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Objectives: The incidence of prostate cancer is increasing every year, and precision diagnosis and treatment can help reduce unnecessary prostate punctures for prostate cancer patients in the gray area. This study aims to investigate the diagnostic value of 18 F-prostate specific membrane antigen (PSMA) imaging combined with prostate specific antigen (PSA)-derived indicators for gray zone prostate cancer., Methods: A total of 107 patients who underwent 18 F-PSMA PET/CT imaging for suspicious prostate cancer with tPSA of 4 to 10 μg/L (PSA gray zone) in a hospital were retrospectively included, and were divided into a prostate cancer group and a non-prostate cancer group based on pathological findings. Patients underwent PSA testing, 18 F-PSMA, and abdominal ultrasound, and age, tPSA, fPSA, f/tPSA, prostate volume, PSA density (PSAD), maximum standardized uptake value (SUVmax), and molecular imaging prostate specific membrane antigen (miPSMA) score were compared between the 2 groups. Multivariate logistic regression was used to analyze the influencing factors the diagnosis of gray zone prostate cancer. Receiver operating characteristic (ROC) curves were constructed to evaluate the efficacy of PSAD and SUVmax alone and in combination in diagnosing gray zone prostate cancer., Results: The volume of the prostate cancer group [42.00(34.00, 58.00) cm 3 vs 49.00(41.27, 60.41) cm 3 ] was smaller than that of the non-prostate cancer group ( Z =-2.376, P =0.017), and the PSAD [(0.18±0.06) μg/(L·cm 3 ) vs 0.15±0.05 μg/(L·cm 3 )] and SUVmax [18.63(8.03, 28.57) vs 9.33(5.90, 13.52)] were higher than those in the non-prostate cancer group (both P <0.05). The percentage of miPSMA score ≥2 in the prostate cancer group was higher than that in the non-prostate cancer group ( χ 2 =40.987, P< 0.001). PSAD ( OR =22.154, 95% CI 1.430 to 873.751, P =0.042) and SUVmax ( OR =1.301, 95% CI 1.034 to 1.678, P =0.009) were independent influential factors for the diagnosis of prostate cancer in the gray zone. The optimal cut-off values of PSAD and SUVmax were 0.22 μg/(L·cm 3 ) and 8.02, respectively, and the AUCs for the diagnosis of prostate cancer in the gray zone alone and in combination were 0.628 (95% CI 0.530 to 0.720, P <0.05) and 0.806 (95% CI 0.718 to 0.876, P <0.05), 0.847 (95% CI 0.765 to 0.910, P <0.05), with sensitivities of 41.03%, 76.92%, and 74.36% and specificities of 79.41%, 89.71%, and 92.65%, respectively., Conclusions: PSAD and SUVmax are increased in patients with gray zone prostate cancer, and the combination of PSAD and SUVmax is of high value in diagnosing gray zone prostate cancer.

Objective: To compare the diagnostic value of u PCA3 m RNA, t PSA, f PSA/t PSA ratio and PASD for prostate cancer with prostate specific antigen(PSA) in diagnostic gray zone. Methods: Prostate biopsy was performed in 200 patients whose t PSA were between 4 ng/m L~10 ng/m L(diagnostic gray zone). Among these patients, 111 of them were diagnosed with benign prostatic hyperplasia(BHP group) and 89 with prostate cancer(PCa group) by pathology. The t PSA and f PSA in serum were measured by chemiluminescence-assay. The volume of prostate was measured by transrectal ultrasound. f PSA/t PSA ratio and PSAD were computed and analyzed.The difference of u PCA3 m RNA, t PSA, f PSA/t PSA ratio and PASD between the PCa group and BPH group were compared, and the area under ROC curve, the sensitivity and specificity were analyzed. Results: The f PSA/t PSA ratio of the PCa group was significantly lower than that of the BPH group. The levels of t PSA, PSAD and PCA3 m RNA were significantly higher in PCa group than in BPH group(both P<0.01). The area under ROC curve of these indexes in descending order was PCA3 m RNA, PSAD, f PSA/t PSA radio and t PSA.When the critical value of PCA3 m RNA and PSAD were 0.27 and 0.15, the sensitivity and specificity of diagnosing PCa were 86.5% and77.5%, 80.9% and 55.9%, respectively. Conclusions: Detection of the u PCA3 m RNA, f PSA/t PSA ratio and PASD can improve the diagnosis of prostate cancer with PSA in diagnostic gray zone, and the value of u PCA3 m RNA in urine was highest for the diagnosis of prostate cancer. [ABSTRACT FROM AUTHOR]

Objective: To investigate the preliminary applicability of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score in the condition of 3.0T multi-parametric magnetic resonance imaging (Mp-MRI) combined with clinical classic indicators for the diagnosis of prostate cancer (PCa). Methods: The clinical and MRI materials of 247 patients of suspicious prostate disease treated in Second Affiliated Hospital of Soochow University from June 2015 to November 2016 were analyzed retrospectively, including 110 cases with PCa and 137 cases without cancer.All cases underwent the high-resolution axial T(2)-weighted imaging (T(2)WI), diffusion weighted imaging (DWI) and dynamic contrast enhancement-magnetic resonance imaging (DCE-MRI) and were confirmed pathologically by puncture biopsies.The Mp-MRI materials of all cases were scored according to PI-RADS v2.The prostate volume and prostate specific antigen (PSA) density (PSAD) value were calculated according to the formulas.The univariate and multivariate analysis were performed for the observed indicators (age, prostate volume, PSA, PSAD and PI-RADS v2 score) to determine the independent predictors for PCa.Then, a Logistic regression model (combined prediction model) was established by the independent predictors for combined diagnosis of PCa.The receiver operating characteristic curve (ROC) curve analysis was performed to get the sensitivity and specificity of each independent predictor and the model to diagnose PCa.The differences of AUC values of each independent predictor and the model were compared with each other to evaluate the diagnostic performance for PCa. Results: The differences in the age, prostate volume, PSA, PSAD and the PI-RADS v2 score between patients with PCa and non-cancer group were all statistically significant ( t =2.870, Z =-4.230, -7.787, -9.477, -10.826, all P <0.05). The PSAD and PI-RADS v2 score were independent predictors for PCa ( OR =3.331, 10.546, both P <0.05). The Logistic regression combined prediction model by PI-RADS v2 score and PSAD to forecast PCa was Logit( P )=-5.097+ 2.309×PSAD+ 1.214×PI-RADS v2 score.The area under the curve (AUC) of ROC in the combined model (0.911) was higher than that in the PI-RADS v2 score (0.886) and PSAD (0.851) and the differences were all statistically significant ( Z =2.416, 2.716, both P <0.05); but the difference in the AUC value between PI-RADS v2 score and PSAD was not statistically significant ( Z =1.191, P =0.234). The diagnostic sensitivity of PSAD, PI-RADS v2 score and the model were: 0.891, 0.782 and 0.855, respectively; the specificity were 0.449, 0.912 and 0.847, respectively on their positive thresholds (0.15 μg·L(-1)·ml(-1,) 4 and -0.82). Conclusion: PI-RADS v2 score combined with PSAD in diagnosing PCa is superior to the single application of them and it can lead to high diagnostic sensitivity and specificity for PCa.