Combined texture analysis of dynamic contrast-enhanced MRI with histogram analysis of diffusion kurtosis imaging for predicting IDH mutational status in gliomas.

BACKGROUND: Non-invasive detection of isocitrate dehydrogenase (IDH) mutational status in gliomas is clinically meaningful for molecular stratification of glioma; however, it remains challenging. PURPOSE: To investigate the usefulness of texture analysis (TA) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and histogram analysis of diffusion kurtosis imaging (DKI) maps for evaluating IDH mutational status in gliomas. MATERIAL AND METHODS: This retrospective study enrolled 84 patients with histologically confirmed gliomas, comprising IDH-mutant (n = 34) and IDH-wildtype (n = 50). TA was performed for the quantitative parameters derived by DCE-MRI. Histogram analysis was performed for the quantitative parameters derived by DKI. Unpaired Student's t-test was used to identify IDH-mutant and IDH-wildtype gliomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to compare the diagnostic performance of each parameter and their combination for predicting the IDH mutational status in gliomas. RESULTS: Significant statistical differences in the TA of DCE-MRI and histogram analysis of DKI were observed between IDH-mutant and IDH-wildtype gliomas (all P < 0.05). Using multivariable logistic regression, the entropy of Ktrans, skewness of Ve, and Kapp-90th had higher prediction potential for IDH mutations with areas under the ROC curve (AUC) of 0.915, 0.735, and 0.830, respectively. A combination of these analyses for the identification of IDH mutation improved the AUC to 0.978, with a sensitivity and specificity of 94.1% and 96.0%, respectively, which was higher than the single analysis (P < 0.05). CONCLUSION: Integrating the TA of DCE-MRI and histogram analysis of DKI may help to predict the IDH mutational status.

Diffusion-relaxation correlation spectrum imaging for predicting tumor consistency and gross total resection in patients with pituitary adenomas: a preliminary study.

OBJECTIVE: To evaluate the ability of diffusion-relaxation correlation spectrum imaging (DR-CSI) to predict the consistency and extent of resection (EOR) of pituitary adenomas (PAs). METHODS: Forty-four patients with PAs were prospectively enrolled. Tumor consistency was evaluated at surgery as either soft or hard, followed by histological assessment. In vivo DR-CSI was performed and spectra were segmented following to a peak-based strategy into four compartments, designated A (low ADC), B (mediate ADC, short T2), C (mediate ADC, long T2), and D (high ADC). The corresponding volume fractions ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) along with the ADC and T2 values were calculated and assessed using univariable analysis for discrimination between hard and soft PAs. Predictors of EOR > 95% were analyzed using logistic regression model and receiver-operating-characteristic analysis. RESULTS: Tumor consistency was classified as soft (n = 28) or hard (n = 16). Hard PAs presented higher [Formula: see text] (p = 0.001) and lower [Formula: see text] (p = 0.013) than soft PAs, while no significant difference was found in other parameters. [Formula: see text] significantly correlated with the level of collagen content (r = 0.448, p = 0.002). Knosp grade (odds ratio [OR], 0.299; 95% confidence interval [CI], 0.124-0.716; p = 0.007) and [Formula: see text] (OR, 0.834, per 1% increase; 95% CI, 0.731-0.951; p = 0.007) were independently associated with EOR > 95%. A prediction model based on these variables yielded an AUC of 0.934 (sensitivity, 90.9%; specificity, 90.9%), outperforming the Knosp grade alone (AUC, 0.785; p < 0.05). CONCLUSION: DR-CSI may serve as a promising tool to predict the consistency and EOR of PAs. CLINICAL RELEVANCE STATEMENT: DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs and may serve as a promising tool to predict the tumor consistency and extent of resection in patients with PAs. KEY POINTS: • DR-CSI provides an imaging dimension for characterizing tissue microstructure of PAs by visualizing the volume fraction and corresponding spatial distribution of four compartments ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]). • [Formula: see text] correlated with the level of collagen content and may be the best DR-CSI parameter for discrimination between hard and soft PAs. • The combination of Knosp grade and [Formula: see text] achieved an AUC of 0.934 for predicting the total or near-total resection, outperforming the Knosp grade alone (AUC, 0.785).

