CE T1WI Research Articles

MRI 3D SPACE T2WI for Pituitary Adenoma Cavernous Sinus Invasion Diagnosis

ObjectiveThis study aims to assess the utility of MRI 3D SPACE T2WI sequences in evaluating cavernous sinus invasion by pituitary adenomas. Materials and methodsData were collected from patients who underwent continuous pituitary MRI examinations at the Medical Imaging Center of our hospital from October 2019 to February 2021. Eligible cases were evaluated for sagittal and axial T1WI sequences, coronal 3D SPACE T2WI sequences, and sagittal and coronal enhanced T1WI sequences using the INFINITT PACS workstation. The Wilcoxon signed-rank test for paired samples and the Mann–Whitney U test for two independent samples were used to statistically analyze differences in image quality scores among various groups. In addition, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of each observation index were compared with intraoperative results. Results3D SPACE T2WI showed superior cavernous sinus imaging quality compared with CE T1WI and T2WI plain scans (P<0.05). The sensitivity, specificity, PPV, and NPV were 100%, 90.0%, 55.60%, and 100.0%, respectively. The accuracy for pituitary adenoma invasiveness diagnosis based on cavernous sinus medial wall integrity was 94.40%. ConclusionThe imaging quality of the medial wall of the cavernous sinus on the 3D SPACE T2WI plain scan sequence surpassed that of CE T1WI TSE-enhanced scans and T2WI TSE plain scans. The continuous observation of the medial wall of the cavernous sinus using this sequence holds great diagnostic value for assessing cavernous sinus invasion by pituitary adenomas. This strategy is more reliable than traditional MRI observation indicators.

Combination of Contrast-enhanced FlAIR and Contrast-enhanced T1WI: A Quick and Efficient Method in Detecting Brain Metastases of Lung Cancers.

Some patients with suspected brain metastases (BM) could not tolerate longer scanning examinations according to the standardized MRI protocol. The purpose of this study was to evaluate the clinical value of contrast-enhanced fast fluid-attenuated inversion recovery (CE FLAIR) imaging in combination with contrast-enhanced T1 weighted imaging (CE T1WI) in detecting BM of lung cancer and explore a quick and effective MRI protocol. In 201 patients with lung cancers and suspected BM, T1WI and FLAIR were performed before and after administration of gadopentetate dimeglumine. Two radiologists reviewed pre- and post-contrast images to determine the presence of abnormal contrast enhancement or signal intensity and decided whether it was metastatic or not on CE T1WI (Group 1) and CE FLAIR (Group 2). The number, locations and features of abnormal findings in two groups were recorded. Receiver Operating Characteristic (ROC) analyses were conducted in three groups: Group 1, 2 and 3(combination of CE FLAIR and CE T1WI). A total of 714 abnormal findings were revealed, of which 672 were considered as BM and 42 nonmetastatic. Superficial and small metastases(≤10mm) in parenchyma and ependyma, leptomeningeal and non-expansive skull metastases were typically better seen on CE FLAIR. The areas under ROC in the three groups were 0.720,0.887 and 0.973, respectively. Group 3 was significantly better in diagnostic efficiency of BMs than Group 1 (p<0.0001) or Group 2 (p=0.0006). The combination of CE T1WI and CE FLAIR promotes diagnostic performance and results in better observation and characterization of BM in patients with lung cancers. It provides a quick and efficient way of detecting BM.

Diffusion-Weighted MR Neurography with Unidirectional Motion-Probing Gradient to Evaluate Lumbar Nerve Roots at 1.5T MR.

Recent studies have demonstrated the usefulness of diffusion-weighted MR neurography (DW MRN) for assessing nerve roots. This study aimed to evaluate the utility of DW MRN with a unidirectional motion-probing gradient (MPG) for the lumbar nerve roots at 1.5T MR. Sixty-four lumbar spine MRI scans with DW MRN using anteroposterior unidirectional MPG were retrospectively analyzed. Any changes in the 512 lumbar spinal nerve roots from L3 to S1 were evaluated using T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE T1WI), and DW MRN, with agreement and correlation analysis. T2WI revealed compression of 78 nerve roots, and CE T1WI revealed 52 instances of nerve root enhancement. Sixty-seven nerve roots showed swelling and hyperintensity on DW MRN. A total of 42 nerve roots showed changes in the CE T1WI and DW MRN sequences. Moderate to substantial agreement and moderate positive correlation were observed between DW MRN and CE T1WI, as well as DW MRN and T2WI (κ = 0.59-0.65, ρ = 0.600-0.653). DW MRN with unidirectional anteroposterior MPG can help evaluate neuritis-related changes in spinal nerve roots and could serve as a sequence capable of complementing or substituting gadolinium CE imaging.

