To compare changes in signal intensity (SI) ratios of the dentate nucleus (DN) and the globus pallidus (GP) to those of other structures on unenhanced T1-weighted magnetic resonance (MR) images ...between linear and macrocyclic gadolinium-based contrast agents (GBCAs).
The study was approved by the ethical committee of the University of Heidelberg (reference no. S-324/2014). Owing to the retrospective character of the study, the ethical committee did not require any written informed consent. Two groups of 50 patients who underwent at least six consecutive MR imaging examinations with the exclusive use of either a linear GBCA (gadopentetate dimeglumine) or a macrocyclic GBCA (gadoterate meglumine) were analyzed retrospectively. The difference in mean SI ratios of DN to pons and GP to thalamus on unenhanced T1-weighted images from the last and first examinations was calculated. One-sample and independent-sample t tests were used to assess the difference in SI ratios for both groups, and regression analysis was performed to account for potential confounders.
The SI ratio difference in the linear group was greater than 0 (mean DN difference ± standard deviation, 0.0407 ± 0.0398 P < .001; GP, 0.0287 ± 0.0275 P < .001) and significantly larger (DN, P < .001 and standardized difference of 1.16; GP, P < .001 and standardized difference of 0.81) than that in the macrocyclic group, which did not differ from 0 (DN, 0.0016 ± 0.0266 P = .680; GP, 0.0031 ± 0.0354 P = .538). The SI ratio difference between the last and first examinations for the DN remained significantly different between the two groups in the regression analysis (P < .001).
This study indicates that an SI increase in the DN and GP on T1-weighted images is caused by serial application of the linear GBCA gadopentetate dimeglumine but not by the macrocyclic GBCA gadoterate meglumine. Clinical implications of this observation remain unclear.
The management of patients with brain metastases has become a major issue due to the increasing frequency and complexity of the diagnostic and therapeutic approaches. In 2014, the European ...Association of Neuro-Oncology (EANO) created a multidisciplinary Task Force to draw evidence-based guidelines for patients with brain metastases from solid tumors. Here, we present these guidelines, which provide a consensus review of evidence and recommendations for diagnosis by neuroimaging and neuropathology, staging, prognostic factors, and different treatment options. Specifically, we addressed options such as surgery, stereotactic radiosurgery/stereotactic fractionated radiotherapy, whole-brain radiotherapy, chemotherapy and targeted therapy (with particular attention to brain metastases from non-small cell lung cancer, melanoma and breast and renal cancer), and supportive care.
Purpose To evaluate the association of multiparametric and multiregional magnetic resonance (MR) imaging features with key molecular characteristics in patients with newly diagnosed glioblastoma. ...Materials and Methods Retrospective data evaluation was approved by the local ethics committee, and the requirement to obtain informed consent was waived. Preoperative MR imaging features were correlated with key molecular characteristics within a single-institution cohort of 152 patients with newly diagnosed glioblastoma. Preoperative MR imaging features (n = 31) included multiparametric (anatomic and diffusion-, perfusion-, and susceptibility-weighted images) and multiregional (contrast-enhancing regions and hyperintense regions at nonenhanced fluid-attenuated inversion recovery imaging) information with histogram quantification of tumor volumes, volume ratios, apparent diffusion coefficients, cerebral blood flow, cerebral blood volume, and intratumoral susceptibility signals. Molecular characteristics determined included global DNA methylation subgroups (eg, mesenchymal, RTK I "PGFRA," RTK II "classic"), MGMT promoter methylation status, and hallmark copy number variations (EGFR, PDGFRA, MDM4, and CDK4 amplification; PTEN, CDKN2A, NF1, and RB1 loss). Univariate analyses (voxel-lesion symptom mapping for tumor location, Wilcoxon test for all other MR imaging features) and machine learning models were applied to study the strength of association and discriminative value of MR imaging features for predicting underlying molecular characteristics. Results There was no tumor location predilection for any of the assessed molecular parameters (permutation-adjusted P > .05). Univariate imaging parameter associations were noted for EGFR amplification and CDKN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values (area under the receiver operating characteristics curve: 63%-69%, false discovery rate-adjusted P < .05). Subjecting all MR imaging features to machine learning-based classification enabled prediction of EGFR amplification status and the RTK II glioblastoma subgroup with a moderate, yet significantly greater, accuracy (63% for EGFR P < .01, 61% for RTK II P = .01) than prediction by chance; prediction accuracy for all other molecular parameters was not significant. Conclusion The authors found associations between established MR imaging features and molecular characteristics, although not of sufficient strength to enable generation of machine learning classification models for reliable and clinically meaningful prediction of molecular characteristics in patients with glioblastoma.