Temporal changes in plaque characteristics after treatment and their relationship with stroke recurrence: a quantitative study using magnetic resonance imaging.

Background: Intracranial atherosclerotic disease (ICAD) is the major cause of ischemic stroke. Despite aggressive medical therapy, around 15% of patients with ICAD experience recurrence. The aim of the present study was to evaluate the temporal changes in intracranial arteriosclerotic plaques after medical treatment based on vessel wall magnetic resonance imaging (VWMRI) and to explore their relationship with stroke recurrence. Methods: A total of 67 symptomatic patients with ICAD who underwent initial and follow-up VWMRI were recruited into this retrospective cohort study. Stroke recurrence was defined as an ipsilateral stroke symptom after the initial attack. The clinical characteristics and plaque features, including stenosis ratio (measured based on luminal diameter or area), plaque thickness, plaque burden (PB), enhancement ratio (ER), and enhancement grade, were evaluated and compared between the initial and follow-up examinations. Changes in plaque characteristics were compared between patients with or without recurrence by univariable analyses. Multivariable regression was performed to investigate imaging markers for recurrent stroke. Results: The median interval between baseline and follow-up VWMRI was 334 days. A total of 13 cases (19.4%) experienced a stroke recurrence. After treatment, significant decreases in the stenosis ratio (area), PB, and ER were observed in cases without recurrence (all P<0.05), while no significant difference in plaque features was found for cases with recurrence. Univariable analyses showed that changes in stenosis ratio (area), plaque thickness, PB, and ER were significantly different between patients with and without recurrence (all P<0.05). Multivariable regression indicated that PB change was the only significant marker associated with stroke recurrence [odds ratio (OR) =1.112 per 1% increase, 95% confidence interval (CI): 1.010 to 1.224, P=0.031]. Conclusions: Patients with arteriosclerotic plaques who benefit from medical treatment show obvious decreases in stenosis (area), PB, and ER. The progression of PB may serve as an independent marker for predicting stroke recurrence.

High-resolution compressed sensing time-of-flight MR angiography outperforms CT angiography for evaluating patients with Moyamoya disease after surgical revascularization.

BACKGROUND: To evaluate the utility of high-resolution compressed sensing time-of-fight MR angiography (CS TOF-MRA) for assessing patients with moyamoya disease (MMD) after surgical revascularization, by comparison with computer tomography angiography (CTA). METHODS: Twenty patients with MMD after surgical revascularizations who underwent CS TOF-MRA and CTA were collected. The scan time of CS TOF-MRA was 5 min and 4 s, with a reconstructed resolution of 0.4 × 0.4 × 0.4 mm3. Visualization of superficial temporal artery and middle cerebral artery (STA-MCA) bypass, neovascularization into the brain pial surface and Moyamoya vessels (MMVs) were independently ranked by two neuroradiologists on CS TOF-MRA and CTA, respectively. The patency of anastomosis was assessed as patent or occluded, using digital subtraction angiography and expert's consensus as ground truth. Interobserver agreement was calculated using the weighted kappa statistic. Wilcoxon signed-rank or Chi-square test was performed to investigate diagnostic difference between CS TOF-MRA and CTA. RESULTS: Twenty-two hemispheres from 20 patients were analyzed. The inter-reader agreement for evaluating STA-MCA bypass, neovascularization and anastomosis patency was good to excellent (κCS TOF-MRA, 0.738-1.000; κCTA, 0.743-0.909). The STA-MCA bypass and MMVs were better visualized on CS TOF-MRA than CTA (both P < 0.05). CS TOF-MRA had a higher sensitivity than CTA (94.7% vs. 73.7%) for visualizing anastomoses. Neovascularization was better observed in 13 (59.1%) sides on CS TOF-MRA, in comparison to 7 (31.8%) sides on CTA images (P = 0.005). CONCLUSION: High-resolution CS TOF-MRA outperforms CTA for visualization of STA-MCA bypass, neovascularization and MMVs within a clinically reasonable time in MMD patients after revascularization.

Texture Analysis of High b-Value Diffusion-Weighted Imaging for Evaluating Consistency of Pituitary Macroadenomas.

