RÉSUMÉ
Objective:To assess the radiation dose and clinical value of "one-stop" whole-brain CT perfusion (CTP) imaging in the evaluation of collateral circulation for patients with acute ischemic stroke (AIS), regarding the digital subtraction angiography (DSA) as the reference.Methods:This retrospective study included 32 AIS patients, for whom both CTP and DSA were obtained <24 h since onset. All CTP scans were acquired in whole-brain volume perfusion mode using a 320-row CT with the phase-specific settings of tube currents to optimize the image quality of CTA images, where multiple-phase (mp) CTA images were extracted from the CTP data in post-processing. The volume CT dose index (CTDI vol), dose length product (DLP), and effective dose were compared to those reported in previous studies. The perfusion parameters of the infarct lesions and their contralateral regions were compared using the paired t-tests. One radiologist scored the collateral circulation with only the CTP and with the CTP plus mp-CTA using a 5-point scale. Another radiologist performed the same evaluation on the DSA. The diagnostic accuracy was calculated referring to the result based on DSA. The scores were analyzed using the Pearson correlation coefficient. The agreement of scores was quantified with the Kappa test. Results:The mean CTDI vol was 184.18 mGy, which was comparable to the result of a previous study (184.19 mGy), and the mean effective dose was reduced 39% compared to that reported in the literature for combined CTP and CTA scanning (6.1 vs 10 mSv). There were statistically significant differences in cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), transit time to peak (TTP), and time-to-maximum (Tmax) between the infarct lesions and their contralateral regions ( P<0.01). The scores between CTP and DSA were significantly correlated ( r=0.95, P<0.01), as well as the scores between CTP plus mp-CTA and DSA ( r=0.98, P<0.01). The Kappa value was 0.64 ( t=7.53, P<0.01) between CTP and DSA, while it increased to 0.88 ( t=9.99, P<0.01) for CTP plus mp-CTA. With the result of DSA as a reference, the diagnostic accuracy was 71.9% and 90.6% for CTP and CTP plus mp-CTA, respectively. Conclusions:The "one-stop" whole-brain CTP imaging with phase-specific settings of tube currents can provide reliable CTP and multiple-phase CTA images simultaneously, which could reasonably reduce the radiation dose. Combined use of multi-phase CTA and CT perfusion improves the diagnostic accuracy of collateral circulation in AIS patients.
RÉSUMÉ
Objective:To construct and validate prognostic model for breast cancer based on metabolic pathway-related genes.Methods:Gene expression data and clinical information of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) website. Then all metabolic pathway-related genes were extracted from the Gene Set Enrichment Analysis (GSEA) website for differential analysis to obtain differentially expressed genes between tumor and normal tissues, and then differential metabolic genes associated with prognosis for constructing a prognostic risk score were screened by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Patients were divided into high-risk group and low-risk group based on the median risk scores, and the efficacy of the prognostic model was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. The nomogram was constructed by combining this model with other clinical factors to predict the survival rate of breast cancer patients. Finally, the model was validated using the Gene Expression Omnibus (GEO) database.Results:A total of six metabolism-related genes ( NT5 E, PAICS, PFKL, PLA2 G2 D, QPRT and SHMT2) were finally screened by univariate Cox and LASSO regression for prognosis model. The prognostic risk score was an independent risk factor for breast cancer in both the training set and validating set, and the results of the Kaplan-Meier survival analysis suggested that the overall survival of patients in the high-risk group was significantly lower than that in the low-risk group, the difference was statistically significant ( P<0.001). The results of the ROC curve indicated that the nomogram model had higher predictive accuracy than other clinicopathological features, with an area under the curve value of 0.794 for both. Calibration curve showed good agreement between predicted and actual values. Based on GSEA, it was determined that the model could reveal metabolic features while monitoring the status of the tumor microenvironment (TME). Conclusion:The metabolism-related gene prognostic model constructed in this study may serve as a promising independent prognostic marker for breast cancer patients and may indicate the status of TME.