Post-contrast T1-weighted MR Images Research Articles

NIMG-50. TOTAL TUMOR CELLULARITY AND CELLULAR GROWTH RATES ESTIMATED USING A RADIO-PATHOMIC AI MODEL PREDICTS PROGNOSIS AND RESPONSE TO CYTOTOXIC THERAPY IN RECURRENT GBM

Abstract PURPOSE A radio-pathomic machine learning model trained with postmortem tissue samples has recently been developed to estimate tumor cellularity from standard anatomic MR images (Bobholz SA et al. AJNR 2022). The aim of this study was to use this externally developed, machine learning-based model to assess the impact of tumor cellularity and changes in cellularity on survival in recurrent glioblastoma (rGBM) treated with standard cytotoxic therapies. METHODS We retrospectively evaluated 22 cases of rGBM treated with chemotherapy. Radio-pathomic maps of predicted tumor cellularity were generated using the radio-pathomic model using pre- and post-contrast T1-weighted MR images, T2-weighted FLAIR images and apparent diffusion coefficient calculated from diffusion-weighted imaging. Total cellularity was estimated by integrating the cellularity measures within a contrast-enhancing tumor volume of interest. Cellularity growth rates (CGRs) were computed using three pre-treatment scans and available post-treatment scans. Tumor cellularity and CGRs were assessed in terms of their impact on overall survival (OS) using log-rank analysis. RESULTS Patients with baseline tumor cellularity < 8.89x108 cells and those with pre-treatment CGR < 7.55x106 cells/day showed significantly longer OS than those with higher cellularity or CGRs (P = 0.001 and 0.008, respectively). Patients with cellularity < 8.90x108 cells at the 1st follow-up (one month after treatment), those with cellularity at 2nd follow-up < 8.89x108 cells (two months after treatment), post-treatment CGR < 1.51x107 cells/day, and change in CGR after treatment < +5.91x106 cells/day showed significantly longer OS than those with higher values (P = 0.03, < 0.001, < 0.001, = 0.02, respectively). CONCLUSION These preliminary results suggest the potential value of estimating total cellularity within the enhancing tumor as a biomarker for prognosis prediction and cytotoxic therapy effect evaluation in rGBM. Additional analyses with more cases and treatments are warranted.

Enhancing Annular Fissures and High-Intensity Zones: Pain, Internal Derangement, and Anesthetic Response at Provocation Lumbar Discography.

A high-intensity zone identified on preprocedural MR imaging is known to correlate with pain at provocation lumbar discography. The correlation between enhancing annular fissures and pain at provocation lumbar discography has not been comprehensively evaluated. The purpose of this study was to assess the pain response and imaging features at enhancing annular fissure nonoperated disc levels identified on preprocedural MR imaging with comparison with the high-intensity zone and nonenhancing disc levels in patients referred for provocation lumbar discography. One-hundred nonoperated discs in 44 patients were retrospectively evaluated for an enhancing annular fissure on sagittal postcontrast T1-weighted pre-discogram MR imaging. Enhancing annular fissure discs were graded on the sagittal T2-weighted sequence (Grade 4: like CSF to Grade 1: negative/barely visible) for high-intensity-zone conspicuity. High-intensity-zone detection was performed independently. In the primary assessment, enhancing annular fissure and high-intensity zones were associated with pain response at provocation lumbar discography. Additional analysis included intradiscal anesthetic response and postdiscogram CT appearance. Thirty-nine discs demonstrated an enhancing annular fissure, with 23/39 demonstrating a high-intensity zone. The presence of a high-intensity zone predicted severe pain (concordant + nonconcordant; P = .005, sensitivity of 40%, specificity of 94%) and concordant pain (P = .007, sensitivity of 39%, specificity of 86%) at provocation lumbar discography. Enhancing annular fissures without a detected high-intensity zone were more frequently observed among severely painful (50%) and concordant (36%) discs than among discs negative for pain (9%; P = .01). This finding resulted in a substantially greater overall sensitivity of enhancing annular fissures for severe (P < .001, 64%) and concordant pain (P = .008, 61%), significantly improving the overall predictive ability of a high-intensity zone alone. A high-intensity zone went undetected in 9/11 Grade 1 disc levels with concordant pain present in 7/9. Consideration of enhancing annular fissures on preprocedural MR imaging substantially improves the prediction of severe/concordant pain in provocation lumbar discography.

MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy.

A reliable prognostic stratification of patients with locally advanced hypopharyngeal cancer who had been treated with concurrent chemoradiotherapy (CCRT) is crucial for informing tailored management strategies. The purpose of this retrospective study was to develop robust and objective magnetic resonance imaging (MRI) radiomics-based models for predicting overall survival (OS) and progression-free survival (PFS) in this patient population. The study participants included 198 patients (median age: 52.25 years (interquartile range = 46.88-59.53 years); 95.96% men) who were randomly divided into a training cohort (n = 132) and a testing cohort (n = 66). Radiomic parameters were extracted from post-contrast T1-weighted MR images. Radiomic features for model construction were selected from the training cohort using least absolute shrinkage and selection operator-Cox regression models. Prognostic performances were assessed by calculating the integrated area under the receiver operating characteristic curve (iAUC). The ability of radiomic models to predict OS (iAUC = 0.580, 95% confidence interval (CI): 0.558-0.591) and PFS (iAUC = 0.625, 95% CI = 0.600-0.633) was validated in the testing cohort. The combination of radiomic signatures with traditional clinical parameters outperformed clinical variables alone in the prediction of survival outcomes (observed iAUC increments = 0.279 [95% CI = 0.225-0.334] and 0.293 [95% CI = 0.232-0.351] for OS and PFS, respectively). In summary, MRI radiomics has value for predicting survival outcomes in patients with hypopharyngeal cancer treated with CCRT, especially when combined with clinical prognostic variables.

Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

PurposeDifferentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting. MethodsWe retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org). ResultsThe most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA. ConclusionRadiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.

MR-Based Radiomics for Differential Diagnosis between Cystic Pituitary Adenoma and Rathke Cleft Cyst.

Background It is often tricky to differentiate cystic pituitary adenoma from Rathke cleft cyst with visual inspection because of similar MRI presentations between them. We aimed to design an MR-based radiomics model for improving differential diagnosis between them. Methods Conventional diagnostic MRI data (T1-,T2-, and postcontrast T1-weighted MR images) were obtained from 215 pathologically confirmed patients (105 cases with cystic pituitary adenoma and the other 110 cases with Rathke cleft cyst) and were divided into training (n = 172) and test sets (n = 43). MRI radiomics features were extracted from the imaging data, and semantic imaging features (n = 15) were visually estimated by two radiologists. Four classifiers were used to construct radiomics models through 5-fold crossvalidation after feature selection with least absolute shrinkage and selection operator. An integrated model by combining radiomics and semantic features was further constructed. The diagnostic performance was validated in the test set. Receiver operating characteristic curve was used to evaluate and compare the performance of the models at the background of diagnostic performance by radiologist. Results In test set, the combined radiomics and semantic model using ANN classifier obtained the best classification performance with an AUC of 0.848 (95% CI: 0.750-0.946), accuracy of 76.7% (95% CI: 64.1-89.4%), sensitivity of 73.9% (95% CI: 56.0-91.9%), and specificity of 80.0% (95% CI: 62.5-97.5%) and performed better than multiparametric model (AUC = 0.792, 95% CI: 0.674-0.910) or semantic model (AUC = 0.823, 95% CI: 0.705-0.941). The two radiologists had an accuracy of 69.8% and 74.4%, respectively, sensitivity of 69.6% and 73.9%, and specificity of 70.0% and 75.0%. Conclusions The MR-based radiomics model had technical feasibility and good diagnostic performance in the differential diagnosis between cystic pituitary adenoma and Rathke cleft cyst.

Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis.