BACKGROUND: Preoperative evaluation of the consistency of pituitary macroadenomas is important for neurosurgeons to prepare the surgical plan. PURPOSE: To evaluate the diagnostic performance of texture analysis (TA) of diffusion-weighted imaging (DWI) at a standard b-value (b = 1000 s/mm2 ) and a high b-value (b = 2000 s/mm2 ) for their ability to assess the tumor consistency of pituitary macroadenomas. STUDY TYPE: Retrospective. POPULATION/SUBJECTS: Fifty patients with histologically confirmed pituitary macroadenomas were classified as soft (n = 37) or hard (n = 13) types. FIELD STRENGTH/SEQUENCE: Coronal T2 -weighted imaging (T2 WI), Readout Segmentation of Long Variable Echo-trains (RESOLVE) DWI at b = 1000 s/mm2 and b = 2000 s/mm2 were acquired with 3.0T MRI. ASSESSMENT: The corresponding apparent diffusion coefficient (ADC) maps (ADC1000 and ADC2000 ) were registered to T2 WI. Regions of interest (ROIs) were manually drawn along the solid part of the tumor from the coregistered T2 WI-ADC images. The texture parameters from T2 WI, ADC1000 , and ADC2000 were acquired. STATISTICAL TESTS: The texture parameters were compared between the two types by using unpaired Student's t-test. Receiver operating characteristic (ROC) curves and logistic regression analyses were used to assess their diagnostic performance. RESULTS: Significant differences in TA parameters of ADC1000 and ADC2000 were observed between soft and hard types (P < 0.05 for all), whereas the TA of T2 WI resulted in no significant difference (P > 0.05 for all). TA of ADC2000 provided a superior diagnostic performance compared with that of ADC1000 (P = 0.038). A combination of mean value and entropy of ADC2000 yielded an AUC, a sensitivity, and a specificity of 0.911, 78.4% and 92.3%, respectively. DATA CONCLUSION: TA of ADC values were useful for assessing the tumor consistency of pituitary macroadenomas. ADC2000 may facilitate better type discrimination. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1507-1513.

Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading.

BACKGROUND: The application of conventional magnetic resonance imaging (MRI) in glioma grading is limited and non-specific. PURPOSE: To investigate the application values of MRI, texture analysis (TA) of dynamic contrast-enhanced MRI (DCE-MRI) and intratumoral susceptibility signal (ITSS) on susceptibility weighted imaging (SWI), alone and in combination, for glioma grading. MATERIAL AND METHODS: Fifty-two patients with pathologically confirmed gliomas who underwent DCE-MRI and SWI were enrolled in this retrospective study. Conventional MRIs were evaluated by the VASARI scoring system. TA of DCE-MRI-derived parameters and the degree of ITSS were compared between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). The diagnostic ability of each parameter and their combination for glioma grading were analyzed. RESULTS: Significant statistical differences in VASARI features were observed between LGGs and HGGs ( P < 0.05), of which the enhancement quality had the highest area under the curve (AUC) (0.873) with 93.3% sensitivity and 80% specificity. The TA of DCE-MRI derived parameters were significantly different between LGGs and HGGs ( P < 0.05), of which the uniformity of Ktrans had the highest AUC (0.917) with 93.3% sensitivity and 90% specificity. The degree of ITSS was significantly different between LGGs and HGGs ( P < 0.001). The AUC of the ITSS was 0.925 with 93.3% sensitivity and 90% specificity. The best discriminative power was obtained from a combination of enhancement quality, Ktrans- uniformity, and ITSS, resulting in 96.7% sensitivity, 100.0% specificity, and AUC of 0.993. CONCLUSION: Combining conventional MRI, TA of DCE-MRI, and ITSS on SWI may help to improve the differentiation between LGGs and HGGs.

Sixty-Two-Year-Old Male Suffering From Uremic Leontiasis Ossea Caused by Severe Secondary Hyperparathyroidism.

