***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Introduction Perfusion magnetic resonance imaging ( MRI ) can be used in the pre-operative assessment of brain tumours . The aim of this prospect i ve study was to identify the perfusion parameters from dynamic contrast-enhanced ( DCE ) and dynamic susceptibility contrast ( DSC ) perfusion imaging that could best discriminate between grade II and III gliomas . Methods MRI ( 3 T ) including morphological ( ( T2 fluid attenuated inversion recovery ( FLAIR ) and T1-weighted (T1W)+Gd ) ) and perfusion ( DCE and DSC ) sequences was performed in 39 patients with newly diagnosed suspected low- grade glioma after written informed consent in this review board-approved study . Regions of interests ( ROIs ) in tumour area were delineated on FLAIR images co-registered to DCE and DSC , respectively , in 25 patients with histopathological grade II ( n = 18 ) and III ( n = 7 ) gliomas . Statistical analysis of differences between grade II and grade III gliomas in histogram perfusion parameters was performed , and the areas under the curves ( AUC ) from the ROC analyses were evaluated . Results In DCE , the skewness of transfer constant ( ktrans ) was found superior for differentiating grade II from grade III in all gliomas ( AUC 0.76 ) . In DSC , the st and ard deviation of relative cerebral blood flow ( rCBF ) was found superior for differentiating grade II from grade III gliomas ( AUC 0.80 ) . Conclusions Histogram parameters from ktrans ( DCE ) and rCBF ( DSC ) could most efficiently discriminate between grade II and grade III gliomas Background Although conventional MR imaging ( MRI ) is the most widely used non-invasive technique for brain tumor grading , its accuracy has been reported to be relatively low . Advanced MR techniques , such as perfusion-weighted imaging ( PWI ) , diffusion-weighted imaging ( DWI ) , and magnetic resonance spectroscopy ( MRS ) , could predict neoplastic histology , but their added value over conventional MRI is still open to debate . Methods We prospect ively analyzed 129 patients diagnosed with primary brain tumors ( 118 gliomas ) classified as low- grade in 30 cases and high- grade in 99 cases . Results Significant differences were obtained in high- grade tumors for conventional MRI variables ( necrosis , enhancement , edema , hemorrhage , and neovascularization ) ; high relative cerebral blood volume values ( rCBV ) , low relative apparent diffusion coefficients ( rADC ) , high ratio of N-acetyl-aspartate/creatine at short echo time ( TE ) and high choline/creatine at long TE . Among conventional MRI variables , the presence of enhancement and necrosis were demonstrated to be the best predictors of high grade in primary brain tumors ( sensitivity 95.9 % ; specificity 70 % ) . The best results in primary brain tumors were obtained for enhancement , necrosis , and rADC ( sensitivity 98.9 % ; specificity 75.9 % ) . Necrosis and enhancement were the only predictors of high grade in gliomas ( sensitivity 97.6 % ; specificity 76 % ) when all the magnetic resonance variables were combined . Conclusions MRI is highly accurate in the assessment of tumor grade . The combination of conventional MRI features with advanced MR variables showed only improved tumor grading by adding rADC to conventional MRI variables in primary brain tumors BACKGROUND AND PURPOSE : The prognostic value of dynamic contrast-enhanced MR imaging – derived plasma volume obtained in tumor and the contrast transfer coefficient has not been well-established in patients with gliomas . We determined whether plasma volume and contrast transfer coefficient in tumor correlated with survival in patients with gliomas in addition to other factors such as age , type of surgery , preoperative Karnofsky score , contrast enhancement , and histopathologic grade . MATERIAL S AND METHODS : This prospect i ve study included 46 patients with a new pathologically confirmed diagnosis of glioma . The contrast transfer coefficient and plasma volume obtained in tumor maps were calculated directly from the signal-intensity curve without T1 measurements , and values were obtained from multiple small ROIs placed within tumors . Survival curve analysis was performed by dichotomizing patients into groups of high and low contrast transfer coefficient and plasma volume . Univariate analysis was performed by using dynamic contrast-enhanced parameters and clinical factors . Factors that were significant on univariate analysis were entered into multivariate