Deep-learning model for diagnostic clue: detecting the dural tail sign for meningiomas on contrast-enhanced T1 weighted images.

Meningiomas are the most common primary central nervous system tumors, and magnetic resonance imaging (MRI), especially contrast-enhanced T1 weighted image (CE T1WI), is used as a fundamental imaging modality for the detection and analysis of the tumors. In this study, we propose an automated deep-learning model for meningioma detection using the dural tail sign. The dataset included 123 patients with 3,824 dural tail signs on sagittal CE T1WI. The dataset was divided into training and test datasets based on specific time point, and 78 and 45 patients were comprised for the training and test dataset, respectively. To compensate for the small sample size of the training dataset, 39 additional patients with 69 dural tail signs from the open dataset were appended to the training dataset. A You Only Look Once (YOLO) v4 network was trained with sagittal CE T1WI to detect dural tail signs. The normal group dataset, comprised of 51 patients with no abnormal finding on MRI, was employed to evaluate the specificity of the trained model. The sensitivity and false positive average were 82.22% and 29.73, respectively, in the test dataset. The specificity and false positive average were 17.65% and 3.16, respectively, in the normal dataset. Most of the false-positive cases in the test dataset were enhancing vessels, misinterpreted as dural thickening. The proposed model demonstrates an automated detection system for the dural tail sign to identify meningioma in general screening MRI. Our model can facilitate and alleviate radiologists' reading process by notifying the possibility of incidental dural mass based on dural tail sign detection.

Leiomyoma or sarcoma? MRI performance in the differential diagnosis of sonographically suspicious uterine masses

PurposeTo assess the diagnostic performance of MRI in distinguishing between leiomyomas and malignant/potentially malignant mesenchymal neoplasms in patients with rapidly enlarging/sonographically suspicious uterine masses. MethodsIRB-approved retrospective study including 88 patients (51 ± 11 years) who underwent MRI for rapidly enlarging/sonographically suspicious uterine mass at our Institution between January 2016 and December 2021, followed by surgery or >12 months follow-up. Qualitative image analysis was independently performed by 2 radiologists and included lesion’s margins (sharp/irregular), architecture (homogeneous/inhomogeneous), presence of endometrial infiltration (yes/no), necrotic areas (yes/no), hemorrhagic areas (yes/no), predominant signal intensity on T1-WI, T2-WI, CE T1-WI, DWI, and ADC map. The same radiologists performed quantitative image analysis in consensus, which included lesion’s maximum diameter, lesion/myometrium signal intensity ratio on T2-WI and CE T1-weighted images, lesion/endometrium signal intensity ratio on DWI and ADC map and necrosis percentage. Lesions were classified as benign or malignant. Imaging findings were compared with pathology and/or follow-up. ResultsAfter surgery (52/88 patients) or follow-up (36/88 patients, 33 ± 20 months), 83/88 (94.3%) lesions were classified as benign and 5/88 (5.7%) as malignant/potentially malignant. Presence of necrotic areas, high necrosis percentage, hyperintensity on DWI and high lesion/endometrium DWI signal intensity ratio were significantly associated with malignant/potentially malignant lesions (p = 0.027, 0.002, 0.008 and 0.015, respectively). The two readers identified malignant/potentially malignant lesions with 95.5% accuracy, 80.0% sensitivity, 96.4% specificity, 57.1 % PPV, 93.3% NPV. ConclusionMRI has high accuracy in identifying malignant/potentially malignant myometrial masses. In everyday practice, however, MRI positive predictive value is relatively low given the low pre-test malignancy probability.

Preoperative Assessment of Blood Vessels and Intratumoral Microbleeds in Brain Tumors Based on a 3D Contrast-Enhanced T1 -Weighted Flow-Sensitive Black-Blood Sequence.

