Glioma characterized by high morbidity and mortality, is one of the most common brain tumors. The application of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) in differentiating glioma grading and IDH1 mutation status were poorly investigated. 78 glioma patients confirmed by pathological and imaging methods were enrolled. Glioma patients were measured using IVIM-DWI, then related parameters such as cerebral blood flow (CBF), perfusion fraction (f), pseudo diffusivity (D*), and true diffusivity (D), were derived. Receiver operating characteristic (ROC) curves were made to calculate specificity and sensitivity. The values of CBF1, CBF3, D*1, rCBF1-2, rCBF3-2, and age in group high-grade gliomas (HGG) were significantly higher than that of in group low-grade gliomas (LGG). The values of CBF1, CBF3, rCBF1-2, rCBF3-2, D*1, and age in group IDH1
mut
were significantly lower than that of in group IDH1
wt
. The levels of D1 and f1 were remarkably higher in the group IDH1
mut
than group IDH1
wt
. rCBF1-2 had a remarkably positive correlation with CBF1 (r=0.852, p<0.001). f1 showed a markedly negative correlation with CBF1 (r= -0.306, p=0.007). IVIM-DWI presented efficacy in differentiating glioma grading and IDH1 mutation status.
PurposeTo evaluate whether multiparametric magnetic resonance imaging (MRI)-based logistic regression models can facilitate the early prediction of chemoradiotherapy response in patients with residual brain gliomas after surgery.Patients and MethodsA total of 84 patients with residual gliomas after surgery from January 2015 to September 2020 who were treated with chemoradiotherapy were retrospectively enrolled and classified as treatment-sensitive or treatment-insensitive. These patients were divided into a training group (from institution 1, 57 patients) and a validation group (from institutions 2 and 3, 27 patients). All preoperative and postoperative MR images were obtained, including T1-weighted (T1-w), T2-weighted (T2-w), and contrast-enhanced T1-weighted (CET1-w) images. A total of 851 radiomics features were extracted from every imaging series. Feature selection was performed with univariate analysis or in combination with multivariate analysis. Then, four multivariable logistic regression models derived from T1-w, T2-w, CET1-w and Joint series (T1+T2+CET1-w) were constructed to predict the response of postoperative residual gliomas to chemoradiotherapy (sensitive or insensitive). These models were validated in the validation group. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were applied to compare the predictive performances of these models.ResultsFour models were created and showed the following areas under the ROC curves (AUCs) in the training and validation groups: Model-Joint series (AUC, 0.923 and 0.852), Model-T1 (AUC, 0.835 and 0.809), Model-T2 (AUC, 0.784 and 0.605), and Model-CET1 (AUC, 0.805 and 0.537). These results indicated that the Model-Joint series had the best performance in the validation group, followed by Model-T1, Model-T2 and finally Model-CET1. The calibration curves indicated good agreement between the Model-Joint series predictions and actual probabilities. Additionally, the DCA curves demonstrated that the Model-Joint series was clinically useful.ConclusionMultiparametric MRI-based radiomics models can potentially predict tumor response after chemoradiotherapy in patients with postoperative residual gliomas, which may aid clinical decision making, especially to help patients initially predicted to be treatment-insensitive avoid the toxicity of chemoradiotherapy.
Horseshoe kidney deformity with duplex urinary collecting systems is a rare congenital urinary tract defect. Clinically, it is very difficult to visually observe and examine the whole anatomic structure and information with the regular 2D diagnostic imaging tools. Here, we report a case in which a middle age patient has bilateral duplex urinary collecting systems and horseshoe kidney deformity accompanied with right renal ureteral calculi and hydronephrosis. It was diagnosed by magnetic resonance urography with urinary system 3D reconstruction. The imaging and display method provides valuable information about abnormal anatomic structures of the kidneys and the related stone diseases for preoperative planning.
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