analysis . RESULTS : For all patients with gliomas , survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor ( P < .05 ) . In subgroups of high- and low- grade gliomas , survival was worse for groups of patients with high contrast transfer coefficient and plasma volume obtained in tumor ( P < .05 ) . Univariate analysis showed that factors associated with lower survival were age older than 50 years , low Karnofsky score , biopsy-only versus resection , marked contrast enhancement versus no/mild enhancement , high contrast transfer coefficient , and high plasma volume obtained in tumor ( P < .05 ) . In multivariate analysis , a low Karnofsky score , biopsy versus resection in combination with marked contrast enhancement , and a high contrast transfer coefficient were associated with lower survival rates ( P < .05 ) . CONCLUSIONS : In patients with glioma , those with a high contrast transfer coefficient have lower survival than those with low parameters OBJECTIVE We have investigated the performance of magnetic resonance ( MR ) perfusion imaging to differentiate between astrocytomas grade II , grade III and glioblastomas in a prospect i ve study . MATERIAL S AND METHODS In 33 patients with suspected supratentorial primary cerebral tumors we performed multi-section Echo Planar MR perfusion imaging .  Regional cerebral blood volume ( rCBV ) maps were calculated and the maximum rCBV was determined from the entire lesion . This value was divided by the mean rCBV value from the contralateral side , which provided the rCBV index used in this study . The rCBV index was correlated with the histological tumor classification after stereotactic biopsy ( n=7 ) or open resection ( n=26 ) . RESULTS The maximum rCBV index was 1.2+/-0.8 for grade II astrocytomas ( n=3 ) , 4.0+/-1.2 for grade III astrocytomas ( n=13 ) , and 10.3+/-3.3 for glioblastomas ( n=17 ) . The difference between grade III astrocytomas and glioblastomas was highly significant ( P<0.001 ) . DISCUSSION AND CONCLUSION The rCBV index measured with multi-section Echo Planar MR perfusion is capable of differentiating grade III astrocytomas from glioblastomas . It serves as an additional parameter to establish a diagnosis in cases where it is not possible to clearly differentiate between these types of tumors on the basis of conventional MR imaging . MR perfusion imaging also provides information about spatial heterogeneities within a tumor which might improve diagnostic performance . This technology may also be of interest for follow-up examinations after histological diagnosis and further treatment BACKGROUND AND PURPOSE : Contrast leakage results in underestimation of the CBV of brain tumors . Our aim was to compare the diagnostic performance of DSC perfusion MR imaging without and with mathematic contrast-leakage correction in differentiating PCNSLs and glioblastomas . MATERIAL S AND METHODS : Perfusion parameters — CBV , corrected CBV , and leakage coefficient — were measured in enhancing tumor portions and contralateral NAWM of 15 PCNSLs and 20 glioblastomas , respectively . The ratios of CBV and corrected CBV were calculated by dividing the tumor values by those obtained from contralateral NAWM . A paired t test was used to compare tumor K2 and NAWM K2 , as well as tumor CBV ratios without and with leakage correction . Comparisons of CBV , corrected CBV , and K2 between PCNSLs and glioblastomas were done by using a 2- sample t test . The diagnostic performance of DSC perfusion MR imaging without and with contrast-leakage correction was assessed with receiver operating characteristic curve analysis . RESULTS : PCNSLs and glioblastomas demonstrated higher K2 than those in their contralateral NAWM . Corrected CBV ratios were significantly higher than the uncorrected ones for both tumors . PCNSLs had lower CBV ratios ( P < .001 ) , lower corrected CBV ratios ( P < .001 ) , and higher K2 ( P = .001 ) compared with glioblastomas . In differentiating between PCNSLs and glioblastomas , the area under the curve of the CBV ratio , corrected CBV ratio , and K2 were 0.984 , 0.940 , and 0.788 , respectively . CONCLUSIONS : PCNSL can be differentiated from glioblastoma with CBV ratios , corrected CBV ratios , and K2 . CBV without contrast-leakage correction seems to have the best diagnostic performance in differentiating the 2 tumors Introduction To investigate the application value of diffusion-weighted imaging ( DWI ) , the difference of apparent diffusion coefficient ( ADCdifference ) value calculated from