconvolution filter according to Sourbron et al for 4D DCE MRI scans as open source. Cross-validation 29/61 (# training / # testing), intraclass correlation coefficient (ICC), Spearman ρ, Pearson r, Sørensen-Dice coefficient, and overlap. Segmentations and derived clinical parameters were processed in ~90 seconds per case on a Xeon E5-2637v4 workstation with Tesla P40 GPUs. Clinical parameters and predicted segmentations exhibit high concordance with the ground truth regarding median perfusion for all lobes with an ICC of 0.99 and a Sørensen-Dice coefficient of 93.4 ± 2.8 (μ ± σ). We present a robust end-to-end pipeline that allows for the extraction of perfusion-based biomarkers for all lung lobes in 4D DCE MRI scans by combining model-free deconvolution with deep learning. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:571-579.

Altered Vascular Permeability in Migraine-associated Brain Regions: Evaluation with Dynamic Contrast-enhanced MRI.

Background Recent studies showed the possible association between inflammation-induced blood-brain barrier (BBB) structural changes followed by greater permeability of the BBB and chronic pain. Thus, measurement of BBB breakdown would be a valuable aid in the diagnosis in migraine. Dynamic contrast material-enhanced (DCE) MRI can determine perfusion and permeability properties related to the BBB. Purpose To evaluate the relationship between permeability of the BBB in migraine-associated brain regions by using DCE MRI. Materials and Methods In this prospective study, from September 2016 to December 2017, 56 study participants underwent DCE MRI after gadobutrol administration and were classified into migraine (n = 35) and healthy control (n = 21) groups. Automatic volumetric segmentation was performed on the pre-contrast-enhanced T1-weighted images by using FreeSurfer, and migraine-associated brain region masks were extracted by using the software NordicICE. The corresponding maps for pharmacokinetic parameters Ktrans (the volume transfer constant) and Vp (the fractional plasma volume) were coregistered with the region-of-interest masks, and their mean values of corresponding total volume of interest were calculated. For comparison analyses, the Mann-Whitney tests were used. Receiver operating characteristic curve analysis and Spearman rank correlation tests were used to identify correlations between clinical characteristics and the aforementioned perfusion parameters. Results Mean age was younger in the migraine group (mean ± standard deviation, 57 years ± 12) than in the healthy control group (mean, 71 years ± 8) (P < .001). In the migraine group, the mean value of Vp in the left amygdala (median, 0.27 mL/100 g) was lower than that in the healthy control group (median, 0.39 mL/100 g) (P = .04). The mean value of Vp in the left amygdala was correlated with the intensity of headache attack in participants with migraine (correlation coefficient, -0.34; P = .04). Conclusion Lower fractional plasma volume in the left amygdala was observed in participants with migraine than in healthy participants. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Carroll and Ginat in this issue.

The Association of Background Parenchymal Enhancement at Breast MRI with Breast Cancer: A Systematic Review and Meta-Analysis

BackgroundThe higher level of background parenchymal enhancement (BPE) at breast MRI has the potential for early detection and prediction of the risk of breast cancer. However, conflicting findings have been reported about the association between the level of BPE at breast MRI and the presence of breast cancer.PurposeTo evaluate the association between qualitative and quantitative BPE at dynamic contrast material-enhanced MRI and breast cancer among populations with average risk and high risk separately.Materials and MethodsA retrospective meta-analysis of observational studies comparing either qualitative or quantitative assessments of BPE in women with and women without breast cancer was performed for studies published through July 2018. Pooled odds ratios (ORs) or standardized mean differences and 95% confidence intervals (CIs) were estimated by using DerSimonian-Laird random-effects models. The heterogeneity across the studies was measured by using the statistic I 2. Sensitivity analyses were conducted to test this association according to different study characteristics. P values less than or equal to 5% were considered to indicate statistically significant results.ResultsEighteen studies comprising 1910 women with breast cancer and 2541 control participants were included in the analysis. Among women with high risk, at least moderate BPE (OR, 1.6; 95% CI: 1.0, 2.6; P = .04) or at least mild BPE (OR, 2.1; 95% CI: 1.5, 3.0; P < .001) was associated with higher odds of breast cancer. Furthermore, women with breast cancer showed a higher average BPE percentage compared with control participants with high risk (standardized mean difference, 0.5; 95% CI: 0.2, 0.9; P = .001). No association was observed between at least mild BPE level (P = .15) or at least moderate BPE level (P = .38) and the presence of breast cancer among the population with average risk.ConclusionA higher level of background parenchymal enhancement measured at breast MRI is associated with the presence of breast cancer in women with high risk, but not in women with average risk.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Mann and Pinker in this issue.