Three-dimensional (3D) contrast-enhanced T1 -weighted flow-sensitive black-blood (CE-T1 WI FSBB) is a newly developed black blood sequence by adding motion probing gradient pulses to gradient echo (GRE) sequences, which has important value for the preoperative assessment of tumor brain blood supply vessels and intratumoral microbleeds. To compare 3D CE-T1 WI FSBB and 3D contrast-enhanced fast spin echo (FSE) sequence for T1 WI for preoperative assessment of blood vessels and microbleeds in brain tumors and to investigate the correlation between visible vessels and microbleeds. Prospective. One hundred and seventy-five patients with brain tumors, 65 were male, 110 were female. Including histologically confirmed 73 meningiomas, 23 schwannomas, 20 gliomas, 7 hemangioblastomas, 5 metastases, 2 lymphomas, 2 hemangiopericytomas, 2 germ cell tumors, 1 craniopharyngioma, and 1 cholesteatoma. A 3-T, CE-T1 WI FSBB, GRE; 3-T, CE-T1 WI, FSE. Three neuroradiologists counted the number of intratumoral vessels on CE-T1 WI and CE-T1 WI FSBB images separately, and they counted the number of intratumoral microbleeds on CE-T1 WI FSBB images. Brain tumors were classified into grade I, grade II, and grade IV according to the World Health Organization (WHO) grading. Differences in the ability of CE-T1 WI FSBB and CE-T1 WI to display intratumoral vessels were compared. The mean counts of three observers were used to study the correlation between vessels and microbleeds. Two-way random intraclass correlation coeficient (ICC) was used for inter-reader agreement regarding intratumoral vessel and microbleed counts, and the linear regression analysis (with F-test) was used to study the correlation between intratumoral vessels and microbleeds based on CE-T1 WI FSBB (α=0.05). Inter-reader agreements for intratumoral vessel count on CE-T1 WI (ICC=0.93) and CE-T1 WI FSBB (ICC=0.92), and the agreement for intratumoral microbleed count on CE-T1 WI FSBB (ICC=0.99) were excellent. There were statistically significant differences in intratumoral vessel counts between CE-T1 WI and CE-T1 WI FSBB using Mann-Whitney U -test: image readers could identify more intratumoral vessels on CE-T1 WI FSBB images, particularly for meningiomas, schwannomas, gliomas, and WHO grade I tumors. The number of intratumoral vessels had a significant positive effect on the number of intratumoral microbleeds (microbleeds=5.024 + 1.665 × vessels; F=11.51). More intratumoral vessels could potentially be identified using a 3D CE-T1 WI FSBB sequence compared to a CE-T1 WI sequence, and the number of intratumoral vessels showed a positive linear relationship with the number of intratumoral microbleeds, which might suggest that brain tumors with rich blood supply were more prone to intratumoral microbleeds. 2 TECHNICAL EFFICACY: Stage 3.

Multimodality MRI-based radiomics for aggressiveness prediction in papillary thyroid cancer

ObjectiveTo investigate the ability of a multimodality MRI-based radiomics model in predicting the aggressiveness of papillary thyroid carcinoma (PTC).MethodsThis study included consecutive patients who underwent neck magnetic resonance (MR) scans and subsequent thyroidectomy during the study period. The pathological diagnosis of thyroidectomy specimens was the gold standard to determine the aggressiveness. Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted. A machine learning model was established to evaluate the prediction of PTC aggressiveness.ResultsThe study cohort included 107 patients with PTC confirmed by pathology (cross-validation cohort: n = 71; test cohort: n = 36). A total of 1584 features were extracted from contrast-enhanced T1-weighted (CE-T1 WI), T2-weighted (T2 WI) and diffusion weighted (DWI) images of each patient. Sparse representation method is used for radiation feature selection and classification model establishment. The accuracy of the independent test set that using only one modality, like CE-T1WI, T2WI or DWI was not particularly satisfactory. In contrast, the result of these three modalities combined achieved 0.917.ConclusionOur study shows that multimodality MR image based on radiomics model can accurately distinguish aggressiveness in PTC from non-aggressiveness PTC before operation. This method may be helpful to inform the treatment strategy and prognosis of patients with aggressiveness PTC.

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.

Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies. To investigate the use of a convolutional neural network (CNN) model for differentiation of PCNSL and GBM without tumor delineation. Retrospective. A total of 289 patients with PCNSL (136) or GBM (153) were included, the average age of the cohort was 54 years, and there were 173 men and 116 women. 3.0 T Axial contrast-enhanced T1 -weighted spin-echo inversion recovery sequence (CE-T1 WI), T2 -weighted fluid-attenuation inversion recovery sequence (FLAIR), and diffusion weighted imaging (DWI, b=0 second/mm2 , 1000 seconds/mm2 ). A single-parametric CNN model was built using CE-T1 WI, FLAIR, and the apparent diffusion coefficient (ADC) map derived from DWI, respectively. A decision-level fusion based multi-parametric CNN model (DF-CNN) was built by combining the predictions of single-parametric CNN models through logistic regression. An image-level fusion based multi-parametric CNN model (IF-CNN) was built using the integrated multi-parametric MR images. The radiomics models were developed. The diagnoses by three radiologists with 6 years (junior radiologist Y.Y.), 11 years (intermediate-level radiologist Y.T.), and 21 years (senior radiologist Y.L.) of experience were obtained. The 5-fold cross validation was used for model evaluation. The Pearson's chi-squared test was used to compare the accuracies. U-test and Fisher's exact test were used to compare clinical characteristics. The CE-T1 WI, FLAIR, and ADC based single-parametric CNN model had accuracy of 0.884, 0.782, and 0.700, respectively. The DF-CNN model had an accuracy of 0.899 which was higher than the IF-CNN model (0.830, P=0.021), but had no significant difference in accuracy compared to the radiomics model (0.865, P=0.255), and the senior radiologist (0.906, P=0.886). A CNN model can differentiate PCNSL from GBM without tumor delineation, and comparable to the radiomics models and radiologists. 4 TECHNICAL EFFICACY: Stage 2.

Multiparametric-MRI-Based Radiomics Model for Differentiating Primary Central Nervous System Lymphoma From Glioblastoma: Development and Cross-Vendor Validation.

Preoperative differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) is important to guide neurosurgical decision-making. To validate the generalization ability of radiomics models based on multiparametric-MRI (MP-MRI) for differentiating PCNSL from GBM. Retrospective. In all, 240 patients with GBM (n = 129) or PCNSL (n = 111). 3.0T scanners (two vendors). Sequences: fluid-attenuation inversion recovery, diffusion-weighted imaging (DWI), and contrast-enhanced T1 -weighted imaging (CE-T1 WI). Apparent diffusion coefficients (ADCs) were derived from DWI. Cross-vendor and mixed-vendor validation were conducted. In cross-vendor validation, the training set was 149 patients' data from vendor 1, and test set was 91 patients' data from vendor 2. In mixed-vendor validation, a training set was 80% of data from both vendors, and the test set remained at 20% of data. Single and multisequence radiomics models were built. The diagnoses by radiologists with 5, 10, and 20 years' experience were obtained. The integrated models were built combining the diagnoses by the best-performing radiomics model and each radiologist. Model performance was validated in the test set using area under the ROC curve (AUC). Histological results were used as the reference standard. DeLong test: differences between AUCs. U-test: differences of numerical variables. Fisher's exact test: differences of categorical variables. In cross-vendor and mixed-vendor validation, the combination of CE-T1 WI and ADC produced the best-performing radiomics model, with AUC of 0.943 vs. 0.935, P = 0.854. The integrated models had higher AUCs than radiologists, with 5 (0.975 vs. 0.891, P = 0.002 and 0.995 vs. 0.885, P = 0.007), 10 (0.975 vs. 0.913, P = 0.029 and 0.995 vs. 0.900, P = 0.030), and 20 (0.975 vs. 0.945, P = 0.179 and 0.995 vs. 0.923, P = 0.046) years' experiences. Radiomics for differentiating PCNSL from GBM was generalizable. The model combining MP-MRI and radiologists' diagnoses had superior performance compared to the radiologists alone. 4 TECHNICAL EFFICACY STAGE: 2.

Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images.

Recent studies have shown that deep learning can help tumor staging automatically. However, automatic nasopharyngeal carcinoma (NPC) staging is difficult due to the lack of large and slice-level annotated datasets. To develop a weakly-supervised deep-learning method to predict NPC patients' T stage without additional annotations. Retrospective. In all, 1138 cases with NPC from 2010 to 2012 were enrolled, including a training set (n=712) and a validation set (n=426). 1.5T, T1 -weighted images (T1 WI), T2 -weighted images (T2 WI), contrast-enhanced T1 -weighted images (CE-T1 WI). We used a weakly-supervised deep-learning network to achieve automated T staging of NPC. T usually refers to the size and extent of the main tumor. The training set was employed to construct the deep-learning model. The performance of the automated T staging model was evaluated in the validation set. The accuracy of the model was assessed by the receiver operating characteristic (ROC) curve. To further assess the performance of the deep-learning-based T score, the progression-free survival (PFS) and overall survival (OS) were performed. The Sklearn package in Python was applied to calculate the area under the curve (AUC) of the ROC. The survcomp package was used for calculations and comparisons between C-indexes. The software SPSS was employed to conduct survival analysis and chi-square tests. The accuracy of the deep-learning model was 75.59% in the validation set. The average AUC of the ROC curve of different stages was 0.943. There were no significant differences in the C-indexes of PFS and OS from the deep-learning model and those from TNM staging, with P values of 0.301 and 0.425, respectively. This weakly-supervised deep-learning approach can perform fully automated T staging of NPC and achieve good prognostic performance. 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:1074-1082.

Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study.

Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients. To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading. Retrospective. This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas. 1.5T MR, containing contrast-enhanced T1 -weighted (CET1 WI), axial T2 -weighted (T2 WI), and apparent diffusion coefficient (ADC) sequences. A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression. Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading. The radiomic signature was significantly associated with glioma grade (P < 0.001) in both the training and validation cohorts. The performance of the radiomics nomogram derived from three MRI sequences (with C-index of 0.971 and 0.961 in the training and validation cohorts, respectively) was improved compared to those based on either CET1 WI, T2 WI, or ADC alone in glioma grading (with C-index of 0.914, 0.714, 0.842 in the training cohort, and 0.941, 0.500, 0.730 in the validation cohort). The nomogram derived from three sequences showed good calibration: the calibration curve showed good agreement between the estimated and the actual probability. The decision curve demonstrated that combining three sequences had more favorable clinical predictive value than single sequence imaging. We created and assessed a multiparametric MRI-based radiomics nomogram that may help clinicians classify gliomas more accurately. 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:825-833.

Detection of Active Plaques in Multiple Sclerosis using 3 and 12 Directional Diffusion-weighted Imaging: Comparison with Gadolinium-enhanced MR Imaging.

Background: Multiple Sclerosis (MS), distinguished by aggravating the function of central nervous system because of inflammatory demyelination. The most sensitive method for MS diagnosis is Magnetic resonance imaging (MRI). To distinguish inactive and active MS lesions, contrast-enhanced T1-weighted imaging (CE T1WI) is being used as a gold standard. There are some contraindications in gadolinium based contrast agents (GBCAs) usage. Moreover, diffusion-weighted imaging (DWI) can discover diffusion changes involved inflammatory lesions. Objective: The current research aims at investigating if typical DWI (3 directional) and 12 directional DWI could be a substitute for CE T1WI in order to show active lesions of MS.Material and Methods: In this cross-sectional study, 138 patients with CNS symptoms were examined. For all patients, along with CE T1WI, 3 & 12 directional DWI were performed. Intraclass correlation coefficient (ICC), receiver operating characteristic (ROC), the sensitivity versus specificity plot and the area under the curve (AUC) were calculated.Results: There was a contrast enhancement in CE T1WI for 114 patients (82.6%); in addition, hyper-intense lesions on DWI 3 and DWI 12 were shown in 107 (77.5%) and 117 patients (84.7%) in order. Sensitivity, specificity and AUC were 94.7%, 62.5% and 84% for DWI 12. Moreover, the results were 86%, 62.5 and 79% for the sensitivity, specificity and AUC for DWI 3 respectively.Conclusion: In spite of lower sensitivity of 12 directional DWI compared to CE T1WI, it could be used as a diagnostic sequence in differentiating enhanced lesions from non-enhanced ones when CE-MRI is a worry.

Leakage correction improves prognosis prediction of dynamic susceptibility contrast perfusion MRI in primary central nervous system lymphoma

To evaluate whether the cerebral blood volume (CBV) measurement with leakage correction from dynamic susceptibility contrast perfusion weighted imaging can be useful in predicting prognosis for primary central nervous system lymphoma (PCNSL). 46 PCNSL patients were included and classified by radiation therapy (RT) stratification into RT (n = 30) and non-RT (n = 16) groups. The corresponding histogram parameters of normalized CBV (nCBV) maps with or without leakage correction were calculated on contrast-enhanced T1 weighted image (CE T1WI) or on fluid attenuated inversion recovery image. The 75th percentile nCBV with leakage correction based on CE T1WI (T1 nCBVL75%) had a significant difference between the short and long progression free survival (PFS) subgroups of the RT group and the non-RT group, respectively. Based on the survival analysis, patients in the RT group with high T1 nCBVL75% had earlier progression than the others with a low T1 nCBVL75%. However, patients in the non-RT group with a high T1 nCBVL75% had slower progression than the others with a low T1 nCBVL75%. Based on RT stratification, the CBV with leakage correction has potential as a noninvasive biomarker for the prognosis prediction of PCNSL to identify high risk patients and it has a different correlation with the PFS based on the presence of combined RT.