Secondary hyperparathyroidism (SHPT) is a long-term complication of chronic kidney disease-mineral and bone disorder (CKD-MBD). SHPT is characterized by hyperplasia of the parathyroid glands and abnormal secretion of parathyroid hormones (PTH), calcium and phosphorous metabolic disorders, renal osteodystrophy, vascular and soft tissue calcification, malnutrition, and other multiple system complications, which can seriously affect the quality of life of the patient and increase the risk of cardiovascular disease and mortality rate. Uremic leontiasis ossea (ULO) is a medical condition only rarely encountered clinically. SHPT causes craniofacial bone deformity accompanied by lesions of the nerve, cardiovascular, respiratory, bone, or other systems within the body. The case discussed here is related to severe SHPT. A 62-year-old male patient was suffering from leontiasis ossea, pectus excavatum, vascular calcification, spontaneous bone fractures, and lower limb deformities. He was undergoing hemodialysis and given total parathyroidectomy (TPTX) with autotransplantation (AT). We further analyzed the multivariate therapeutic effects of TPTX on this patient in order to provide clinical data for standardized treatment of individuals with CKD-MBD.

MRI of plaque characteristics and relationship with downstream perfusion and cerebral infarction in patients with symptomatic middle cerebral artery stenosis.

BACKGROUND: Intracranial plaque characteristics are associated with stroke events. Differences in plaque features may explain the disconnect between stenosis severity and the presence of ischemic stroke. PURPOSE: To investigate the relationship between plaque characteristics and downstream perfusion changes, and their contribution to the occurrence of cerebral infarction beyond luminal stenosis. STUDY TYPE: Case control. SUBJECTS: Forty-six patients with symptomatic middle cerebral artery (MCA) stenosis (with acute cerebral infarction, n = 30; without acute cerebral infarction, n = 16). FIELD STRENGTH/SEQUENCE: 3.0T with 3D turbo spin echo sequence (3D-SPACE). ASSESSMENT: Luminal stenosis grade, plaque features including lesion T2 and T1 hyperintense components, plaque enhancement grade, and plaque distribution were assessed. Brain perfusion was evaluated on mean transient time maps based on the Alberta Stroke Program Early CT score (MTT-ASPECTS). STATISTICAL TESTS: Plaque features, grade of luminal stenosis, and MTT-ASPECTS were compared between two groups. The association between plaque features and MTT-ASPECTS were assessed using Spearman's correlation analysis. Multivariate logistic regression and receiver operating characteristic (ROC) curves were constructed to assess the effect of significant variables alone and their combination in determining the occurrence of cerebral infarction. RESULTS: Stronger enhanced plaques were associated with downstream lower MTT-ASPECTS (P = 0.010). Plaque enhancement grade (P = 0.039, odds ratio [OR] 5.9, 95% confidence interval [CI] 1.1-32) and MTT-ASPECTS (P = 0.003, OR 2.6, 95% CI 1.4-4.7) were associated with a recent cerebral infarction, whereas luminal stenosis grade was not (P = 0.128). The combination of MTT-ASPECTS and plaque enhancement grade provided incremental information beyond luminal stenosis grade alone. The area under the receiver operating characteristic curve (AUC) improved from 0.535 to 0.921 (P < 0.05). DATA CONCUSION: Strongly enhanced plaques are associated with a higher likelihood of downstream perfusion impairment. Plaque enhancement and perfusion evaluation may play a complementary role to luminal stenosis in determining the occurrence of acute cerebral infarction. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.

Quantitative Evaluation of Diffusion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Differentiation Between Primary Central Nervous System Lymphoma and Glioblastoma.

OBJECTIVE: This study aimed to evaluate the utility of diffusion and permeability parameters derived from diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for differentiating primary central nervous system lymphoma (PCNSL) and glioblastoma multiforme (GBM) and to assess the correlation among these parameters. MATERIALS AND METHODS: Forty-two patients with GBM and 18 patients with PCNSL underwent conventional 3.0-T MRI, diffusion-weighted imaging, and DCE-MRI before surgery. Normalized apparent diffusion coefficient ratio (rADC) and DCE-MRI-derived parameters (the volume transfer constant [K], the flux rate constant, the volume fraction of extravascular extracellular space [Ve], and the fractional plasma volume) were measured within the entire enhancing tumor and compared between the 2 groups. The diagnostic ability of each parameter and their optimal combination for differentiating between PCNSL and GBM, and the correlation among these parameters, were statistically analyzed. RESULTS: The PCNSLs demonstrated significantly lower rADC (P = 0.000), higher K (P = 0.000), and higher Ve (P = 0.001) than GBMs. With the combination of rADC and K, the diagnostic ability for discriminating between PCNSL and GBM was significantly improved (area under the receiver operating characteristic curve [AUC] = 0.930) as compared with rADC (AUC = 0.858) and K (AUC = 0.852) alone (P < 0.001 for both). The rADC did not correlate with K or Ve derived from DCE-MRI. CONCLUSIONS: Apparent diffusion coefficient ratio, K, and Ve are useful parameters for differentiating between PCNSL and GBM. The combination of rADC and K helps to improve the diagnostic accuracy. The rADC may not show correlation with K or Ve.