Interreader Variability of Dynamic Contrast-enhanced MRI of Recurrent Glioblastoma: The Multicenter ACRIN 6677/RTOG 0625 Study.

Purpose To evaluate factors contributing to interreader variation (IRV) in parameters measured at dynamic contrast material-enhanced (DCE) MRI in patients with glioblastoma who were participating in a multicenter trial. Materials and Methods A total of 18 patients (mean age, 57 years ± 13 [standard deviation]; 10 men) who volunteered for the advanced imaging arm of ACRIN 6677, a substudy of the RTOG 0625 clinical trial for recurrent glioblastoma treatment, underwent analyzable DCE MRI at one of four centers. The 78 imaging studies were analyzed centrally to derive the volume transfer constant (Ktrans) for gadolinium between blood plasma and tissue extravascular extracellular space, fractional volume of the extracellular extravascular space (ve), and initial area under the gadolinium concentration curve (IAUGC). Two independently trained teams consisting of a neuroradiologist and a technologist segmented the enhancing tumor on three-dimensional spoiled gradient-recalled acquisition in the steady-state images. Mean and median parameter values in the enhancing tumor were extracted after registering segmentations to parameter maps. The effect of imaging time relative to treatment, map quality, imager magnet and sequence, average tumor volume, and reader variability in tumor volume on IRV was studied by using intraclass correlation coefficients (ICCs) and linear mixed models. Results Mean interreader variations (± standard deviation) (difference as a percentage of the mean) for mean and median IAUGC, mean and median Ktrans, and median ve were 18% ± 24, 17% ± 23, 27% ± 34, 16% ± 27, and 27% ± 34, respectively. ICCs for these metrics ranged from 0.90 to 1.0 for baseline and from 0.48 to 0.76 for posttreatment examinations. Variability in reader-derived tumor volume was significantly related to IRV for all parameters. Conclusion Differences in reader tumor segmentations are a significant source of interreader variation for all dynamic contrast-enhanced MRI parameters. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Wolf in this issue.

Dynamic Contrast-enhanced Breast MRI for Evaluating Residual Tumor Size after Neoadjuvant Chemotherapy.

