Purpose:To evaluate functional alterations of renal ischemia and reperfusion injury using MR diffusion-weighted imaging and dynamic perfusion imaging. Materials and Methods:Twelve dogs were randomly divided into four groups. Animal renal ischemia was respectively induced for 30 (group 1), 60 (group 2), 90 (group 3), and 120 (group 4) minutes by left renal artery ligation under anesthesia. Using a 1.5 T MR system, true-FISP, TSE, EPI, and DWI sequences were acquired in five different periods; specifically, pre-ischemia, onset-ischemia, late ischemia, onset-reperfusion, and post-reperfusion. Moreover, a turbo-FLASH sequence (TR/TE/TI/FA ϭ 5.8/3.2/ 400 msec/10°) with a temporal resolution of 1.16 seconds was acquired. Signal intensity (SI) was measured in the cortex, outer medulla, and inner medulla of kidney. Apparent diffusion coefficient (ADC) values were calculated, and SI was plotted as a function of time. Results:In all animals, significant SI changes of the left kidney on T2/T2*WI were detected following ischemia-reperfusion insult compared to corresponding values of the right kidney. Following ligation, the ADC values decreased in all layers of the left kidney. Immediately after the release of ligation, ADC values in both outer and inner medulla of the left kidney remained lower than those of the right kidney in those animals which were induced with renal ischemia for 60, 90, and 120 minutes. In all groups, a uniphasic enhancement pattern was observed in the outer and inner medulla of the left kidney, accompanied by a decrease of the area under the curve. Conclusion:Our results suggest that MR diffusionweighted imaging and dynamic perfusion imaging are useful in identifying renal dysfunction following normothermic ischemia and reperfusion injury.
ObjectivesTo evaluate the predictive value of radiomics features based on multiparameter magnetic resonance imaging (MP-MRI) for peritoneal carcinomatosis (PC) in patients with ovarian cancer (OC).MethodsA total of 86 patients with epithelial OC were included in this retrospective study. All patients underwent FS-T2WI, DWI, and DCE-MRI scans, followed by total hysterectomy plus omentectomy. Quantitative imaging features were extracted from preoperative FS-T2WI, DWI, and DCE-MRI images, and feature screening was performed using a minimum redundancy maximum correlation (mRMR) and least absolute shrinkage selection operator (LASSO) methods. Four radiomics models were constructed based on three MRI sequences. Then, combined with radiomics characteristics and clinicopathological risk factors, a multi-factor Logistic regression method was used to construct a radiomics nomogram, and the performance of the radiomics nomogram was evaluated by receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis.ResultsThe radiomics model from the MP-MRI combined sequence showed a higher area under the curve (AUC) than the model from FS-T2WI, DWI, and DCE-MRI alone (0.846 vs. 0.762, 0.830, 0.807, respectively). The radiomics nomogram (AUC=0.902) constructed by combining radiomics characteristics and clinicopathological risk factors showed a better diagnostic effect than the clinical model (AUC=0.858) and the radiomics model (AUC=0.846). The decision curve analysis shows that the radiomics nomogram has good clinical application value, and the calibration curve also proves that it has good stability.ConclusionRadiomics nomogram based on MP-MRI combined sequence showed good predictive accuracy for PC in patients with OC. This tool can be used to identify peritoneal carcinomatosis in OC patients before surgery.
Background Genotype status of glioma have important significance to clinical treatment and prognosis. At present, there are few studies on the prediction of multiple genotype status in glioma by method of multi-sequence radiomics. The purpose of the study is to compare the performance of clinical features (age, sex, WHO grade, MRI morphological features etc.), radiomics features from multi MR sequence (T2WI, T1WI, DWI, ADC, CE-MRI (contrast enhancement)), and a combined multiple features model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q of glioma. Methods In this retrospective analysis, 81 glioma patients confirmed by histology were enrolled in this study. Five MRI sequences were used for radiomic feature extraction. Finally, 107 features were extracted from each sequence on Pyradiomics software, separately. These included 18 first-order metrics, such as the mean, standard deviation, skewness, and kurtosis etc., 14 shape features and second-order metrics including 24 grey level run length matrix (GLCM), 16 grey level run length matrix (GLRLM), 16 grey level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix (NGTDM), and 14 grey level dependence matrix (GLDM). Then, Univariate analysis and LASSO (Least absolute shrinkage and selection operator regression model were used to data dimension reduction, feature selection, and radiomics signature building. Significant features (p < 0.05 by multivariate logistic regression were retained to establish clinical model, T1WI model, T2WI model, T1 + C (T1WI contrast enhancement model, DWI model and ADC model, multi sequence model. Clinical features were combined with multi sequence model to establish a combined model. The predictive performance was validated by receiver operating characteristic curve (ROC analysis and decision curve analysis (DCA). Results The combined model showed the better performance in some groups of genotype status among some models (IDH AUC = 0.93, MGMT AUC = 0.88, TERT AUC = 0.76). Multi sequence model performed better than single sequence model in IDH, MGMT, TERT. There was no significant difference among the models in predicting 1p/19q status. Decision curve analysis showed combined model has higher clinical benefit than multi sequence model. Conclusion Multi sequence model is an effective method to identify the genotype status of cerebral glioma. Combined with clinical models can better distinguish genotype status of glioma. Key Points The combined model showed the higher performance compare with other models in predicting genotype status of IDH, MGMT, TERT. Multi sequence model showed a better predictive model than that of a single sequence model. Compared with other models, the combined model and multi sequence model show no advantage in prediction of 1p/19q status.
Objective The objective of this study was to evaluate the role and limit of iodine maps by dual-energy computed tomography (CT) single scan for pancreatic cancer. Methods Thirty patients with suspected solitary pancreatic cancer were enrolled in this study and underwent CT perfusion and iodine maps. The parameters of pancreatic cancer and normal pancreatic tissue were calculated. Pearson correlation and paired t test were used for evaluating 2 techniques. Results Iodine concentration had a moderate positive correlation with blood flow or blood volume (P < 0.05 for both). All values of iodine concentration and blood flow, iodine concentration, and blood volume had significant positive correlations (P < 0.001 for both). The mean effective dose for CT perfusion and iodine maps had significant difference (8.61 ± 0.00 mSv vs 1.13 ± 0.14 mSv, P < 0.001). Conclusions Iodine maps had the potential to replace routine CT perfusion for pancreatic cancer with low radiation dose.
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