An automatic glioma grading method based on multi-feature extraction and fusion.

BACKGROUND: An accurate assessment of tumor malignancy grade in the preoperative situation is important for clinical management. However, the manual grading of gliomas from MRIs is both a tiresome and time consuming task for radiologists. Thus, it is a priority to design an automatic and effective computer-aided diagnosis (CAD) tool to assist radiologists in grading gliomas. OBJECTIVE: To design an automatic computer-aided diagnosis for grading gliomas using multi-sequence magnetic resonance imaging. METHODS: The proposed method consists of two steps: (1) the features of high and low grade gliomas are extracted from multi-sequence magnetic resonance images, and (2) then, a KNN classifier is trained to grade the gliomas. In the feature extraction step, the intensity, volume, and local binary patterns (LBP) of the gliomas are extracted, and PCA is used to reduce the data dimension. RESULTS: The proposed "Intensity-Volume-LBP-PCA-KNN" method is validated on the MICCAI 2015 BraTS challenge dataset, and an average grade accuracy of 87.59% is obtained. CONCLUSIONS: The proposed method is an effective method for automatically grading gliomas and can be applied to real situations.

Orbital benign and malignant lymphoproliferative disorders: Differentiation using semi-quantitative and quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging.

OBJECTIVES: To assess the value of dynamic contrast-enhanced MR imaging (DCE-MRI) in differentiating benign from malignant orbital lymphoproliferative disorders (OLPDs). METHODS: Thirty-nine patients with orbital lymphoproliferative disorders (21 malignant and 18 benign) underwent DCE-MRI scan for pre-treatment evaluation from March 2013 to December 2015. Both semi-quantitative (TTP, AUC, Slopemax) and quantitative (Ktrans, kep, ve) parameters were calculated, and compared between two groups. Receiver operating characteristic (ROC) curve analyses were used to determine the diagnostic value of each significant parameter. RESULTS: Malignant OLPDs showed significantly higher kep, lower ve, and lower AUC than benign OLPDs, while no significant differences were found on Ktrans, TTP and Slopemax. ROC analyses indicated that ve exhibited the best diagnostic performance in predicting malignant OLPDs (cutoff value, 0.211; area under the curve, 0.896; sensitivity, 76.2%; specificity, 94.9%), followed by kep (cutoff value, 0.853; area under the curve, 0.839; sensitivity, 85.7%; specificity, 89.9%). CONCLUSION: DCE-MRI and specially its derived quantitative parameters of kep and ve are promising metrics for differentiating malignant from benign OLPDs.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge.

In this paper, we propose an automatic brain tumor segmentation method based on Deep Belief Networks (DBNs) and pathological knowledge. The proposed method is targeted against gliomas (both low and high grade) obtained in multi-sequence magnetic resonance images (MRIs). Firstly, a novel deep architecture is proposed to combine the multi-sequences intensities feature extraction with classification to get the classification probabilities of each voxel. Then, graph cut based optimization is executed on the classification probabilities to strengthen the spatial relationships of voxels. At last, pathological knowledge of gliomas is applied to remove some false positives. Our method was validated in the Brain Tumor Segmentation Challenge 2012 and 2013 databases (BRATS 2012, 2013). The performance of segmentation results demonstrates our proposal providing a competitive solution with stateof- the-art methods.