Purpose To investigate the accuracy of dynamic contrast material-enhanced (DCE) breast MRI for determining residual tumor size after neoadjuvant chemotherapy (NAC). Materials and Methods For this retrospective study, 487 consecutive women (mean age, 47.0 years ± 10.3 [standard deviation]; range, 24-78 years) underwent preoperative DCE MRI following NAC and subsequent surgeries between 2008 and 2011. Tumor size was measured at early-phase, conventional delayed-phase, and late delayed-phase MRI (90, 360, and 590 seconds after contrast material injection, respectively). At histopathologic examination, total tumor size (both invasive and in situ) and the size of invasive tumor alone were separately recorded. Absolute agreement between tumor size at MRI and histopathologic examination was assessed by using intraclass correlation coefficient (ICC) analysis. Factors affecting size discrepancy were assessed by using multiple linear regression analysis. Results Compared with tumor size at histopathologic examination, total tumor sizes showed higher agreement at conventional delayed-phase MRI than at early-phase MRI (ICC, 0.76 vs 0.56; P ˂ .001) and comparable agreement at conventional and late delayed-phase MRI (ICC, 0.76 vs 0.74; P = .55). Lobular histologic features and tumor subtype were independently associated with greater size discrepancy (P ˂ .001). Lobular cancers were underestimated in size compared with ductal cancers (mean size discrepancy, -2.8 cm ± 3.2 vs -0.3 cm ± 1.8; P = .004). Estrogen receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative cancers were underestimated compared with HER2-positive cancers (-0.8 cm ± 2.0 vs -0.3 cm ± 1.7, P = .006) and triple-negative cancers (-0.8 cm ± 2.0 vs 0.3 cm ± 1.7, P ˂ .001). Conclusion Delayed-phase MRI is more accurate than early-phase MRI for evaluating residual breast tumor size after neoadjuvant chemotherapy. Lobular or estrogen receptor-positive/human epidermal growth factor receptor 2-negative cancers are underestimated in size at MRI compared with ductal or other subtypes. © RSNA, 2018 Online supplemental material is available for this article.

Pancreatic Ductal Adenocarcinoma: Rim Enhancement at MR Imaging Predicts Prognosis after Curative Resection.

Purpose To identify features at preoperative magnetic resonance (MR) imaging that could predict favorable prognosis after curative resection of pancreatic ductal adenocarcinoma (PDAC). Materials and Methods From January 2009 to December 2014, this retrospective study included 143 patients with surgically resected (ie, R0) PDAC who underwent preoperative MR imaging within 1 month before surgery. Clinical-pathologic and MR imaging findings for predicting disease-free survival (DFS) and overall survival (OS) were identified by using a Cox proportional hazards model. Important MR imaging features were compared with clinical-pathologic findings. Results Tumor size at histopathologic analysis was associated with both DFS and OS (hazard ratio per centimeter, 1.37; 95% confidence interval: 1.15, 1.63; P < .001 and hazard ratio, 1.44; 95% confidence interval: 1.20, 1.73; P < .001, respectively). Rim enhancement at dynamic contrast material-enhanced MR imaging was associated with significantly worse DFS and OS (hazard ratio, 1.72; 95% confidence interval: 1.05, 2.82; P = .030 and hazard ratio, 2.27; 95% confidence interval: 1.39, 3.69; P = .001, respectively). Diffusion-weighted imaging parameters, including diffusion restriction and apparent diffusion coefficient value, did not predict DFS or OS after resection of PDAC (all P > .05). Rim-enhancing lesions had more aggressive histologic tumor grades, less frequent remaining acini, and more frequent necrosis within the tumor compared with non-rim-enhancing pancreatic lesions (P = .002, P = .008, and P < .001, respectively). Conclusion Greater tumor size and rim enhancement were associated with lower DFS and OS rates after attempted curative resection of PDAC.

Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer.

Purpose To evaluate a radiomics model of Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 breast lesions extracted from breast-tissue-optimized kurtosis magnetic resonance (MR) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods This institutional study included 222 women at two independent study sites (site 1: training set of 95 patients; mean age ± standard deviation, 58.6 years ± 6.6; 61 malignant and 34 benign lesions; site 2: independent test set of 127 patients; mean age, 58.2 years ± 6.8; 61 malignant and 66 benign lesions). All women presented with a finding suspicious for cancer at x-ray mammography (BI-RADS 4 or 5) and an indication for biopsy. Before biopsy, diffusion-weighted MR imaging (b values, 0-1500 sec/mm2) was performed by using 1.5-T imagers from different MR imaging vendors. Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed. A radiomics feature model was developed by using a random forest regressor. The fixed model was tested on an independent test set. Conventional interpretations of MR imaging were also assessed for comparison. Results The radiomics feature model reduced false-positive results from 66 to 20 (specificity 70.0% [46 of 66]) at the predefined sensitivity of greater than 98.0% [60 of 61] in the independent test set, with BI-RADS 4a and 4b lesions benefiting from the analysis (specificity 74.0%, [37 of 50]; 60.0% [nine of 15]) and BI-RADS 5 lesions showing no added benefit. The model significantly improved specificity compared with the median apparent diffusion coefficient (P < .001) and apparent kurtosis coefficient (P = .02) alone. Conventional reading of dynamic contrast material-enhanced MR imaging provided sensitivity of 91.8% (56 of 61) and a specificity of 74.2% (49 of 66). Accounting for fat signal intensity during fitting significantly improved the area under the curve of the model (P = .001). Conclusion A radiomics model based on kurtosis diffusion-weighted imaging performed by using MR imaging machines from different vendors allowed for reliable differentiation between malignant and benign breast lesions in both a training and an independent test data set. © RSNA, 2018 Online supplemental material is available for this article.