BCAT1 is a New MR Imaging-related Biomarker for Prognosis Prediction in IDH1-wildtype Glioblastoma Patients

Isocitrate dehydrogenase 1 (IDH1)-wildtype glioblastoma (GBM) has found to be accompanied with increased expression of branched-chain amino acid trasaminase1 (BCAT1), which is associated with tumor growth and disease progression. In this retrospective study, quantitative RT-PCR, immunohistochemistry, and western blot were performed with GBM patient tissues to evaluate the BCAT1 level. Quantitative MR imaging parameters were evaluated from DSC perfusion imaging, DWI, contrast-enhanced T1WI and FLAIR imaging using a 3T MR scanner. The level of BCAT1 was significantly higher in IDH1-wildtype patients than in IDH1-mutant patients obtained in immunohistochemistry and western blot. The BCAT1 level was significantly correlated with the mean and 95th percentile-normalized CBV as well as the mean ADC based on FLAIR images. In addition, the 95th percentile-normalized CBV from CE T1WI also had a significant correlation with the BCAT1 level. Moreover, the median PFS in patients with BCAT1 expression <100 was longer than in those with BCAT1 expression ≥100. Taken together, we found that a high BCAT1 level is correlated with high CBV and a low ADC value as well as the poor prognosis of BCAT1 expression is related to the aggressive nature of GBM.

One step diagnosis of multiple sclerosis disease activity, dissemination in time and space using diffusion weighted MRI

ObjectivesTo assess the value of DWI with its two components (DWI-b1000 and DWI b0) images in assessment of active MS and in defining dissemination in time (DIT) and dissemination in space (DIS) in those patients. MethodsBrain MRI studies were performed using T1WI, T2WI, FLAIR and DWI and CE T1WI. ResultsTwenty-one patients (Males/Females=6/15)of age range 18–50 years were included. Forty lesions were hyperintense on DWI-b1000 that had significant link to enhancement seen on contrast-enhanced T1WI (100%) indicating disease activity. Using the 2010 revised McDonald criteria: In 9 patients (42.85%), there were [Asymptomatic active lesions (n=17)]. Those lesions were simultaneously present along with [Non-active demyelinating lesions]; DIT could be diagnosed in these cases based alone on MRI findings. In 12 patients (57.14%), there was simultaneous presence of [Symptomatic active lesions (n=23)]. Those lesions were present along with [Non-active demyelinating lesions]; DIT could be diagnosed in these cases based on both clinical and MRI findings. ConclusionDWI-b1000 is of the same sensitivity as CE T1WI in detection of MS active lesions and in detection of DIT and DIS. DWI-b0 can substitute the T2WI in detection of demyelinating lesions and in confirmation of the MS disease beyond the active stage.

MRI for local staging of endometrial carcinoma: Is endovenous contrast medium administration still needed?

PurposeTo compare the diagnostic performance of T2-weighted images (T2-WI)+contrast-enhanced T1-weighted images (CE T1-WI) with the one of T2-WI+diffusion-weighted images (DWI) in the assessment of myometrial and cervical stromal infiltration by endometrial carcinoma (EC). Materials and methodsInstitutional review board approved our retrospective study; requirement for informed consent was waived. 56 patients with histologically proven EC who underwent preoperative MRI and surgery at our Institution over a 34 months period were included. Two radiologists independently evaluated T2-WI+CE T1-WI and T2-WI+DWI of each patient. Confidence in imaging evaluation (0–3), depth of myometrial invasion (</≥50%) and presence of cervical stromal infiltration (Yes/No) were assessed. MRI findings were compared with pathological ones. ResultsConfidence in imaging evaluation was higher for T2-WI+CE T1-WI (2.83/3) than for T2-WI+DWI (2.62/3). T2-WI+DWI showed a better diagnostic performance than T2-WI+CE T1-WI in recognizing deep myometrial infiltration by EC (accuracy, sensitivity and specificity of 0.89, 0.89 and 0.89 vs. 0.86, 0.84, 0.86; p>0.05) whereas both imaging sequences combinations showed the same diagnostic performance in recognizing cervical stromal infiltration (accuracy, sensitivity and specificity of 0.95, 0.98 and 0.80, p>0.05). ConclusionT2-WI+DWI can reliably replace the “classical” combination T2-WI+CE T1-WI for local staging of endometrial carcinoma.