ObjectivesTo differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a radiomic features-based Machine Learning (ML) classifier trained from post-contrast three-dimensional T1-weighted (post-contrast 3DT1) MR imaging, and compare its performance in medical diagnosis versus human experts, on a testing cohort.MethodsWe enrolled 143 patients (71 GBM and 72 BM) in a retrospective bicentric study from January 2010 to May 2019 to train the classifier. Post-contrast 3DT1 MR images were performed on a 3-Tesla MR unit and 100 radiomic features were extracted. Selection and optimization of the Machine Learning (ML) classifier was performed using a nested cross-validation. Sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were calculated as performance metrics. The model final performance was cross-validated, then evaluated on a test set of 37 patients, and compared to human blind reading using a McNemar’s test.ResultsThe ML classifier had a mean [95% confidence interval] sensitivity of 85% [77; 94], a specificity of 87% [78; 97], a balanced accuracy of 86% [80; 92], and an AUC of 92% [87; 97] with cross-validation. Sensitivity, specificity, balanced accuracy and AUC were equal to 75, 86, 80 and 85% on the test set. Sphericity 3D radiomic index highlighted the highest coefficient in the logistic regression model. There were no statistical significant differences observed between the performance of the classifier and the experts’ blinded examination.ConclusionsThe proposed diagnostic support system based on radiomic features extracted from post-contrast 3DT1 MR images helps in differentiating solitary BM from GBM with high diagnosis performance and generalizability.

Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower-Grade Gliomas by Using Three-dimensional Fully Convolutional Neural Networks.

To evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MR images generated by using precontrast MR images in patients with brain glioma. In this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast sequences in 400 patients (mean age, 57 years; 239 men; from 2015 to 2020), including 332 with glioblastoma and 68 with lower-grade gliomas. Performance was evaluated by using quantitative image similarity and error metrics and enhancing tumor overlap analysis. Performance was also assessed on a multicenter external dataset (n = 286 from the 2019 Multimodal Brain Tumor Segmentation Challenge; mean age, 60 years; ratio of men to women unknown) by using transfer learning. A subset of cases was reviewed by neuroradiologist readers to assess whether simulated images affected the ability to determine the tumor grade. Simulated whole-brain postcontrast images were both qualitatively and quantitatively similar to the real postcontrast images in terms of quantitative image similarity (structural similarity index of 0.84 ± 0.05), pixelwise error (symmetric mean absolute percent error of 3.65%), and enhancing tumor compartment overlap (Dice coefficient, 0.65 ± 0.25). Similar results were achieved with the external dataset (Dice coefficient, 0.62 ± 0.27). There was no difference in the ability of the neuroradiologist readers to determine the tumor grade in real versus simulated images (accuracy, 87.7% vs 90.6%; P = .87). The developed model was capable of producing simulated postcontrast T1-weighted MR images that were similar to real acquired images as determined by both quantitative analysis and radiologist assessment.Keywords: MR-Contrast Agent, MR-Imaging, CNS, Brain/Brain Stem, Contrast Agents-Intravenous, Neoplasms-Primary, Experimental Investigations, Technology Assessment, Supervised Learning, Transfer Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.

Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm.

Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q status can be assessed prior to tumor resection, we developed a machine learning algorithm to predict the 1p/19q status of presumed LGG based on preoperative MRI. Preoperative brain MR images from 284 patients who had undergone biopsy or resection of presumed LGG were used to train a support vector machine algorithm. The algorithm was trained on the basis of features extracted from post-contrast T1-weighted and T2-weighted MR images and on patients' age and sex. The performance of the algorithm compared with tissue diagnosis was assessed on an external validation dataset of MR images from 129 patients with LGG from The Cancer Imaging Archive (TCIA). Four clinical experts also predicted the 1p/19q status of the TCIA MR images. The algorithm achieved an AUC of 0.72 in the external validation dataset. The algorithm had a higher predictive performance than the average of the neurosurgeons (AUC 0.52) but lower than that of the neuroradiologists (AUC of 0.81). There was a wide variability between clinical experts (AUC 0.45-0.83). Our results suggest that our algorithm can noninvasively predict the 1p/19q status of presumed LGG with a performance that on average outperformed the oncological neurosurgeons. Evaluation on an independent dataset indicates that our algorithm is robust and generalizable.

