).q RSNA, 2015 Purpose:To quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas. Materials andMethods:This study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusionweighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations. Results:ADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P , .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P , .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P , .05). Conclusion:Water molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.q RSNA, 2015

BackgroundIdentification of pregnancies with postpartum haemorrhage (PPH) antenatally rather than intrapartum would aid delivery planning, facilitate transfusion requirements and decrease maternal complications. MRI has been increasingly used for placenta evaluation. Here, we aim to build a nomogram incorporating both clinical and radiomic features of placenta to predict the risk for PPH in pregnancies during caesarian delivery (CD).MethodsA total of 298 pregnant women were retrospectively enrolled from Henan Provincial People's Hospital (training cohort: n = 207) and from The Third Affiliated Hospital of Zhengzhou University (external validation cohort: n = 91). These women were suspected with placenta accreta spectrum (PAS) disorders and underwent MRI for placenta evaluation. All of them underwent CD and were singleton. PPH was defined as more than 1000 mL estimated blood loss (EBL) during CD. Radiomic features were selected based on their correlations with EBL. Radiomic, clinical, radiological, clinicoradiological and clinicoradiomic models were built to predict the risk of PPH for each patient. The model with the best prediction performance was validated with its discrimination ability, calibration curve and clinical application.FindingsThirty-five radiomic features showed strong correlation with EBL. The clinicoradiomic model resulted in the best discrimination ability for risk prediction of PPH, with AUC of 0.888 (95% CI, 0.844–0.933) and 0.832 (95% CI, 0.746–0.913), sensitivity of 91.2% (95% CI, 85.8%-96.7%) and 97.6% (95% CI, 92.7%-100%) in the training and validation cohort respectively. For patients with severe PPH (EBL more than 2000 mL), 53 out of 55 pregnancies (96.4%) in the training cohort and 18 out of 18 (100%) pregnancies in the validation cohort were identified by the clinicoradiomic model. The model performed better in patients without placenta previa (PP) than in patients with PP, with AUC of 0.983 compared with 0.867, sensitivity of 100% compared with 90.8% in the training cohort, AUC of 0.832 compared with 0.815, sensitivity of 97.6% compared with 97.2% in the validation cohort.InterpretationThe clinicoradiomic model incorporating both prenatal clinical factors and radiomic signature of placenta on T2WI showed good performance for risk prediction of PPH. The predictive model can identify severe PPH with high sensitivity and can be applied in patients with and without PP.

Using noninvasive magnetic resonance imaging techniques to accurately evaluate the grading and cellularity of gliomas is beneficial for improving the patient outcomes. Amide proton transfer imaging is a noninvasive molecular magnetic resonance imaging technique based on chemical exchange saturation transfer mechanism that detects endogenous mobile proteins and peptides in biological tissues. Between August 2012 and November 2015, a total number of 44 patients with pathologically proven gliomas were included in this study. We compared the capability of amide proton transfer magnetic resonance imaging with that of noninvasive diffusion-weighted imaging and noninvasive 3-dimensional pseudo-continuous arterial spin imaging in evaluating the grading and cellularity of gliomas. Our results reveal that amide proton transfer magnetic resonance imaging is a superior imaging technique to diffusion-weighted imaging and 3-dimensional pseudo-continuous arterial spin imaging in the grading of gliomas. In addition, our results showed that the Ki-67 index correlated better with the amide proton transfer-weighted signal intensity than with the apparent diffusion coefficient value or the cerebral blood flow value in the gliomas. Amide proton transfer magnetic resonance imaging is a promising method for predicting the grading and cellularity of gliomas.

Background/Aims: To investigate whether diffusion tensor imaging (DTI) is more sensitive than conventional MRI at detecting cognitive deterioration in patients with subcortical ischemic vascular disease (SIVD). Methods: Forty-two SIVD patients had a diagnosis of no cognitive impairment (NCI), vascular cognitive impairment/no dementia or vascular dementia (VaD). Whole-brain DTI histography and routine MRI were performed on these participants. Results: There were significant differences between cognitively impaired patients and NCI subjects in mean diffusivity and fractional anisotropy in either whole-brain white matter (WBWM) or in normal-appearing white matter (NAWM). All DTI indices within either WBWM or NAWM were found to be significantly correlated with both the attention-executive and memory measures in SIVD subjects. Lacune numbers and T₂-weighted lesions correlated only with attention-executive measures, whereas hippocampal volumes correlated only weakly with memory measures. Whole-brain gray matter volumes correlated with Z scores for all cognitive domains but language. After VaD patients had been excluded from the analysis, cognitive measures remained significantly correlated with some of the DTI indices, but not with conventional MRI findings. Conclusions: Compared with conventional MRI, whole-brain DTI is a more reliable and sensitive technique for the early detection of cognitive impairment in SIVD patients.

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Background and Purpose: To assess the validity of the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) in the detection of vascular mild cognitive impairment (VaMCI) in patients with subcortical ischemic vascular disease (SIVD). Methods: Among 102 SIVD patients, both cutoff scores of the MMSE and MoCA for differentiating VaMCI from no cognitive impairment (NCI) or differentiating VaMCI from vascular dementia (VaD) were determined by the receiver operator characteristic (ROC) analysis. Optimal sensitivity with specificity of cutoff scores was obtained after the raw scores were adjusted for education. Results: After adjusting for education, the MoCA cutoff score for differentiating VaMCI from NCI was at 24/25 and that for differentiating VaMCI from VaD was at 18/19. After applying the adjusted MoCA scores from 19 to 24 to identify VaMCI in all SIVD patients, sensitivity was at 76.7% and specificity was at 81.4% (κ = 0.579). The adjusted cutoff score of the MMSE for differentiating VaMCI from NCI was at 28/29 and that for differentiating VaMCI from VaD was at 25/26. The sensitivity and specificity of the adjusted MMSE was at 58.1 and 71.2%, respectively, when using the score from 26 to 28 to identify VaMCI in SIVD patients (κ = 0.294). Conclusions: The MoCA detected subcortical VaMCI better than the MMSE.

Our results suggests that there were no survival benefit from non-curative gastrectomy for patients with single liver, distant nodes, or multiple sites metastasis. However, only patients with single peritoneal dissemination had survival benefit from non-curative resection. The value of non-curative resection should be evaluated by well-designed clinical trials.

The clinical manifestation of WD is very diverse and no one feature is completely reliable. Doctors in many fields have opportunities to encounter this disease, and the most important thing is to be aware of the possibility of WD.