Conventional MRI texture analysis of peritumoral edema in the differential diagnosis of glioblastoma and solitary metastatic brain tumor / 中华放射学杂志

Objective To investigate the value of conventional MR texture analysis of peritumoral edema in differentiating between glioblastoma and solitary metastatic brain tumor. Methods Forty patients with brain glioma and forty patients with brain metastases confirmed by pathology or clinic were enrolled . All patients underwent conventional MRI scan and enhanced examination, including axial T1WI, T2WI, fluid attenuated inversion recovery(FLAIR) and enhanced T1WI. The ROI was manually outlined by MaZda software in the most obvious level of 4 sequences of peritumoral edema and the texture features were extracted, including mean, variance, skewness, kurtosis, 1 percentile, 10 percentile, 50 percentile, 90 percentile, 99 percentile. Independent sample t-test (in case of normality and variance homogeneity test) or rank sum test (in case of not satisfying normality and homogeneity of variance) was used to compare the differences of the histogram parameters and to make meaningful parameters based on logistic regression modeling and drawing the receiver operating characteristic curve (ROC curve) to evaluate the differential diagnosis efficacy. Results Through the preliminary feature screening, skew of FLAIR (SkewFLAIR) for glioblastomas was -0.358±0.432, respectively,while for solitary metastatic brain tumor, the statistics was-0.648 ± 0.503. The results showed statistically significant difference(t=7.574,P=0.006). The differencesbetween the kurtosis (KurtFLAIR) were also proven to be statistically significant (Z=4.360 ,P=0.037),with-0.384 (-0.798, 0.049) for glioblastomas and-0.089 (-0.456, 0.613) for solitary metastasis brain tumor. The sensitivity of the corresponding logistic regression differential diagnosis model was 77.5％, specificity was 94.3％, and area under the curve (AUC) was 0.85. Conclusion Conventional MRI texture analysis of peritumoral edema provides reliable and quantified objective basis for the differential diagnosis of glioblastoma and solitary metastasis.

MR texture analysis in differential diagnosis of glioblastoma and solitary metastatic brain tumor / 中国医学影像技术

Objective To explore the value of MR texture analysis in differential diagnosis of glioblastoma and solitary metastasis.Methods Thirty-five patients with brain glioblastomas and forty patients with brain metastases confirmed by pathology or clinic were enrolled.All patients underwent conventional MR scan and enhanced examination,including T1 FLAIR,T2WI,T2 FLAIR,DWI,and enhanced T1WI.Texture features of the lesions were extracted with MaZda software by manually drawing ROI on the maximum level of tumor delineation in DWI and enhanced T1WI.Results There were statistical differences between glioblastoma and solitary metastasis of enhanced T1WI skewness in the histogram class texture parameters,gray co-occurrence matrix parameters including enhanced T1WI image correlation,mean and DWI correlation.Logistic regression analysis showed the area under ROC curve of the model was 0.834.Conclusion Texture analysis can provide reliable and quantified objective basis for differential diagnosis of glioblastoma and solitary metastatic brain tumor.

Visualization tool-supported problem-based learning in clinical diagnostic expertise develop-ment / 中华医学教育探索杂志

Objective In problem-based learning, students are often found difficult to con-struct medical knowledge systematically and transfer knowledge to solve new problems. In face of this challenge, this study aims to investigate the effect of visualization tool-supported online problem-based learning on medical students' clinical diagnostic expertise development. Methods A controlled study was conducted and 52 medical students were randomly assigned into experimental group (using visual-ization tool-supported online PBL environment for learning) and control group (using online PBL envi-ronment for learning without visualization tool support). Participants were asked to complete the diag-nostic analysis of three kidney problems according to the requirements of the learning environments and to provide feedback of online learning experience afterwards. Paired-sample t test and one-way analysis of vonriance were used to analyze both group's case 1 and case 3 on line learing scores. Results The results revealed that the experimental group had significant improvement in online learning performance [case 1: (1.47 ±0.54), case 3: (2.14 ±0.55), P=0.015], while the control group had no significant improvement [case 1:(1.57±0.67), case 3:(1.66±0.49), P=0.234]. Early performance of online learning and group factor had interative effects (F=7.266, P=0.013). Conclusions The findings suggest that visualization tool-supported online PBL environment can facilitate medical student clinical diagnostic expertise development effectively.