Imaging findings of lymphocytic hypophysitis easily misdiagnosed as pituitary adenoma

Objective To study the imaging features of lymphocytic hypophysitis in order to improve the accuracy of imaging diagnosis of the disease. Methods Imaging findings of 9 patients with lymphocytic hypophysitis admitted to Neurosurgery Department of Beijing Tiantan Hospital, Capital Medical University from January 2010 to December 2016 were retrospectively reviewed. All patients underwent laboratory examinations related to pituitary function. Except for 2 cases of slight increase in prolactin, the levels of other hormones were normal. All patients were examined with pre-contrast and post-contrast T1 weighted MR imaging and non-contrast T2 weighted MR imaging. Six patients received CT scan. Imaging diagnoses were all mistaken as pituitary adenomas. Results All cases showed diffuse enlargement of the pituitary, disappearance of neurohypophysis T1WI high signal and enlarged sella. Lesions showed iso-signal on both T1 and T2 weighted image with strong homogeneous or ring-like enhancement. Pituitary stalk was thickened in 5 cases. None of the cavernous sinus was involved in those cases. Lymphocytic hypophysitis was confirmed by pathologic examinations in all 9 patients. Conclusions Lymphocytic hypophysitis seems to have characteristic imaging findings. Compared with pituitary adenoma, lymphocytic hypophysitis does not destroy the sellar floor bone with no involvement of the cavernous sinus and it has diffuse pituitary enlargement and basically symmetrical shape. Those imaging findings of lymphocytic hypophysitis may help us improve the accuracy of imaging diagnosis. Key words: Autoimmune diseases; Lymphocytic hypophysitis; Magnetic resonance imaging; X-ray computed tomography

Computer Aided Diagnosis System-A Decision Support System for Clinical Diagnosis of Brain Tumours

The iso, hypo or hyper intensity, similarity of shape, size and location complicates the identification of brain tumors. Therefore, an adequate Computer Aided Diagnosis (CAD) system is designed for classification of brain tumor for assisting inexperience radiologists in diagnosis process. A multifarious database of real post contrast T1-weighted MR images from 10 patients has been taken. This database consists of primary brain tumors namely Meningioma (MENI- class 1), Astrocytoma (AST- class 2), and Normal brain regions (NORM- class 3). The region of interest(s) (ROIs) of size 20 x 20 is extracted by the radiologists from each image in the database. A total of 371 texture and intensity features are extracted from these ROI(s). An Artificial Neural Network (ANN) is used to classify these three classes as it shows better classification results on multivariate non-linear, complicated, rule based domains, and decision making domains. It is being observed that ANN provides much accurate results in terms of individual classification accuracy and overall classification accuracy. The four discrete experiments have been performed. Initially, the experiment was performed by extracting 263 features and an overall classification accuracy 78.10% is achieved, however, it was noticed that MENI (class-1) was highly misclassified with AST (class-2). Further, to improve the overall classification accuracy and individual classification accuracy specifically for MENI (class-1), LAWs textural energy measures (LTEM) are added in the feature bank (263+108=371). An individual class accuracy of 91.40% is obtained for MENI (class-1), 91.43% for AST (class-2), 94.29% for NORM (class-3) and an overall classification accuracy of 92.43% is achieved. The results are calculated with and without addition of LTEM feature with Principle component analysis (PCA)-ANN. LTEM-PCA-ANN approach improved results with an overall accuracy of 93.34%. The texture patterns obtained were clear enough to differentiate between MENI (class-1) and AST (class-2) despite of necrotic and cystic component and location and size of tumor. LTEM detected fundamental texture properties such as level, edge, spot, wave and ripple in both horizontal and vertical directions which boosted the texture energy.