Radiological indeterminate vestibular schwannoma and meningioma in cerebellopontine angle area: differentiating using whole-tumor histogram analysis of apparent diffusion coefficient.

PURPOSE: To assess the role of whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating radiological indeterminate vestibular schwannoma (VS) from meningioma in cerebellopontine angle (CPA). MATERIALS AND METHODS: Diffusion-weighted (DW) images (b = 0 and 1000 s/mm2) of pathologically confirmed and radiological indeterminate CPA meningioma (CPAM) (n = 27) and VS (n = 12) were retrospectively collected and processed with mono-exponential model. Whole-tumor regions of interest were drawn on all slices of the ADC maps to obtain histogram parameters, including the mean ADC (ADCmean), median ADC (ADCmedian), 10th/25th/75th/90th percentile ADC (ADC10, ADC25, ADC75 and ADC90), skewness and kurtosis. The differences of ADC histogram parameters between CPAM and VS were compared using unpaired t-test. Multiple receiver operating characteristic (ROC) curves analysis was used to determine and compare the diagnostic value of each significant parameter. RESULTS: Significant differences were found on the ADCmean, ADCmedian, ADC10, ADC25, ADC75 and ADC90 between CPAM and VS (all p values < 0.001), while no significant difference was found on kurtosis (p = 0.562) and skewness (p = 0.047). ROC curves analysis revealed, a cut-off value of 1.126 × 10-3 mm2/s for the ADC90 value generated highest area under curves (AUC) for differentiating CPAM from VS (AUC, 0.975; sensitivity, 100%; specificity, 88.9%). CONCLUSIONS: Histogram analysis of ADC maps based on whole tumor can be a useful tool for differentiating radiological indeterminate CPAM from VS. The ADC90 value was the most promising parameter for differentiating these two entities.

Effects of regions of interest methods on apparent coefficient measurement of the parotid gland in early Sjögren's syndrome at 3T MRI.

BACKGROUND: The apparent diffusion coefficient (ADC) has been used to assess parotid gland abnormalities in Sjögren's syndrome (SS) patients; however, few data exist on the influence of region of interest (ROI) methods on ADC measurements. PURPOSE: To assess the influence of ROI methods on ADC measurement, and their diagnostic ability in detecting parotid gland abnormalities in early SS patients. MATERIAL AND METHODS: Thirteen early SS patients underwent parotid gland diffusion-weighted imaging scans at a 3.0 T MR unit. Two readers independently measured the parotid gland ADC value using three different ROIs (whole-gland [WG], single-slice [SS], and reader-based circular [RBC]). The ADC value based on three different ROIs (ADC-ROIWG, ADC-ROISS, ADC-ROIRBC) were compared between the SS group and a matched healthy control (HC) group (n = 19). Receiver operating characteristic (ROC) curves and intra-class correlation coefficients (ICC) were used to determine the diagnostic ability and reproducibility of the parameters. RESULTS: The ADC-ROIWG, ADC-ROISS, and ADC-ROIRBC in the SS group were all significantly higher than those in HC group (all P < 0.05). The ADC-ROIWG showed better diagnostic ability than did ADC-ROIRBC (P = 0.0200), while no significant difference was found between ADC-ROIWG and ADC-ROISS (P = 0.4636). The ROIWG method showed the best inter- and intra-reader agreement (ICC, 0.902 and 0.928, respectively), followed by ROISS and ROIRBC. CONCLUSION: The ROI methods can influence the parotid gland ADC measurements and their diagnostic ability. Considering our results, we suggest using in clinical practice single-slice ROIs to measure the ADC of the parotid gland.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis.

Utility of dynamic contrast-enhanced magnetic resonance imaging for differentiating glioblastoma, primary central nervous system lymphoma and brain metastatic tumor.