ADCdifference map was used , in evaluating the pathologic grade of astrocytic tumors . Methods 33 patients with histopathologically proven supratentorial astrocytic tumors were included in this prospect i ve study . All of them received  conventional magnetic resonance imaging ( MRI ) , DWI with diffusion factor of 0 and 50 s/mm2 and of 0 and 3,000 s/mm2 , and perfusion-weighted imaging ( PWI ) examinations . Pseudo-color ADCdifference maps were obtained by means of using ADC map with low b value ( 0 and 50 s/mm2 ) minus ADC map with high b value ( 0 and 3,000 s/mm2 ) . Results The highest ADCdifference value of grade s I – II , grade III , and grade IV was ( 0.91 ± 0.07 ) × 10−3 , ( 1.81 ± 0.38 ) × 10−3 , and ( 2.36 ± 0.32 ) × 10−3 mm2/s , respectively , and there was statistical difference among them ( p < 0.001 ) . The highest ADCdifference value between low- grade ( grade s I – II ) and high- grade ( grade s III – IV ) astrocytic tumors showed statistical difference as well ( p < 0.001 ) . The highest ADCdifference value of astrocytic tumors correlated positively with the pathologic grade of tumor ( r = 0.853 , p < 0.001 ) . Positive correlation was found between the highest ADCdifference value and maximum relative cerebral blood volume ( rCBV ) value ( r = 0.829 , p < 0.001 ) in high- grade astrocytic tumors ; however , the highest ADCdifference value and maximum rCBV value had no significant correlation in low- grade astrocytic tumors ( r = 0.259 , p = 0.536 ) . Conclusion Quantitative analysis of highest ADCdifference value of supratentorial astrocytic tumors may provide valuable information of tumor microcirculation and perfusion , thus allowing a promising new method for preoperatively assessing the pathologic grade of tumor Introduction It is not always possible to differentiate infective from neoplastic brain lesions with conventional MR imaging . In this study , we assessed the utility of various perfusion indices in the differentiation of infective from neoplastic brain lesions . Methods A total of 103 patients with infective brain lesions ( group I , n=26 ) and neoplastic brain lesions ( high- grade glioma , HGG , group II , n=52 ; low- grade glioma , LGG , group III , n=25 ) underwent dynamic contrast-enhanced MR imaging .  The perfusion indices , including relative cerebral blood volume ( rCBV ) , relative cerebral blood flow ( rCBF ) , transfer coefficient ( ktrans ) and leakage ( ve ) , were calculated and their degree of correlation with immunohistologically obtained microvessel density ( MVD ) and vascular endothelial growth factor ( VEGF ) determined . The rCBV was corrected for the leakage effect . Discriminant analysis for rCBV , rCBF , ktrans and ve was performed to predict the group membership of each case and post hoc analysis was performed to look for group differences . Results The rCBV , rCBF , ktrans , ve , MVD and VEGF were significantly different ( P<0.001 ) between the three groups . Discriminant analysis showed that rCBV predicted 73.1 % of the infective lesions , 84.6 % of the HGG and 72.0 % of the LGG . The rCBF classified 86.5 % of the HGG , 80.0 % of the LGG and 65.4 % of the infective lesions . The ktrans discriminated 98.1 % of the HGG , 76.0 % of the LGG and 88.5 % of the infective lesions correctly . The ve classified 98.1 % of the HGG , 76.0 % of the LGG and 84.6 % the infective lesions . The rCBV was correlated significantly with MVD and VEGF , while the correlation between ktrans and MVD was not significant . Conclusion Physiological perfusion indices such as ktrans and ve appear to be useful in differentiating infective from neoplastic brain lesions . Adding these indices to the current imaging protocol is likely to improve tissue characterization of these focal brain mass lesions Context With the advent of new imaging modalities , radiologists are faced with h and ling increasing volumes of data for diagnosis and treatment planning . The use of automated and intelligent systems is becoming essential in such a scenario . Machine learning , a branch of artificial intelligence , is increasingly being used in medical image analysis applications such as image segmentation , registration and computer 
    Output:
    
LGG had significantly lower rCBV , Cho/Cr , and Cho/NAA values than HGG or MTS .
No significant differences were found for nADC .
Conclusions Best differentiation between HGG and LGG is obtained from rCBV , Cho/Cr , and Cho/NAA metrics .
MTS could not be reliably distinguished from HGG by the methods investigated