A package-SFERCB-“Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors”

The objective of this experimentation is to develop an interactive CAD system for assisting radiologists in multiclass brain tumor classification. The study is performed on a diversified dataset of 428 post contrast T1-weighted MR images of 55 patients and publically available dataset of 260 post contrast T1-weighted MR images of 10 patients. The first dataset includes primary brain tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), childhood tumor-Medulloblastoma (MED) and Meningioma (MEN), along with secondary tumor-Metastatic (MET). The second dataset consists of Astrocytoma (AS), Low Grade Glioma (LGL) and Meningioma (MEN). The tumor regions are marked by content based active contour (CBAC) model. The regions are than saved as segmented regions of interest (SROIs). 71 intensity and texture feature set is extracted from these SROIs. The features are specifically selected based on the pathological details of brain tumors provided by the radiologist. Genetic Algorithm (GA) selects the set of optimal features from this input set. Two hybrid machine learning models are implemented using GA with support vector machine (SVM) and artificial neural network (ANN) (GA-SVM and GA-ANN) and are tested on two different datasets. GA-SVM is proposed for finding preliminary probability in identifying tumor class and GA-ANN is used for confirmation of accuracy. Test results of the first dataset show that the GA optimization technique has enhanced the overall accuracy of SVM from 79.3% to 91.7% and of ANN from 75.6% to 94.9%. Individual class accuracies delivered by GA-SVM are: AS-89.8%, GBM-83.3%, MED-95.6%, MEN-91.8%, and MET-97.1%. Individual class accuracies delivered by GA-ANN classifier are: AS-96.6%, GBM-86.6%, MED-93.3%, MEN-96%, MET-100%. Similar results are obtained for the second dataset. The overall accuracy of SVM has increased from 80.8% to 89% and that of ANN has increased from 77.5% to 94.1%. Individual class accuracies delivered by GA-SVM are: AS-85.3%, LGL-88.8%, MEN-93%. Individual class accuracies delivered by GA-ANN classifier are: AS-92.6%, LGL-94.4%, MED-95.3%. It is observed from the experiments that GA-ANN classifier has provided better results than GA-SVM. Further, it is observed that along with providing finer results, GA-SVM provides advantage in speed whereas GA-ANN provides advantage in accuracy. The combined results from both the classifiers will benefit the radiologists in forming a better decision for classifying brain tumors.

Delineation and diagnosis of brain tumors from post contrast T1-weighted MR images using rough granular computing and random forest

This paper presents a new approach of delineation and characterization of four different types of brain tumors viz. Glioblastoma multiforme (GBM), metastasis (MET), meningioma (MG) and granuloma (GN) from magnetic resonance imaging (MRI) slices of post contrast T1-weighted (T1C) sequence to improve the computer assistive diagnostic accuracy. An integrated framework of identification and extraction of tumor region, quantification of histogram, shape and textural features followed through pattern classification by machine learning algorithm has been proposed. Rough entropy based thresholding in granular computing paradigm has been adopted for delineation of tumor area. After accomplishing quantitative validation and comparison with existing methods, experimental results prove the efficiency and applicability of proposed segmentation approach. In the next stage, the extracted lesions have been quantified with 86 features to develop the training dataset. Random forest (RF), an ensemble learning scheme has been implemented, which learns the training data for accurate prediction of the class label of a given input. The performance of RF has been evaluated by statistical measures from 3 fold cross-validation and compared with five different classifiers. The same experiment has been repeated over the reduced set of features generated by correlation based feature selection strategy. Experimental results show the superiority of RF (Sensitivity achieved in %: GBM-96.7, MET-96.2, MG-98.1 and GN-97.7) with the complete set of features. The comparison of proposed methodology with the existing works signifies its applicability and effectiveness. Additionally a 10 fold cross-validation has been accomplished to justify the statistical significance of the classification accuracy achieved from proposed methodology.

