RESUMEN
Objective:To evaluate the diagnostic efficacy of prostate imaging recurrence reporting (PI-RR) system for detecting local recurrence after radical prostatectomy (RP) in prostate cancer (PCa) and to assess the consistency of the PI-RR scores assigned by different seniority radiologists.Methods:This study was a cross-sectional study. A total of 176 PCa patients who underwent multi-parametric MRI (mpMRI) for biochemical recurrence (BCR) after RP from July 2015 to October 2021 at the First Affiliated Hospital of Soochow University were retrospectively collected. The mpMRI images were reviewed and the PI-RR scores of the main lesions were assigned independently by six different seniority radiologists (2 junior, 2 senior and 2 expert radiologists). Following the reference standard determined by biopsy pathologic results, follow-up imaging, or prostate specific antigen levels, the patients were divided into two groups: 54 patients with local recurrence and 122 patients without local recurrence. The intraclass correlation coefficient ( ICC) and Kappa test were used to evaluate the consistency of the PI-RR scores by different seniority radiologists. The receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic efficacy of the PI-RR scores assessed by different seniority radiologists for detecting local recurrence of PCa after RP. The DeLong test was utilized to compare the areas under the ROC curve (AUC) of different seniority radiologist PI-RR scores and a false discovery rate (FDR) was applied to correct results using the Benjamini and Hochberg method. Sensitivity and specificity were calculated according to the cutoff value of PI-RR score≥3 or 4. Results:The ICC (95% CI) of all different seniority radiologists was 0.70 (0.64-0.76). The Kappa value was 0.528, 0.325 and 0.370 respectively between expert and senior radiologists, expert and junior radiologists, senior and junior radiologists. The AUC (95% CI) of junior, senior, and expert radiologists were separately 0.73 (0.65-0.81), 0.81 (0.74-0.88), and 0.86 (0.80-0.93). The AUC of the expert radiologist PI-RR score was higher than those of senior and junior radiologist PI-RR scores ( Z=2.22, 3.21, FDR P=0.039, 0.003). The PI-RR score of senior radiologist had higher AUC than that of junior radiologist ( Z=2.22, FDR P=0.026). With the PI-RR score of 3 or greater as a cutoff value, the sensitivity of junior, senior and expert radiologists were respectively 0.59, 0.65, and 0.78 and the specificity were 0.82, 0.93, and 0.95. With the PI-RR score of 4 or greater as a cutoff value, the sensitivity of junior, senior and expert radiologists were respectively 0.50, 0.54, and 0.69 and the specificity were 0.88, 0.96 and 0.97. Conclusion:PI-RR score can accurately diagnose local recurrence of PCa after RP. PI-RR score has a moderate inter-reader consistency across different seniority radiologists. And the diagnostic performance is influenced by the experience of radiologists.
RESUMEN
Objective:To construct a clinical-radiomics model based on MRI, and to explore its predictive value for biochemical recurrence (BCR) after radical prostatectomy in prostate cancer patients.Methods:A total of 212 patients with prostate cancer who underwent radical prostatectomy in the First Affiliated Hospital of Soochow University from January 2015 to December 2018 and had complete follow-up data were retrospectively analyzed. The random toolkit of Python language was used to randomly sample the patients at a ratio of 7∶3 without replacement, and they were divided into a training set (149 cases) and a test set (63 cases). The endpoint of follow-up was BCR or at least 3 years. BCR occurred in 50 patients in the training group and 21 patients in the test group. The imaging features of the main lesion area in the preoperative T 2WI, diffusion-weighted imaging and apparent diffusion coefficient map of patients in the training set were extracted, and the unsupervised K means clustering algorithm was used to screen the features. The selected features were fitted by a multivariate Cox regression model, and the radiomics model was constructed. Univariate Cox regression analyses were used to screen the main clinical risk factors associated with BCR, and the clinical-radiomics model was constructed combined with RadScore. In the test set, the time-dependent receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) was calculated to evaluate the predictive efficacy of the radiomics model, clinical-radiomics model and prostate cancer risk assessment after radical resection (CAPRA-S) score for the occurrence of BCR. Harrell consistency index (C-index) was used to evaluate the model to predict BCR consistency. The calibration curve was used to evaluate the degree of variation of the model. The decision curve was used to evaluate the clinical application value of the prediction model. Results:A total of 26 radiomics features were screened to establish the radiomics model. The univariate Cox showed that the preoperative clinical features included preoperative prostate-specific antigen level (HR=1.006, 95%CI 1.002-1.009, P=0.001), Gleason score of biopsy (HR=1.422, 95%CI 1.153-1.753, P=0.001), clinical T stage (HR=1.501, 95%CI 1.238-1.822, P<0.001). The multivariate Cox showed that the RadScore was an independent predictor of BCR after radical prostatectomy (HR=51.214, 95%CI 18.226-143.908, P<0.001). The selected preoperative clinical features were combined with RadScore to construct a clinical-radiomics model. In the test set, the AUCs of the time (3 years)-dependent ROC curves of the radiomics model, the clinical-radiomics model, and the CAPRA-S score were 0.824 (95%CI 0.701-0.948), 0.841 (95%CI 0.714-0.968), and 0.662 (95%CI 0.518-0.806), respectively. The C-index of the radiomics model, clinical-radiomics model and CAPRA-S score were 0.784 (95%CI 0.660-0.891), 0.802 (95%CI 0.637-0.912) and 0.650 (95%CI 0.601-0.821), respectively. The calibration curve showed that the predicted probability and actual probability of BCR by radiomics model, clinical-radiomics model and CAPRA-S score were in good agreement (χ 2=7.64, 10.61, 6.37, P=0.465, 0.225, 0.498). The decision curve showed that the clinical net benefit of the clinical-radiomics model and the radiomics model was significantly higher than the CAPRA-S score. When the threshold probability was 0.20-0.30, 0.40-0.50, and >0.55, the clinical net benefit of the clinical radiomics model was higher than that of the radiomics model. Conclusions:The clinical-radiomics model can effectively predict the occurrence of BCR in patients with prostate cancer after radical prostate ctomy, and the prediction efficacy is better than the radiomics model and CAPRA-S score.
RESUMEN
Objective To examine the association between the polymorphisms of brain-derived neurotrophic factor (BDNF)gene with heroin dependence.Methods Genomic DNA was isolated from the venous blood leukocytes of 308 unrelated patients with heroin dependence and 31 7 healthy individuals.Seven single nucleotide polymorphisms (SNPs)were genotyped using MassARRAY system.Data were analyzed using HaploView 4.0 and SPSS 20.0 software.Results There was a significant difference in the genotype frequency of rs6265 between heroin dependence group and healthy control group (χ2 =1 5.1 5 1,P =0.001).The rs6265 G allele was significantly higher than in controls (χ2 =9.864,P =0.002,OR =1.429,95% CI =1.143 -1.786).Furthermore,there was also a significant difference in the genotype frequency of rs13306221 between heroin dependence group and control group (χ2 =7.699,P =0.006).The rs13306221 G allele was significantly higher than in controls (χ2 =7.137,P =0.008,OR =0.539,95% CI =0.340-0.853).Strong linkage disequilibrium (LD)was observed in one block (D’> 0.9;r 2 >0.8),and significantly less G-G haplotype frequency of block 1 (χ2 =4.546;P =0.033)was found in heroin dependence group. Conclusion Our findings support the role of BDNF rs6265 and rs13306221 polymorphisms in heroin dependence and may guide future studies to identify other genetic risk factors for heroin dependence.