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.

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).

Background and purpose:With the widespread use of screening of prostate-specific antigen (PSA) levels, prostate cancers at organ-conifned stage are increasing in newly diagnosed cases. However, some defects remain in conventional monoexponential diffusion-weighted imaging (DWI) for differentiating organ-conifned prostate cancer from benign lesions. Therefore, the aim of this study was to obtain biexponential apparent diffusion parameters of prostate organ-conifned cancer, chronic prostatitis in peripheral zone (PZ) and normal PZ tissue, and to compare with monoexponential apparent diffusion coeffcient (ADC) for differentiating prostate cancer from prostatitis lesions. Methods:Sixteen patients with pathologically confirmed prostate organ-confined cancer in PZ, 14 with prostatitis underwent conventional (b-factors 0, 1 000 s/mm2) and 10b-factors (0-3 000 s/mm2) diffusion-weighted imaging (DWI).The monoexponential ADC value and biexponential parameters fast ADC (ADCf), fraction of ADCf (f), slow ADC (ADCs) value for prostate cancer, prostatitis and normal tissues were calculated and compared. Receiver operating characteristic analysis was performed for those parameters.Results:Biexponential and monoexponential parameters were obtained for 18 prostate cancers, 18 prostatitis and 37 normal PZ tissues. The ADC value of prostate cancer tissues was remarkably lower [(0.83±0.11)×10-3 mm2/s] than that of other tissues (P<0.01), while the ADC value of prostatitis [(1.45±0.19)×10-3 mm2/s] was lower than that of PZ [(1.67±0.31)×10-3 mm2/s] (P<0.01). Prostate cancer tissues had low-er ADCf [(1.54±0.23)×10-3 mm2/s],f [(45.8±5.4)%] and ADCs [(0.52±0.15)×10-3mm2/s] than the other tissues (P<0.01). The ADCf,f and ADCs were higher in PZ [(3.90±0.40)×10-3, (67.3±8.2)% and (1.51±0.36)×10-3 mm2/s] than prostatitis [(3.06±0.49)×10-3, (47.9±3.9)% and (0.91±0.29)×10-3 mm2/s) (P<0.01). The area under the curve (AUC) of ADCf and ADC were similar in differentiating cancer and prostatitis (0.96vs 0.94) (P>0.01), but the AUC off and ADCs in differ-entiating cancer from prostatitis (0.83 and 0.80) were signiifcantly lower than that of ADC (P<0.01).Conclusion:The biexponential DWI provided additional tissue characterization parameters for different prostate tissues. ADCf yielded comparable accuracy with ADC in identiifcation of prostate organ-conifned cancer. The biexponential parameter could further improve the diagnostic effcacy.