MRI diagnosis of dural sinus—Cortical venous thrombosis: Immediate post-contrast 3D GRE T1-weighted imaging versus unenhanced MR venography and conventional MR sequences

ObjectivePrimary aim is to compare the diagnostic value of contrast-enhanced 3D GRE T1-weighted sequences with unenhanced MR venography and conventional magnetic resonance imaging (MRI), in detection of dural venous sinus (DVS) and cortical venous thrombosis; secondary aim is to determine the relationship between DVS thrombosis/site and gender, age, infarction or hemorrhage. MethodsWe retrospectively reviewed conventional MR images, unenhanced MR venography and immediate post-contrast 3D GRE T1-weighted MR images in 30 patients (17 male and 13 female, 21–70 years old, mean age 40.1) with clinically suspected DVS thrombosis. MR examinations had been performed with 1.5T or 3T MR Scanners. DVSs were evaluated in 10 sub-segments, including cortical veins. Each set of MR images were examined separately, blinded to the final diagnosis. Associated findings were also noted and sensitivity, specificity and accuracy of each MRI technique were calculated. ResultsFinal diagnosis of cortical venous and/or dural sinus thrombosis was established in 24 (80%) of 30 cases and 67 (22.3%) out of 300 segments. For detection of the thrombotic segment, sensitivity, specificity, and accuracy were 83.6%, 95.3%, and 92.7% by conventional MR sequences, 89.6%, 91.8%, and 91.3% by unenhanced MR venography, and 92.5%, 100%, and 98.3% by contrast-enhanced 3D GRE T1-weighted sequence, respectively. Infarction and hemorrhage were more frequent in cases with cortical venous thrombosis, while gender and age had no significant relation with DVS thrombosis or its site. Conventional MR sequences and unenhanced MR venography were helpful due to additional information they provided in some cases with isolated cortical venous thrombosis, with hyperintense thrombus material and with associated hemorrhage or infarction. ConclusionContrast-enhanced 3D GRE T1-weighted MRI is the most accurate imaging method for the detection of DVS and/or cortical venous thrombosis. Infarction and hemorrhage were more frequent in cases with cortical venous thrombosis.

Accuracy of Postcontrast 3D Turbo Spin-Echo MR Sequence for the Detection of Enhanced Inflammatory Lesions in Patients with Multiple Sclerosis

Therapeutic strategies for patients with MS partly rely on contrast-enhanced MR imaging. Our aim was to assess the diagnostic performance of 3D turbo spin-echo MR imaging with variable refocusing flip angles at 3T for the detection of enhanced inflammatory lesions in patients with multiple sclerosis. Fifty-six patients with MS were prospectively investigated by using postcontrast T1-weighted axial 2D spin-echo and 3D TSE MR images. The order in which both sequences were performed was randomized. Axial reformats from 3D T1 TSE were generated to match the 2D spin-echo images. The reference standard was defined by using clinical data and all MR images available. Three separate sets of MR images (2D spin-echo images, axial reformats, and multiplanar images from 3D TSE sequences) were examined in a blinded fashion by 2 neuroradiologists separately for the detection of enhanced MS lesions. Image artifacts and contrast were evaluated. No artifacts related to vascular pulsation were observed on 3D TSE images, whereas image artifacts were demonstrated on 2D spin-echo images in 41 patients. One hundred twelve enhanced MS lesions were identified in 19 patients. Sixty-four lesions were correctly diagnosed by using 2D spin-echo images; 90, by using 3D TSE axial reformatted views; and 106, by using multiplanar analysis of the 3D TSE sequence. Multiplanar analysis was 94.7% sensitive and 100% specific for the diagnosis of patients with at least 1 enhanced lesion. Contrast of enhanced MS lesions was significantly improved by using the 3D TSE sequence (P < .011). The 3D TSE sequence with multiplanar analysis is a useful tool for the detection of enhanced MS lesions.