Brain Gliomas: Multicenter Standardized Assessment of Dynamic Contrast-enhanced and Dynamic Susceptibility Contrast MR Images.

Purpose To evaluate the feasibility of a standardized protocol for acquisition and analysis of dynamic contrast material-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance (MR) imaging in a multicenter clinical setting and to verify its accuracy in predicting glioma grade according to the new World Health Organization 2016 classification. Materials and Methods The local research ethics committees of all centers approved the study, and informed consent was obtained from patients. One hundred patients with glioma were prospectively examined at 3.0 T in seven centers that performed the same preoperative MR imaging protocol, including DCE and DSC sequences. Two independent readers identified the perfusion hotspots on maps of volume transfer constant (Ktrans), plasma (vp) and extravascular-extracellular space (ve) volumes, initial area under the concentration curve, and relative cerebral blood volume (rCBV). Differences in parameters between grades and molecular subtypes were assessed by using Kruskal-Wallis and Mann-Whitney U tests. Diagnostic accuracy was evaluated by using receiver operating characteristic curve analysis. Results The whole protocol was tolerated in all patients. Perfusion maps were successfully obtained in 94 patients. An excellent interreader reproducibility of DSC- and DCE-derived measures was found. Among DCE-derived parameters, vp and ve had the highest accuracy (are under the receiver operating characteristic curve [Az] = 0.847 and 0.853) for glioma grading. DSC-derived rCBV had the highest accuracy (Az = 0.894), but the difference was not statistically significant (P > .05). Among lower-grade gliomas, a moderate increase in both vp and rCBV was evident in isocitrate dehydrogenase wild-type tumors, although this was not significant (P > .05). Conclusion A standardized multicenter acquisition and analysis protocol of DCE and DSC MR imaging is feasible and highly reproducible. Both techniques showed a comparable, high diagnostic accuracy for grading gliomas. © RSNA, 2018 Online supplemental material is available for this article.

Differentiation of High-Grade from Low-Grade Astrocytoma: Improvement in Diagnostic Accuracy and Reliability of Pharmacokinetic Parameters from DCE MR Imaging by Using Arterial Input Functions Obtained from DSC MR Imaging.