PURPOSE: The study aimed to investigate the use of dynamic contrast-enhanced magnetic resonance imaging (MRI)-derived permeability parameters for the differentiation of glioblastoma multiformes (GBMs), primary central nervous system lymphomas (PCNSLs), and brain metastatic tumors (MTs). MATERIALS AND METHODS: Seventy-five patients with histopathologically confirmed GBMs (n=38), PCNSLs (n=16) and MTs (n=21) underwent dynamic contrast-enhanced MRIs before surgery. The volume transfer constant K(trans), the flux rate constant between extravascular extracellular space and plasma Kep, the extravascular extracellular volume Ve and the fractional plasma volume Vp were measured within the entire contrast-enhancing tumor by extended Tofts model. A one-way analysis of variance was used to compare all of the parameters among these three tumors, followed by the post-hoc test. Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of the permeability parameters. RESULTS: Mean K(trans) value and Ve value were significantly higher in PCNSLs than in GBMs (P<0.001 and P=0.011) and MTs (P<0.001 and P<0.001). No significant difference was observed in all of the permeability parameters between GBMs and MTs. According to the receiver operating characteristic analyses, both K(trans) and Ve had good diagnostic performance for discriminating between PCNSLs and GBMs (the area under the curve: 0.847 and 0.785, respectively), as well as between PCNSLs and MTs (the area under the curve: 0.851 and 0.884, respectively). CONCLUSIONS: The K(trans) and Ve derived from dynamic contrast-enhanced MRI facilitate the differentiation of PCNSLs from GBMs and MTs.

Diagnosis and stage of Graves' ophthalmopathy: Efficacy of quantitative measurements of the lacrimal gland based on 3-T magnetic resonance imaging.

The aim of the present study was to determine the efficacy of quantitative measurements of the lacrimal gland based on 3-T magnetic resonance (MR) imaging in the diagnosis and staging of Graves' ophthalmopathy (GO). The study retrospectively enrolled 33 patients with GO (the GO group) and 24 healthy volunteers [the healthy control (HC) group] with orbit MR imaging performed using a 3-T MR scanner. Quantitative parameters of the lacrimal gland, including axial length, axial width, axial area, coronal length, coronal width, coronal area, volume and signal intensity ratio (SIR) of the lacrimal gland to the ipsilateral temporal muscle were measured. The difference of quantitative parameters between the GO and HC groups, or between active and inactive GO groups were evaluated using a Student's t-test. Receiver operating characteristic analyses were used to evaluate the diagnostic value of the significant parameters in discriminating patients with GO from healthy controls, or discriminating active from inactive GO. All the quantitative measurements of the GO patients were significantly larger than those of the healthy controls (P<0.05), with the exception of the coronal length (P=0.150). Axial and coronal width had the best efficacy in discriminating patients with GO from healthy controls. Only SIR was found to be different between the active and inactive GO groups (P=0.001). Setting a SIR of 2.57 as the threshold value, the optimal efficacy was achieved (area under the curve, 0.711; sensitivity, 57.7%; specificity, 77.5%) in discriminating between active and inactive GO. Quantitative measurements of the lacrimal gland based on 3-T MR imaging may assist in the diagnosis and stage of GO.

Potential Utility of Visually AcceSAble Rembrandt Images Assessment in Brain Astrocytoma Grading.

OBJECTIVE: The aim of this study was to evaluate the predictive value of multivariate factors of Visually AcceSAble Rembrandt Images (VASARI) in brain astrocytoma grading. METHODS: Presurgical magnetic resonance images of 126 patients with brain astrocytomas (World Health Organization grade 2, n = 38; grade 3, n = 36; grade 4, n = 52) were rated by 2 neuroradiologists for tumor size, location, and tumor morphology by using a standardized imaging feature set VASARI. RESULTS: Significant differences were noted in 12 factors of VASARI including enhancement quality, enhancing proportion, noncontrast enhancing tumor proportion, necrosis proportion, edema proportion, hemorrhage, thickness of enhancing margin, definition of the enhancing margin, pial and ependymal invasion, enhanced tumor crossing midline, and satellites between brain astrocytoma grades (grades 1-IV, P < 0.05). On multivariate regression analysis, enhancement quality was an independent diagnostic factor for high-grade brain astrocytoma, whereas edema proportion was an independent diagnostic factor in differentiating grade 2 and grade 3. Noncontrast enhancing tumor proportion was a predictive factor in the diagnosis of grade 4 astrocytoma. Receiver operating characteristic analysis illustrates edema proportion score higher than 2 with sensitivity of 86.1% in differentiating grade 2 and grade 3 astrocytoma. Noncontrast enhancing tumor proportion scores 4 or lower has high sensitivity (92.3%) but moderate specificity (50.0%) in differentiating grade 3 and grade 4 astrocytoma. CONCLUSIONS: Our data illustrate that magnetic resonance features of VASARI especially enhancement quality, edema proportion, and noncontrast enhancing tumor proportion provided precise and detailed information of astrocytoma grading and suggested that prediction of astrocytoma grading is based on VASARI as an adjunct to biopsy.