Hepatocellular carcinoma: Assessment of response to transarterial chemoembolization with image subtraction

To assess the diagnostic accuracy of image subtraction compared with nonsubtracted images obtained with contrast-enhanced T1-weighted imaging (CE T1WI) for the diagnosis of hepatocellular carcinoma (HCC) necrosis after transarterial chemoembolization (TACE), using liver explant as the reference standard. Thirty-four patients who underwent TACE within 90 days of liver transplantation and CE MRI scans were assessed by two independent observers who determined the percentage of tumor necrosis using nonsubtracted and subtracted postcontrast phases. Histopathologic percentage of necrosis was retrospectively determined by an experienced pathologist. Spearman rank correlation test was used to correlate the percentages of necrosis from MR evaluation and from pathology. Receiver operating characteristics curve analysis was performed to determine the performance of subtracted versus nonsubtracted datasets for the diagnosis of complete tumor necrosis. There were 57 HCCs (mean size, 2.4 cm; range, 1.2-4.2 cm) diagnosed at explant and identified on MRI, including 16 completely necrotic HCCs. Subtraction demonstrated better interobserver agreement than nonsubtraction dataset for the diagnosis of tumor necrosis. There was a strong correlation between image subtraction and histopathologic assessment of necrosis (r = 0.80-0.86, depending on the phase, P < 0.0001). Subtraction demonstrated significantly higher sensitivity and accuracy for the diagnosis of complete tumor necrosis compared with nonsubtracted dataset. Image subtraction enables accurate assessment of necrosis of HCC after TACE with the best accuracy observed at the arterial phase.

Diagnostic value of contrast-enhanced fluid-attenuated inversion-recovery MRI for intracranial tumors in comparison with post-contrast T1W spin-echo MRI

Contrast-enhanced fluid-attenuated inversion-recovery (FLAIR) magnetic resonance imaging (MRI) has been reported to have higher sensitivity for detecting leptomeningeal disease compared with contrast-enhanced T1-weighted MRI (CE T1WI). However, currently there are no studies showing the potential value of clinical applications of contrast-enhanced FLAIR (CE FLAIR) sequence in diagnosing intracranial tumors in a larger group of patients. The purpose of this study was to evaluate the diagnostic value of CE FLAIR in comparison with CE T1WI for intracranial tumors and to provide more information for clinical diagnosis and therapy. One hundred and four consecutive cases of intracranial tumors referred for CE brain MRI were analyzed with regard to FLAIR and T1WI pre- and post-administration of Gd-DTPA. The CE FLAIR and CE T1WI were evaluated independently by two radiologists for the number of examinations with one or more enhanced lesions, the number and location of enhanced lesions per examination, signal-to-noise ratio (SNR) and contrast-enhancement ratio (CER) of lesions, as well as the size and extent of the enhanced lesions. In 98 of 104 cases, enhanced lesions were seen both on the FLAIR and T1W images. More lesions were seen on CE T1WI (n = 120) than those on CE FLAIR sequence (n = 117), but no differences of statistical significance were found between the two sequences (P > 0.05). Four lesions were revealed only on the CE FLAIR images whereas 7 lesions were only found on CE T1WI. Enhanced lesions located in the cerebral hemisphere or the forth ventricle were revealed much more on CE T1WI than on CE FLAIR images. However, CE FLAIR images may be useful in showing superficial abnormalities and those located in the sulcus or lateral ventricle. The CER and contrast-to-noise ratio (CNR) on CE T1WI was significantly higher (t = 7.10, P = 0.00; t = 9.67, P = 0.00, respectively), but grey matter/white matter contrast was lower (t = 2.46, P = 0.02) than those on CE FLAIR images. The SNR did not show any statistically significant difference between the two sequences (t = 1.1, P = 0.27). The size and extent of lesions on the CE FLAIR images were significantly larger than those on CE T1WI (t = 4.13, P = 0.00). CE FLAIR and CE T1WI may complement each other in showing intracranial tumors and the CE FLAIR sequence should be selected as a routine MRI sequence.