Purpose To evaluate whether arterial input functions (AIFs) derived from dynamic susceptibility-contrast (DSC) magnetic resonance (MR) imaging, or AIFDSC values, improve diagnostic accuracy and reliability of the pharmacokinetic (PK) parameters of dynamic contrast material-enhanced (DCE) MR imaging for differentiating high-grade from low-grade astrocytomas, compared with AIFs obtained from DCE MR imaging (AIFDCE). Materials and Methods This retrospective study included 226 patients (138 men, 88 women; mean age, 52.27 years ± 15.17; range, 24-84 years) with pathologically confirmed astrocytomas (World Health Organization grade II = 21, III = 53, IV = 152; isocitrate dehydrogenase mutant, 11.95% [27 of 226]; 1p19q codeletion 0% [0 of 226]). All patients underwent both DSC and DCE MR imaging before surgery, and AIFDSC and AIFDCE were obtained from each image. Volume transfer constant (Ktrans), volume of vascular plasma space (vp), and volume of extravascular extracellular space (ve) were processed by using postprocessing software with two AIFs. The diagnostic accuracies of individual parameters were compared by using receiver operating characteristic curve (ROC) analysis. Intraclass correlation coefficients (ICCs) and the Bland-Altman method were used to assess reliability. Results The AIFDSC-driven mean Ktrans and ve were more accurate for differentiating high-grade from low-grade astrocytoma than those derived by using AIFDCE (area under the ROC curve: mean Ktrans, 0.796 vs 0.645, P = .038; mean ve, 0.794 vs 0.658, P = .020). All three parameters had better ICCs with AIFDSC than with AIFDCE (Ktrans, 0.737 vs 0.095; vp, 0.848 vs 0.728; ve, 0.875 vs 0.581, respectively). In AIF analysis, maximal signal intensity (0.837 vs 0.524) and wash-in slope (0.800 vs 0.432) demonstrated better ICCs with AIFDSC than AIFDCE. Conclusion AIFDSC-driven DCE MR imaging PK parameters showed better diagnostic accuracy and reliability for differentiating high-grade from low-grade astrocytoma than those derived from AIFDCE. © RSNA, 2017 Online supplemental material is available for this article.

Ductal Carcinoma in Situ: Quantitative Preoperative Breast MR Imaging Features Associated with Recurrence after Treatment.

Purpose To investigate whether specific imaging features on breast magnetic resonance (MR) images are associated with ductal carcinoma in situ (DCIS) recurrence risk after definitive treatment. Materials and Methods Patients with DCIS who underwent preoperative dynamic contrast material-enhanced (DCE) MR imaging between 2004 and 2014 with ipsilateral recurrence more than 6 months after definitive surgical treatment were retrospectively identified. For each patient, a control subject with DCIS that did not recur was identified and matched on the basis of clinical, histopathologic, and treatment features known to affect recurrence risk. On DCE MR images, lesion characteristics (longest diameter, functional tumor volume [FTV], peak percentage enhancement [PE], peak signal enhancement ratio [SER], and washout fraction) and normal tissue features (background parenchymal enhancement [BPE] volume, mean BPE) were quantitatively measured. MR imaging features were compared between patients and control subjects by using the Wilcoxon signed-rank test, with adjustment for multiple comparisons. Results Of 415 subjects with DCIS who underwent preoperative MR imaging, 14 experienced recurrence and 11 had an identifiable matching control subject (final cohort, 11 patients and 11 control subjects). Median time to recurrence was 14 months, and median follow-up for control subjects was 102 months. When compared with matched control subjects, patients with DCIS recurrence exhibited significantly greater FTV (median, 9.3 cm3 vs 1.3 cm3, P = .01), lesion peak SER (median, 1.7 vs 1.2; P = .03), and mean BPE (median, 58.3% vs 41.1%; P = .02). Conclusion Quantitative lesion and normal breast tissue characteristics at preoperative MR imaging in women with newly diagnosed DCIS show promise for association with breast cancer recurrence after treatment. © RSNA, 2017.

Dynamic Contrast-enhanced MR Imaging Parameters in Bone Metastases from Non-Small Cell Lung Cancer: Comparison between Lesions with and Lesions without Epidermal Growth Factor Receptor Mutation in Primary Lung Cancer.

