The predictive value of PI-RADS v2 score in high-grade prostate cancer: a multicenter retrospective study

Background: To explore the predictive value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with prostate cancer related biomarkers. Material and Methods: A total of 316 patients with newly discovered prostate cancer at Hospital of and Hospital from December 2017 to August 2019 were enrolled in this study. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade prostate cancer. ROC curves were generated for the risk factors to distinguish the cut-off point. Logistic regression analyses were used to investigate the independent risk factors of high-grade prostate cancer. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve, ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. Results: Of 316 patients, a total of 187 patients were diagnosed as high-grade prostate cancer. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 score were significantly different between the high- and low-grade prostate cancer patients. Univariate and multivariate logistic regression analyses showed only tPSA, prostate volume and PI-RADS v2 score were the independent risk factors of high-grade prostate cancer. The nomogram could predict the probability of high-grade prostate cancer, with a sensitivity of 79.4% and a specificity of 77.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.840 (0.797-0.884). Leave-one-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. External data validation was performed with a sensitivity of 80.6% and a specificity of 77.3%, the Kappa value was 0.5755. Conclusions: PI-RADS v2 score had the value in predicting high-grade prostate cancer, the nomogram prediction model may help

based on PSA and derived parameters, few models incorporating multiparametric prostate MRI such as PI-RADS v2 score [18][19][20]. Furthermore, due to the differences in races and morbidity, it needs further confirming whether these models are appropriate for Chinese patients, and most of these studies are lack of external data validation.
Thus, we conducted a multicenter retrospective study to determine the predictive value of the PI-RADS v2 score in high-grade PCa and establish a prediction model combined with prostate cancer related biomarkers.

Material And Methods Study patients
The study patients consisted of a development cohort and a validation cohort. The development cohort included 316 patients with newly discovered PCa at Zhongnan Hospital of Wuhan University and Renmin Hospital of Wuhan University from December 2017 to August 2019. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. The pathological result of ultrasound guided prostate biopsy or radical prostatectomy was as the outcome variable. In this sdudy, We considered GS ≤ 3 + 4 as a low-grade PCa, GS ≥ 3 + 4 as a high-grade PCa.
We retrospective reviewed medical records of all enrolled patients to acquire the clinical information.
An independent cohort included 53 patients from Xiangyang Central Hospital (January 2018 to October 2019) was used to validate the nomogram prediction model. All patients provided the informed consent. The Ethics Committee at Zhongnan Hospital of Wuhan University had approved the using clinical information in our study (approval number: 2015029). All procedures and ethical standards were done in accordance with the national research committee and/or institutional.

Inclusion criteria
Patients were enrolled in this study if they met all the following criteria: (i) the prostate cancer patients; (ii) patients who underwent ultrasound guided prostate biopsy or radical prostatectomy; (iii) patients who underwent multiparameter MRI of the prostate (T2 WI, DWI, DCE imaging), and prostate multiparameter MRI distanced biopsy or radical prostatectomy time was within one month; (iv) had a complete and detailed clinical, pathological data record.

Exclusion criteria
Patients meeting any of the following criteria were excluded: (i) merge other tumors; (ii) patients have received treatment before multiparameter MRI examination, such as hormone therapy, radiotherapy; (iii) any incomplete clinical or pathological data.

Statistical analysis
Age, fPSA and PSAD were analyzed by two-sample t test, graded variables were analyzed with Mann-Whitney test or Chi-square test. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade PCa. Receiver operating characteristic (ROC) curves were generated for the risk factors to distinguish the cut-off points, the areas under the curves (AUCs) were compared. Univariate and multivariate logistic regression analyses were used to investigate the independent risk factors of high-grade PCa. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve was generated to assess the agreement of the nomogram-predicted probability with the actual observed probability. ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. We used SPSS 16.0 to perform all statistical analyses. Nomogram and calibration curve were generated with R version 3.5.0 and a p value < 0.05 was considered statistically significant.

