With the improvement of MRI equipment performance and the usage of multi-channel high sensitivity coil, the application of the diffusion-weighted imaging (DWI) in the abdomen has been achieved. And the DWI has great significance in the diagnosis and differential diagnosis of renal tumors. In this article we reviewed advantages and limitations of magnetic resonance diffusion weighted imaging technology diagnosis in renal cell carcinoma, and also reviewed the latest research progress of DWI technology in the use of kidney.

Background and purpose: Since the detection of localized prostate cancer is increasing, it's important to distinguish from benign lesions like prostatitis. This study aimed to compare diffusion weighted imaging with apparent diffusion coefifcients in differential diagnosis of localized prostate cancer on 3.0T MR. Methods:Sixty-nine cases with localized prostate cancer proved by pathology, 43 in perpheral zone (PZ) and 26 in central gland (CG), 33 with prostatitis, and 37 with benign prostatic hyperplasia (BPH) were analyzed. The signal noise ratio (SNR) and apparent diffusion coefifcient (ADC) value of lesions were measured, and a semiquantitative grading of DW image was performed. The diagnostic accuracy of both methods was evaluated by ROC. Results:45 cancer foci and 36 prostatitis lesions in PZ, 27 cancer foci and 42 BPH lesions in CG were included. The sensitivity and speciifcity for ADC value to distinguish cancer from begin lesions in PZ and CG were 88.9%and 86.1%、81.5%and 73.8%respectively. The diagnostic accuracy of ADC value was higher than DWI semiquantitative grading and SNR (P<0.05). Conclusion:ADC value yielded a higher accuracy in differential diagnosis of localized prostate cancer on 3.0T MR, thus it’s recommended as a major index for diagnosis.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) enables non-invasive imaging characterization of tissue vascularity with small molecular weight gadolinium chelates. Depending on this technique, tissue blood perfusion, microvessel permeability and extracellular leakage space can be obtained. The basic principles of two dynamic MRI techniques (T2*W and T1W DCE-MRI) and their applications in prostate cancer of DCE-MRI including diagnosis, differential diagnosis, formulation of treatment plan, evaluation of therapeutic reaction, detection of lesion recurrent were reviewed in this article.

Objective To analyze MR imaging manifestations of spinal area lymphoma in order to improve the recognition and understanding of the disease. Methods A group of 45 patients with pathologically or clinically proven spinal area lymphoma were reviewed. Five cases were primary NHL,40 cases were secondary with 9 HL and 31 NHL (27 B-cell type NHL and 4 T-cell type NHL). MR Imaging findings were analyzed and correlated with clinical and pathologic findings. Results (1) Location of lesions: 13 cases were focal type and 32 cases were multifocal type. All of the 5 patients with primary lymphoma were focal type, while 32 of 40 cases of secondary lymphoma were multifocal type. (2) Type oflesions: ①Vertebral destruction: 27 cases manifested as bone destruction with 23 of them had soft tissuemass and the extent of soft tissue masses were larger than that of bone destruction in 18 cases.②Soft tissuemasses: 6 cases manifested as soft masses without obvious bone destruction, of which 5 cases had soft tissuemasses imbedded vertebrae and communicated paravertebral and epidural spaces through intervertebralforamen.③Bone marrow infiltration: 9 cases of secondary spinal lympboma had signal intensity changes ofbone marrow without obvious cortical bone destruction and soft tissue mass. ④ Spinal cord infiltration:3 cases of secondary spinal lymphoma had spinal cord swelling and signal intensity changes. (3) MRIfindings: all lesions of bone destruction and marrow infiltration manifested as hypointense on T1-weightedimages, hypointense, isointense or hyperintense on T2-weighted images and hyperintense on T2-weightedimages with fat-suppression technique. All soft tissue masses were homogeneous hypointense on T1-weightedimages and hyperintense on T2-weighted images. After intravenous injection of contrast media, the lesions ofthe bone and the soft tissue showed mild or moderate enhancement without remarkable cystic degenerationand necrosis. Conclusions Most of the spinal area lymphoma is the secondary B type NHL with complexMRI manifestation. Osteolytic lesion with contiguous paravertebral soft tissue mass imbedded vertebrae whichcommunicated paravertebral and epidural spaces through intervertebral foramen with a mild or moderateenhancement may suggest the diagnosis of this rare disease.

This article introduces the definition and sample size estimation of three special tests (namely, non-inferiority test, equivalence test and superiority test) for qualitative data with the design of one factor with two levels having a binary response variable. Non-inferiority test refers to the research design of which the objective is to verify that the efficacy of the experimental drug is not clinically inferior to that of the positive control drug. Equivalence test refers to the research design of which the objective is to verify that the experimental drug and the control drug have clinically equivalent efficacy. Superiority test refers to the research design of which the objective is to verify that the efficacy of the experimental drug is clinically superior to that of the control drug. By specific examples, this article introduces formulas of sample size estimation for the three special tests, and their SAS realization in detail.

Sample size estimation is necessary for any experimental or survey research. An appropriate estimation of sample size based on known information and statistical knowledge is of great significance. This article introduces methods of sample size estimation of difference test for data with the design of one factor with two levels, including sample size estimation formulas and realization based on the formulas and the POWER procedure of SAS software for quantitative data and qualitative data with the design of one factor with two levels. In addition, this article presents examples for analysis, which will play a leading role for researchers to implement the repetition principle during the research design phase.

ABSTRACT: Estimation of sample size and testing power is an important component of research design. This article introduced methods for sample size and testing power estimation of difference test for quantitative and qualitative data with the single-group design, the paired design or the crossover design. To be specific, this article introduced formulas for sample size and testing power estimation of difference test for quantitative and qualitative data with the above three designs, the realization based on the formulas and the POWER procedure of SAS software and elaborated it with examples, which will benefit researchers for implementing the repetition principle.

This article introduces the general concepts and methods of sample size estimation and testing power analysis. It focuses on parametric methods of sample size estimation, including sample size estimation of estimating the population mean and the population probability. It also provides estimation formulas and introduces how to realize sample size estimation manually and by SAS software.

This article introduces definitions of three special tests, namely, non-inferiority test (to verify that the efficacy of the experimental drug is clinically not inferior to that of the positive control drug), equivalence test (to verify that the efficacy of the experimental drug is equivalent to that of the control drug) and superiority test (to verify that the efficacy of the experimental drug is superior to that of the control drug), and methods of sample size estimation under the three different conditions. By specific examples, the article introduces formulas of sample size estimation for the three special tests, and their SAS realization in detail.

The design of one factor with k levels (k≥3) refers to the research that only involves one experimental factor with k levels (k≥3), and there is no arrangement for other important non-experimental factors. This paper introduces the estimation of sample size and testing power for quantitative data and qualitative data having a binary response variable with the design of one factor with k levels (k≥3).