Purpose To compare dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging parameters between metastatic bone lesions with and without the epidermal growth factor receptor (EGFR) mutation in consecutive patients with primary non-small cell lung cancer (NSCLC). Materials and Methods This study was approved by the institutional review board. Forty-seven patients with NSCLC and a confirmed EGFR mutation status (27 patients were positive and 26 were negative for EGFR mutation), who underwent DCE MR imaging for bone metastases between November 2012 and March 2016, were included in this study. Two radiologists independently analyzed the volume transfer constant (Ktrans), reflux rate (kep), and volume fraction of the extravascular extracellular matrix (ve) using image processing software. Intergroup comparisons of the mean measured parameters were performed with the Mann-Whitney U test. Interobserver agreement was calculated with the intraclass correlation coefficient. Results There was a high level of agreement between the two reviewers for all three parameters (intraclass correlation coefficient = 0.95 for Ktrans, 0.97 for kep, and 0.91 for ve). Ktrans was significantly higher in the EGFR mutation-positive group (P = .039 for reviewer 1, P = .032 for reviewer 2). kep was also higher in the EGFR mutation-positive group but showed statistical significance only in the evaluation performed by one reviewer (P = .048 for reviewer 2, P = .058 for reviewer 1). No significant difference was observed in ve (P = .873 for reviewer 1, P = .889 for reviewer 2). Conclusion The differences in the DCE MR imaging parameters between metastatic bone lesions with and without EGFR mutations in primary NSCLC may be attributed to differences in the vascular structure related to angiogenesis stimulated by the activation of the EGFR signaling pathway. © RSNA, 2017 Online supplemental material is available for this article.

Detection of Clinically Significant Prostate Cancer: Short Dual-Pulse Sequence versus Standard Multiparametric MR Imaging-A Multireader Study.

Purpose To compare the diagnostic performance of a short dual-pulse sequence magnetic resonance (MR) imaging protocol versus a standard six-pulse sequence multiparametric MR imaging protocol for detection of clinically significant prostate cancer. Materials and Methods This HIPAA-compliant study was approved by the regional ethics committee. Between July 2013 and March 2015, 63 patients from a prospectively accrued study population who underwent MR imaging of the prostate including transverse T1-weighted; transverse, coronal, and sagittal T2-weighted; diffusion-weighted; and dynamic contrast material-enhanced MR imaging with a 3-T imager at a single institution were included in this retrospective study. The short MR imaging protocol image set consisted of transverse T2-weighted and diffusion-weighted images only. The standard MR imaging protocol image set contained images from all six pulse sequences. Three expert readers from different institutions assessed the likelihood of prostate cancer on a five-point scale. Diagnostic performance on a quadrant basis was assessed by using areas under the receiver operating characteristic curves, and differences were evaluated by using 83.8% confidence intervals. Intra- and interreader agreement was assessed by using the intraclass correlation coefficient. Transperineal template saturation biopsy served as the standard of reference. Results At histopathologic evaluation, 84 of 252 (33%) quadrants were positive for cancer in 38 of 63 (60%) men. There was no significant difference in detection of tumors larger than or equal to 0.5 mL for any of the readers of the short MR imaging protocol, with areas under the curve in the range of 0.74-0.81 (83.8% confidence interval [CI]: 0.64, 0.89), and for readers of the standard MR imaging protocol, areas under the curve were 0.71-0.77 (83.8% CI: 0.62, 0.86). Ranges for sensitivity were 0.76-0.95 (95% CI: 0.53, 0.99) and 0.76-0.86 (95% CI: 0.53, 0.97) and those for specificity were 0.84-0.90 (95% CI: 0.79, 0.94) and 0.82-0.90 (95% CI: 0.77, 0.94) for the short and standard MR protocols, respectively. Ranges for interreader agreement were 0.48-0.60 (83.8% CI: 0.41, 0.66) and 0.49-0.63 (83.8% CI: 0.42, 0.68) for the short and standard MR imaging protocols. Conclusion For the detection of clinically significant prostate cancer, no difference was found in the diagnostic performance of the short MR imaging protocol consisting of only transverse T2-weighted and diffusion-weighted imaging pulse sequences compared with that of a standard multiparametric MR imaging protocol. © RSNA, 2017 Online supplemental material is available for this article.