RSNA, 2016 Online supplemental material is available for this article.
To compare multiparametric diagnostic performance with diffusion-weighted, dynamic susceptibility-weighted contrast material-enhanced perfusion-weighted, and susceptibility-weighted magnetic ...resonance (MR) imaging for differentiating primary central nervous system lymphoma (PCNSL) and atypical glioblastoma.
This retrospective study was institutional review board-approved and informed consent was waived. Pretreatment MR imaging was performed in 314 patients with glioblastoma, and a subset of 28 patients with glioblastoma of atypical appearance (solid enhancement with no visible necrosis) was selected. Parameters of diffusion-weighted (apparent diffusion coefficient ADC), susceptibility-weighted (intratumoral susceptibility signals ITSS), and dynamic susceptibility-weighted contrast-enhanced perfusion-weighted (relative cerebral blood volume rCBV) imaging were evaluated in these 28 patients with glioblastoma and 19 immunocompetent patients with PCNSL. A two-sample t test and χ(2) test were used to compare parameters.The diagnostic performance for differentiating PCNSL from glioblastoma was evaluated by using logistic regression analyses with leave-one-out cross validation.
Minimum, maximum, and mean ADCs and maximum and mean rCBVs were significantly lower in patients with PCNSL than in those with glioblastoma (P < .01, respectively), whereas mean ADCs and mean rCBVs allowed the best diagnostic performance. Presence of ITSS was significantly lower in patients with PCNSL (32% six of 19) than in those with glioblastoma (82% 23 of 28) (P < .01). Multiparametric assessment of mean ADC, mean rCBV, and presence of ITSS significantly increased the probability for differentiating PCNSL and atypical glioblastoma compared with the evaluation of one or two imaging parameters (P < .01), thereby correctly predicting histologic results in 95% (18 of 19) of patients with PCNSL and 96% (27 of 28) of patients with atypical glioblastoma.
Combined evaluation of mean ADC, mean rCBV, and presence of ITSS allowed reliable differentiation of PCNSL and atypical glioblastoma in most patients, and these results support an integration of advanced MR imaging techniques for the routine diagnostic workup of patients with these tumors.
Objectives
To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks.
Methods
Dual-energy CTs in the ...arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (−50% and −80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency.
Results
The −80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the −50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use.
Conclusions
The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results.
Key Points
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The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks
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Not only the image quality but especially the pathological consistency must be evaluated to assess safety
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A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80%
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OBJECTIVESThe aim of this study was to compare changes in signal intensity (SI) ratios of the dentate nucleus (DN) to pons and cerebrospinal fluid (CSF) on unenhanced T1-weighted magnetic resonance ...imaging (MRI) scans between the first and last MRI using the linear gadolinium-based contrast agent gadobenate dimeglumine.
MATERIALS AND METHODSThe study was approved by the ethical committee of the University of Heidelberg (S-324/2014), and written informed consent was waived due to the retrospective character of the study. Fifty patients who underwent at least 5 consecutive MRI examinations (plus an additional last MRI for reference) with the exclusive use of gadobenate dimeglumine were analyzed retrospectively. The difference of DN-to-pons and DN-to-CSF mean SI ratios was calculated on unenhanced T1-weighted images between the first and last examination. Results were compared with previously published data on gadopentetate dimeglumine and gadoterate meglumine.
RESULTSSignal intensity ratio differences for DN-to-pons and DN-to-CSF were significantly greater than 0 (pons0.0399 ± 0.0307, P < 0.001; CSF0.1439 ± 0.1524, P < 0.001). No control variable consistently predicted the SI ratio difference for the DN-to-pons and the DN-to-CSF ratio. Compared with previously published data, the difference in SI increase between gadopentetate dimeglumine and gadobenate dimeglumine was not significant for the DN-to-pons ratio (P = 0.906). In contrast, the DN-to-CSF ratio difference was significantly lower (P < 0.001) for gadobenate dimeglumine. Dentate nucleus-to-pons (P < 0.001) and DN-to-CSF (P = 0.017) ratio differences were both significantly higher for gadobenate dimeglumine than for gadoterate meglumine.
CONCLUSIONSThe present study found an increase in SI in the DN after serial injections of gadobenate dimeglumine. Further studies are needed to clarify the potential of different linear gadolinium-based contrast agents to cause SI increase in the DN.
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/mm
) 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.
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