Patient characteristics and univariate analysis for prostate cancer
The detailed clinical parameters of development cohort were displayed in Table 1, no significant difference was observed in clinical parameters between the two hospitals (all p > 0.05). In development cohort, 187 (59.2%) of 316 patients were classified as high-grade PCa. The mean age was 73.1 ± 8.5 years, the median age was 73 years. The mean age of high-grade PCa patients was 73.5 ± 8.1 years, the median age was 74 years, and the mean age of low-grade PCa patients was 72.6 ± 7.9 years, with a median age of 72 years. Two-sample t test showed that only fPSA and PSAD were significantly different between the high-and low-grade prostate cancer patients (p < 0.05).
Mann-Whitney test and Chi-square indicated that tPSA, prostate volume and PI-RADS v2 were significantly different between the two groups (p < 0.05). The age and fPSA/tPSA had no statistical difference between two groups ( Table 2). ROC curves were generated for the risk factors to distinguish the cut-off points To distinguish the cut-off points of high-grade PCa risk factors, ROC curves were generated and the AUCs were compared. The cut-off point of every variable was set based on the value of the maximum sum of the sensitivity and specificity on the ROC curve. Figure 1 and Table 3 Table 3. Table 3 The diagnostic value of each variable in high grade prostate cancer.   (Table 4). Corresponding to each variable on the nomogram (Fig. 2), the total score was calculated to predict the probability of infection in each patient. In the nomogram, the scores corresponding to the vertical line on the "score" ruler by all the variable values of the patient were found, accumulated the scores of all the variable values and found the vertical line of the "predictive ruler" on the accumulated "total score" ruler. The corresponding point was converted to the corresponding probability on the "High-grade PCa probability" scale according to the score on the predicted ruler, which was the probability of patient with high-grade PCa. The clinical information of each patient was included in the nomogram for matching analysis. The sensitivity was 79.4% and the specificity was 77.6%. The calibration curve (Fig. 3) displayed good agreement of the predicted probability with the actual observed probability for high-grade PCa, which indicated that the nomogram had good accuracy.
Evaluation of the nomogram prediction model for high-grade prostate cancer ROC curve was generated to evaluate the value of the nomogram prediction model, the "high-grade PCa" was as the outcome variable (Fig. 4). The AUC of ROC curve was 0.840 (0.797-0.884). Leaveone-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. It was been proved again that the nomogram prediction model had good discriminative ability and accuracy. To confirm the stability of the model, external data validation was performed, which was independently collected in Xiangyang Central Hospital. The sensitivity was 80.6% and the specificity was 77.3%, the Kappa value was 0.5755 (Table 5).  [21]. Therefore, it is necessary to study how to improve the diagnostic accuracy of high-grade PCa.
Most studies considered GS ≤ 3 + 3 as a low-grade PCa, GS ≥ 7 as a high-grade PCa, but in recent years, more and more evidence indicated that the metastasis probability, 10-years cancer specific survival and biochemical recurrence after radical resection of PCa patients with a GS = 3 + 4 were closer to patients with a GS = 3 + 3 [3][4][5]. CUA Guideline, European Association of Urology (EAU) Guideline and European Society for Medical Oncology (ESMO) Guideline stated that PCa patients with a GS = 4 + 3 had remarkably different prognosis from who with a GS = 3 + 4, and recommended different interventions [8,[22][23]. Hence, we considered GS ≤ 3 + 4 as a low-grade PCa and GS ≥ 3 + 4 as a high-grade PCa in this sdudy.
The detailed clinical parameters of enrolled patients in development cohort from the two hospitals had no significant difference, demonstrating the universality of the enrolled patients. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 were significantly different between the high-grade PCa patients and low-grade PCa patients, mainly consistent with previous studies [14][15][16][17]. We generated ROC curves and found that age, fPSA, fPSA/tPSA and prostate volume were not ideal diagnostic parameters because of low sensitivity (< 70%) or low specificity (< 70%).
Park et al. demonstrated that PI-RADS v2 score could help preoperatively predict clinically significant prostate cancers, with the AUC was about 0.80, although it was higher than AUC of tPSA, there was no statistical difference between them [10]. The results basically consistent with our study. PSAD refers to the PSA content of a prostate per unit volume. PSAD was reported to significantly increase tumor detection rate and had a closely relationship with tumor invasiveness [24]. The results in our study showed that AUC of PI-RADS v2 score was the highest (0.869), and PSAD was the second (0.818). The cut-off point of PI-RADS v2 score was ≥ 4, as same as our previous study [13]. But the cut-off point of PSAD was ≥ 0.61 ng/mL/cm 3 , higher than 0.15-0.35 in previous studies [15][16][17]. The analysis of the reasons may be as follows: (i) the scope of tPSA in this study was large (1.57-964.43 ng/ml), not limited to 4-10 ng/ml; (ii) mainly for high-grade tumors, Li et al. [25] found that Gleason score < 7 group, PSAD average ± standard deviation was (0.43 ± 0.48) ng/ml/cm 3 , and Gleason score ≥ 7 group, PSAD average ± standard deviation was (2.55 ± 11.06) ng/ml/cm 3 , the difference p value between the two groups was < 0.001. Therefore, it was reasonable to believe that when the research object was high-grade prostate cancer, the cut-off point of PSAD will increased.
In this study, univariate logistic regression analysis showed tPSA, fPSA, fPSA/tPSA, prostate volume, PSAD and PI-RADS v2 score were the risk factors (p < 0.05 University had approved the using clinical information in our study (approval number: 2015029). All procedures and ethical standards were done in accordance with the national research committee and/or institutional.

Consent for publication:
Written consent for publication was obtained from all the patients involved in our study.

Competing interests:
The authors declare that they have no competing interests.  The nomogram was developed for high-grade prostate cancer. To estimate the risk of highgrade prostate cancer, the points for each variable were calculated by drawing a straight line from a patient's variable value to the axis.

Figure 3
The calibration curve was developed for high-grade prostate cancer. The nomogrampredicted probability is plotted on the x-axis, and the actual probability is plotted on the yaxis.

Figure 4
The ROC curve developed for nomogram prediction model of high-grade prostate cancer.