Value of Dynamic Contrast-enhanced and Diffusion-weighted MR Imaging in the Detection of Pathologic Complete Response in Cervical Cancer after Neoadjuvant Therapy: A Retrospective Observational Study.

Purpose To evaluate the association between dynamic contrast material-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance (MR) imaging with pathologic complete response after preoperative combined chemotherapy and radiation therapy for cervical carcinoma and evaluate the risk of local recurrence. Materials and Methods The institutional ethics committee approved the study and waived the requirement to obtain informed consent. The study comprised 52 patients with locally advanced carcinoma, treated first with combined chemotherapy and radiation therapy, who underwent MR imaging before final surgery between June 2011 and July 2015. Three radiologists evaluated conventional, DW, and DCE MR images to identify a complete response. The standard of reference was surgical-pathologic findings. Results An initial increase in signal intensity on DCE MR images that was greater in the cervical lesion than in the myometrium was defined as time-signal intensity curve type B and showed a significant association with incomplete response (P = .0004). DCE MR imaging parameters (ie, maximum slope enhancement, area under the gadolinium concentration-time curve during the first 90 seconds after gadolinium injection [AUGC90], and volume transfer constant [Ktrans]) and a low signal intensity on apparent diffusion coefficient (ADC) maps were significantly associated with an incomplete response (P = .027, P = .041, P = .037, and P = .032, respectively). A mean ADC of 0.0014 m2/sec or less (hazard ratio [HR] = 8.3), low ADC signal intensity (HR = 7.3), high signal intensity at DW imaging (HR = 7.1), and time-signal intensity curve type B (HR = 4.3) were associated with earlier recurrence (P < .05). Excellent agreement between readers was found for time-signal intensity curve analysis (κ > 0.9) and the following parameters: AUGC90, Ktrans, and maximum slope enhancement (intraclass correlation coefficient, >0.9). Conclusion DCE MR imaging parameters, especially the time-signal intensity curve, and DW imaging are associated with complete response and incomplete response and could potentially help oncologists with management decisions. Moreover, DCE and DW MR imaging could help oncologists accentuate the follow-up for patients with a high risk of local recurrence to assess for recurrence. © RSNA, 2017 Online supplemental material is available for this article.

Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging.

Purpose To evaluate histogram and texture parameters on pretreatment dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images in lung cancer in terms of temporal change, optimal time for analysis, and prognostic potential. Materials and Methods This retrospective study was approved by the institutional review board, and the requirement to obtain informed consent was waived. Thirty-eight patients with pathologically proved lung cancer undergoing standard pretreatment DCE MR imaging were included. A fat-suppressed, T1-weighted, volume-interpolated breath-hold MR sequence was performed every 30 seconds for 300 and 480 seconds after contrast material administration. A region of interest was manually drawn in the largest cross-sectional area of the tumor on DCE MR images to extract semiquantitative perfusion, histogram, and texture parameters. Predictability of 2-year progression-free survival (PFS) was analyzed by using the Kaplan-Meier method and Cox regression analysis. Results MR histogram and texture parameters increased rapidly 30-60 seconds after contrast material administration. Standard deviation and entropy then plateaued, whereas skewness and kurtosis rapidly decreased. Univariate Cox regression analysis revealed that standard deviation and entropy were significant predictors of survival; their statistical significance was preserved from 60 to 300 seconds, with the smallest P values (P ≤ .001) occurring from 120 to 180 seconds. At multivariate Cox regression analysis, entropy was the sole significant predictor of 2-year PFS (hazard ratio at 180 seconds, 10.098 [95% confidence interval: 1.579, 64.577], P = .015; hazard ratio at 120 seconds: 11.202 [95% confidence interval: 1.761, 71.260], P = .010). Conclusion Histogram and texture parameter changes varied after contrast material injection. The 120-180-second window after contrast material injection was optimal for MR imaging-derived texture parameter and entropy at DCE MR imaging. (©) RSNA, 2016 Online supplemental material is available for this article.