The circumventricular organs of the brain: conspicuity on clinical 3T MRI and a review of functional anatomy

The circumventricular organs (CVOs) occupy seven midline locations around the ventricles. They contain specialized ependymal cells called tanycytes and have an incomplete blood-brain barrier (BBB). We hypothesized that appearances of the lesser known CVOs on contrast-enhanced MRI might lead to confusion in image interpretation whereby they might be mistaken for pathology-related abnormal contrast enhancement. We therefore assessed the normal appearances and prevalence of contrast enhancement of the CVOs on routine clinical brain MRI and reviewed the functional anatomy of the CVOs. We retrospectively reviewed sagittal and coronal pre- and post-contrast T1-weighted brain 3T MR images in 100 adult patients with normal findings. We assessed the presence of the median eminence (ME), neurohypophysis (NH), pineal gland (PG), subforniceal organ (SFO), organum vasculosum of the lamina terminalis (OVLT), subcommissural organ (SCO), and the area postrema (AP). The frequency of contrast enhancement of the seven CVOs was as follows: ME in 100%, NH in 96%, PG in 84%, SFO in 1%, OVLT in 34%, SCO in 0%, and AP in 2%. The main CVOs (ME, NH, and PG) are well known and appreciated on brain imaging. However, there is a little awareness of the minor CVOs among neuroimagers. This is the first study of contrast enhancement prevalence of the SF, OV, SC, and AP on brain MRI. All the latter are small, faint, rarely visualized, and therefore not likely to cause misinterpretation with significant sources of pathology that cause breakdown of the BBB, such as tumor or inflammation.

Quantitative Diffusion-Weighted and Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion MR Imaging Analysis of T2 Hypointense Lesion Components in Pediatric Diffuse Intrinsic Pontine Glioma

Focal anaplasia characterized by T2 hypointensity, signal-intensity enhancement on postcontrast T1-weighted MR imaging and restricted water diffusion has been reported in a patient with juvenile pilocytic astrocytoma. We identified T2(HOF) with these MR imaging characteristics in children with DIPG and hypothesized that these represent areas of focal anaplasia; and may, therefore, have increased perfusion properties and should be characterized by increased perfusion. Thus, we used DSC to investigate our hypothesis. We retrospectively reviewed the baseline MR imaging scans of 86 patients (49 girls, 37 boys; median age, 6.1 years; range, 1.1-17.6 years) treated for DIPG at our hospital (2004-2009). T2(HOF) with the described MR imaging characteristics was identified in 10 patients. We used a region of interest-based approach to compare the ADC, FA, rCBV, rCBF, and rMTT of T2(HOF) with those of the typical T2(HRT). The ADC of T2(HOF) with the specified MR imaging characteristics was significantly lower than that of T2(HRT) (range, 0.71-1.95 μm(2)/ms versus 1.36-2.13 μm(2)/ms; P < .01); and the FA (range, 0.12-0.34 versus 0.07-0.24; P = .03) and rCBV (range, 0.4-2.62 versus 0.23-1.57; P = .01) values of T2(HOF)s were significantly higher. Our data suggest that T2(HOF) in DIPG may represent areas of focal anaplasia and underline the importance of regional, rather than global, tumor-field analysis. T2(HOF) may be the ideal target when stereotactic biopsy of tumors that present with an inhomogeneous T2 signal intensity is considered.

Comparison of volumetric methods for tumor measurements on two and three dimensional MRI in adult glioblastoma

Change in tumor size is a frequent endpoint in cancer clinical trials, but whether change in size should be measured using volume on two-dimensional (2D) or three-dimensional (3D) images is not certain. We compared volumetric measurements on post-contrast 2D and high-resolution 3D T1-weighted MR images (T1WI) in evaluating tumor response in glioblastoma multiforme (GBM). Tumor volume measurements were performed on 86 MRI studies from 37 adult patients with GBM on post-contrast 5mm 2D T1WI and isotropic high-resolution T1WI. The means of the two volumes were compared and their association was analyzed. There is no significant difference between volumes measured on 2D and 3D in 86 scans (Z = 0.63, p = 0.53), and a high correlation was revealed between them (r = 0.95, 95% CI: 0.93-0.97, p < 0.001). When the percentage changes were categorized into traditional tumor response criteria (complete response/partial response/stable disease/progressive disease), the kappa coefficient between the volume on 2D and volume on 3D was 0.80 (95% CI: 0.57-1.03, p < 0.05) with an overall agreement of 84%. Volume on post-contrast 2D T1WI appears comparable to volume on 3D T1WI and should be a practical alternative to volume on 3D in evaluating tumor response.

