Objective: To investigate the expression and clinical significance of Ezrin, a tumor metastasis related gene, in tissues of nasopharyngeal carcinoma (NPC). Methods: The expression of Ezrin was detected with two-step immunohistochemistry technique in tissues of 38 NPC patients and 20 nasopharyngitis patients. All of the Ezrin expression and clinical data (gender, age, clinical stage, recurrence, and ]ymphonode metastasis) were compared and analyzed between two groups. Resuits: The Ezrin expressions were significantly different between NPC tissue (and peri-NPC tissue) and naeopharyngitis tissue (P＜0.05). No significant difference was found in the Ezrin expressions between the NPC tissue and the peri-NPC tissue (P＞0.05). It was found by Spearman analysis method that the Ezrin expression increased with patients' age (P＜0.05), but no significam relationship with gender, clinical stage, recurrence, and lymphonode metastasis (P＞0.05). The Ezrin expression influenced the survival time of NPC patients significantly (P＜0.01). Conclusion: The Ezrin expression can be an useful prognostic factor for NPC patients.

Objective To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods A total of 55 patients who were undergoing preoperative muti?parameters MRI of T2WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI?transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo?diffusivity (D*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t?test was used to compare the differences in lesion parameters between prostate cancer and BPH, low?risk (BPH+Gleason score 6 points) and medium?high?risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis.Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low?risk or high?risk. Results 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia(BPH) in PZ were included, meanwhile, the cases of GS≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa?BHP group and high?low risk group in TZ (P<0.05), D*and f had no significant differences (P>0.05). ADC and MD showed relatively higher negativity correlations (r were－0.585 and－0.489, P<0.05) with GS of PCa in TZ. ADC exhibited a higher area under the curve (AUC 0.864) compared with D with area under the curve (AUC 0.853), however, the difference is not significant (P>0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid?higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD.

Objective:To evaluate the application of multiparametric MRI (mpMRI)-transrectal ultrasound (TRUS) fusion guided biopsy in the diagnosis of clinical significant prostate cancer (PCa).Methods:A prospective analysis was performed in 168 patients with suspected PCa from September 2015 to June 2017 in the First Affiliated Hospital of Soochow University. Suspicious areas on mpMRl were defined and graded using prostate imaging reporting and data system version 2 (PI-RADS V2) score. All the patients had the TRUS-guided systematic biopsy, 108 patients with PI-RAD V2 scores ≥ 3 had additional MRI-TRUS targeted biopsies. Taking pathologic results as golden standard, the detection rates were compared between the 2 methods using χ 2 test. Results:Initially, all of the 168 patients underwent TRUS biopsy. PCa was detected in 86 (101 niduses) of 168 patients (51.19%, 86/168), 82 (91 niduses) (48.81%, 82/168) were not prostate cancer. Seventy eight (46.43%, 78/168) cases of PCa were detected by TRUS biopsy, and 63 (58.33%, 63/168) cases of PCa were detected by MRI-TRUS fusion guided biopsy, the difference was statistically significant between TRUS biopsy and MRI-TRUS fusion guided biopsy (χ 2=3.73, P=0.035). The 168 patients were biopsied with a total of 2 300 cores, including TRUS biopsy 2 016 cores and MRI-TRUS fusion targeted biopsy 284 cores. Additionally, the detection rate for per cores for MRI-TRUS fusion targeted biopsy (51.76%, 147/284) was significantly higher than that for TRUS biopsy cores (19.64%, 396/2 016) (χ 2=142.38, P<0.05). Among patients with a positive biopsy for PCa, the biopsy cores for conventional TRUS biopsy was 1 032 comparing to 214 cores for MRI-TRUS biopsy. The suspicious MRI-TRUS fusion targeted biopsy (68.69%, 147/214) detected more PCa compared with TRUS biopsy (38.37%, 396/1 032) (χ 2=66.27, P<0.05). Among patients with a positive biopsy for PCa, MRI-TRUS fusion targeted biopsy [69.74% (106/152)] detected more significant cancer cores than TRUS biopsy [54.50% (351/644) ] (χ 2=11.67, P<0.05). Conclusion:MRI-TRUS fusion biopsy combined with PI-RADS V2 increases positive rate markedly and improves the detection rate of clinical significant PCa.

Objective: To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods: A total of 55 patients who were undergoing preoperative muti-parameters MRI of T₂WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI-transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo-diffusivity (D^*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t-test was used to compare the differences in lesion parameters between prostate cancer and BPH, low-risk (BPH+Gleason score 6 points) and medium-high-risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis. Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low-risk or high-risk. Results: 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia (BPH) in PZ were included, meanwhile, the cases of GS ≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa-BHP group and high-low risk group in TZ (P<0.05), D^* and f had no significant differences (P>0.05). ADC and MD showed relatively higher negativity correlations (r were -0.585 and -0.489, P<0.05) with GS of PCa in TZ. ADC exhibited a higher area under the curve (AUC 0.864) compared with D with area under the curve (AUC 0.853), however, the difference is not significant (P>0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS ≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid-higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD.