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.

Background and purpose:Renal cell carcinoma is the most common type of adult renal cancer, and the Fuhrman grading system is the most widely accepted independent indicator for the prognosis of kidney tumors. This study aimed to explore the correlation between the conventional diffusion weighted imaging (DWI) with various b values and Fuhrman grade of clear cell renal cancer, and assess the diagnostic efifciency of ADC values at different b values in differentiating Fuhrman low- and high-grade tumor. Methods:Thirty-three patients with pathologically proved clear cell renal cell cancer (CCRCC) and qualiifed images were included for the research. The diagnostic efif-ciency of ADC values at different b values for differentiating Fuhrman low-(1 and 2) and high-grade (3 and 4) tumor were also assessed and compared by receiver operator characteristic curve (ROC), and the optimum sensitivity, speciifc-ity and accuracy were selected using Youden index. Results:The 33 patients consist one tumor of Fuhrman grade 1, 14 of grade 2, 16 of grade 3 and 2 of grade 4. A negative correlation was found between the ADC0-800, ADC0-400-800, ADC0-600-1 200, ADC0-400-800-1 200 and ADCtotal with Fuhrman grade (r:-0.553,-0.511,-0.603,-0.645,-0.610, respectively), among these the ADC0-400-800-1200 showed the strongest correlation. The AUCs [area under the (receiver operator characteristic curve) ROC curve] of those parameters for differentiating the low-and high-grade tumor were 0.789, 0.757, 0.813, 0.844 and 0.835, among which the ADC0-400-800-1 200 reveal the best result, but the difference was not statistically signif-icant (P>0.05). The sensitivity of the ADC values were 86.7%, 73.3%, 60.0%, 86.7%and 86.7%;The speciifcity were 66.7%, 77.8, 72.2%, 77.8%and 72.2%. Conclusion:ADC0-800, ADC0-400-800, ADC0-600-1 200, ADC0-400-800-1 200 and ADCtotal re-vealed signiifcant correlation with the Fuhrman grade of clear cell renal cancer, and providing high diagnostic efifciency in differentiating Fuhrman low-and high-grade tumor.

Background and purpose: Human epididymis protein 4 (HE4) has been suggested to be a novel biomarker of epithelial ovarian cancer (EOC). The present study aimed to evaluate and compare HE4 with the commonly used marker, carbohydrate antigen 125 (CA125) and imaging examinations in detecting the recurrence of postoperative EOC. Methods:In this study, 92 patients with the recurrence of postoperative EOC were analyzed retro-spectively. The treatments method included the secondary operation (48 patients) and chemotherapy (44 patients). The sensitivity of CA125, HE4 and CT/MRI examinations in detecting the recurrence of postoperative EOC were analyzed and correlated with surgical pathology and clinical follow-up results. Results:The sensitivity of CA125 and HE4 were 58.7%and 61.9%with no signiifcant difference (P>0.05). The diagnostic sensitivity of CA125 combined with HE4 (80.4%) was statistically higher than that of 2 tumor marks being applied independently (P>0.05). The diagnostic sensitivity of CT/MRI examinations (88.0%) was statistically higher than that of CA125 and HE4 (P<0.05), and no signiifcant difference was observed between CT/MRI and combined CA125 and HE4 (P>0.05). The highest diagnostic sensitivity (97.8%) was attained by combining of CA125, HE4 and CT/MRI examinations, which was statistically high-er than that of CA125 combined with HE4 (P<0.01), and CT/MRI examinations (P<0.05). Conclusion:Serum HE4 is an effective tumor marker in detecting the recurrence of postoperative EOC as well as serum CA125. HE4 and CA125 may improve the diagnostic sensitivity statistically. The highest diagnostic sensitivity was attained by combining of CA125, HE4 and CT/MRI examinations, which is the suitable strategy in screening the patient of postoperative EOC.

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.