Lumbar vertebral chordoma arising from an intraosseous benign notochordal cell tumour: radiological findings and histopathological description with a good clinical outcome

Benign notochordal cell tumours have recently been described as intraosseous benign lesions of notochordal cell origin. The lesions are found in vertebral bodies in 20% of autopsy studies and are a potential precursor of chordoma. We report a rare case of lumbar vertebral chordoma that was thought to arise from a benign intraosseous notochordal cell tumour and which showed significant osteosclerotic change. Radiologically, the lumbar vertebral mass lesion showed hyperintensity on T2 weighted images, with scanty enhancement on post-contrast T1 weighted MR images. High uptake corresponding to the mass was noted on fluorine-18-fluorodeoxyglucose positron emission tomography. Bone biopsy revealed proliferation of the physaliphorous cells between thickened bone trabeculae; no nuclear mitosis was observed. Although the mass was diagnosed clinically as spinal chordoma, histopathology contained both benign notochordal cell tumour and conventional chordoma. After heavy particle (11C)-charged radiation therapy was applied to the lesion with a sufficient radiation field margin, the tumour volume significantly decreased and there was improvement in the patient's symptoms. On follow-up radiological studies, the tumour had markedly regressed and there was no tumour regrowth or distant metastasis. In this case report, benign notochordal cell tumour and conventional chordoma are histopathologically identified in the L1 vertebral body, which contains osteosclerotic and osteolytic areas. It is suggested that the benign notochordal cell tumour coexists with a conventional chordoma and that this histopathological finding supports a hypothetical relationship between benign notochordal cell tumour and chordoma.

Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1‐weighted MR images

To test the hypothesis that texture analysis of postcontrast T1-weighted MR images will predict hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) with better accuracy than visual evidence of contrast-enhancement (VE). Thirty-four AIS patients were examined within 3.5 +/- 1.5 h after stroke. T1-weighted MR images were acquired 19 +/- 7 min postcontrast injection. HT was determined by follow-up imaging at 24-72 h. Postcontrast images were evaluated for VE. Four second-order textural features were extracted (f1, f2, f3, and f9) for each patient. Receiver operating characteristic (ROC) curves were constructed for VE and for textural features, with HT as the outcome measure. The f2 for HT patients (n = 12) was significantly lower than in non-HT patients (1058 +/- 356 versus 1568 +/- 527; P = 0.005); the converse was true for f3 (0.67 +/- 0.12 versus 0.54 +/- 0.13; P = 0.007). ROC analysis indicated that the f2 and f3 textural features were the only two significant predictors of HT (P = 0.0018 and P = 0.0042). The addition of VE to either f2 or f3 did not result in a significant improvement in accuracy. Texture analysis of postcontrast T1-weighted images may be superior to visual evidence of enhancement for the prediction of HT.

Intra-arterial delivery of triolein emulsion increases vascular permeability in skeletal muscles of rabbits

BackgroundTo test the hypothesis that triolein emulsion will increase vascular permeability of skeletal muscle.MethodsTriolein emulsion was infused into the superficial femoral artery in rabbits (triolein group, n = 12). As a control, saline was infused (saline group, n = 18). Pre- and post-contrast T1-weighted MR images were obtained two hours after infusion. The MR images were qualitatively and quantitatively evaluated by assessing the contrast enhancement of the ipsilateral muscles. Histologic examination was performed in all rabbits.ResultsThe ipsilateral muscles of the rabbits in the triolein group showed contrast enhancement, as opposed to in the ipsilateral muscles of the rabbits in the saline group. The contrast enhancement of the lesions was statistically significant (p < 0.001). Histologic findings showed that most examination areas of the triolein and saline groups had a normal appearance.ConclusionRabbit thigh muscle revealed significantly increased vascular permeability with triolein emulsion; this was clearly demonstrated on the postcontrast MR images.