Utility of histogram analysis of ADC maps for differentiating orbital tumors.

PURPOSE: We aimed to evaluate the role of histogram analysis of apparent diffusion coefficient (ADC) maps for differentiating benign and malignant orbital tumors. METHODS: Fifty-two patients with orbital tumors were enrolled from March 2013 to November 2014. Pretreatment diffusion-weighted imaging was performed on a 3T magnetic resonance scanner with b factors of 0 and 800 s/mm2, and the corresponding ADC maps were generated. Whole-tumor regions of interest were drawn on all slices of the ADC maps to obtain histogram parameters, including ADCmean, ADCmedian, standard deviation (SD), skewness, kurtosis, quartile, ADC10, ADC25, ADC75, and ADC90. Histogram parameter differences between benign and malignant orbital tumors were compared. The diagnostic value of each significant parameter in predicting malignant tumors was established. RESULTS: Age, ADCmean, ADCmedian, quartile, kurtosis, ADC10, ADC25, ADC75, and ADC90 parameters were significantly different between benign and malignant orbital tumor groups, while gender, location, SD, and skewness were not significantly different. The best diagnostic performance in predicting malignant orbital tumors was achieved at the threshold of ADC10=0.990 (AUC, 0.997; sensitivity, 96.2%; specificity, 100%). CONCLUSION: Histogram analysis of ADC maps holds promise for differentiating benign and malignant orbital tumors. ADC10 has the potential to be the most significant parameter for predicting malignant orbital tumors.

Physiological Ischemic Training Promotes Brain Collateral Formation and Improves Functions in Patients with Acute Cerebral Infarction.

OBJECTIVES: To observe the effectiveness and mechanisms of physiological ischemic training (PIT) on brain cerebral collateral formation and functional recovery in patients with acute cerebral infarction. METHODS: 20 eligible patients with acute cerebral infarction were randomly assigned to either PIT group (n = 10) or Control group (n = 10). Both groups received 4 weeks of routine rehabilitation therapy, while an additional session of PIT, which consisted of 10 times of maximal voluntary isometric handgrip for 1 min followed by 1 min rest, was prescribed for patients in the PIT groups. Each patient was trained with four sections a day and 5 days a week for 4 weeks. The Fugl-Meyer Assessment (FMA), the Modified Barthel Index (MBI), and the short-form 36-item health survey questionnaire (SF-36) were applied for the evaluation of motor impairment, activity of daily living, and quality of life at the baseline and endpoint. MRI was applied to detect the collateral formation in the brain. The concentration of vascular endothelial growth factor (VEGF) and endothelial progenitor cells (EPCs) number in plasma were also tested at the endpoint. RESULTS: Demographic data were consistent between experimental groups. At the endpoint, the scores of the FMA, MBI, and SF-36 were significantly higher than that at baseline. As compared to the Control group, the score of FMA and SF-36 in PIT group was significantly higher, while no significant difference was detected between groups in terms of MBI. Both groups had significantly higher cerebral blood flow (CBF) level at endpoint as compared to that at baseline. Moreover, the CBF level was even higher in the PIT group as compared to that in the Control group after 4 weeks of training. The same situations were also found in the plasma VEGF and EPCs assessment. In addition, positive correlations were found between FMA score and CBF level (r = 0.686, p < 0.01), CBF level and VEGF concentration (r = 0.675, p < 0.01), and VEGF concentration and EPC number (r = 0.722, p < 0.01). CONCLUSION: PIT may be effective in increasing the expression of VEGF and recruitment of EPCs and in turn promote the formation of brain collateral circulation. The positive correlations may demonstrate a potential association between biological and functional parameters, and PIT may be able to improve the motor function, activity of daily living, and quality of life in patients with stroke.