Establishing a Novel Prediction Model for Improving the Positive Rate of Prostate Biopsy

Tao Tao; Deyun Shen; Lei Yuan; Ailiang Zeng; Kaiguo Xia; Bin Li; Qingyu Ge; Jun Xiao

Disclosures

Transl Androl Urol. 2020;9(2):574-582. 

In This Article

Discussion

We investigated the serological tumor markers, imaging features, and history of tumor-related metabolic diseases in 237 patients with suspected PCa. We demonstrated that decreased age, PSAD, mpMRI, and diabetes were independent and adverse predictors in the logistic analysis. The incidence of PCa is increasing annually with an aging population. The early screening markers for PCa are thus extremely important, with the current markers being predominantly serum PSA. Most urologists judge whether to perform prostate biopsy based on these PSA results. Nevertheless, PSA is susceptible to many negative factors, and its prediction for PCa is not only insensitive but also too generalized. To better predict PCa, scholars have proposed different PCa prediction models,[18,19] with poor predictive performance. In addition, researchers have created PSAD, which is based on PSA and PV. PSAD attenuates the effect of PV on PSA, and thus PSAD is considered to be superior to PSA in identifying PCa, especially when the PSA concentration is in the gray area (4–10 ng/mL).[20]

mpMRI is one of the most important imaging tests for the diagnosis of PCa. It is not only able to detect an early-stage tumor but it can also predict the Gleason score of PCa.[21] Pre-biopsy mpMRI is more accurate in assessing the extent and risk of PCa, and the excellent diagnostic accuracy of mpMRI PI-RADS v2 score has been conclusively proven.[22] When it and other indicators were combined, the judgment threshold was even higher. Many studies showed that when PI-RADS v2 score was combined with PSAD, the screening efficiency of biopsy improved greatly, resulting in a near 50% reduction in unnecessary biopsies.[23,24] In the present study, the PI-RADS v2 score has the highest OR value in multivariate logistic regression analysis, showing that the higher the value, the higher the risk of PCa. In our data, 118 (49.79%) patients' PI-RADS v2 score was >3, and, according to the guidelines, this indicates that the possibility of PCa is high. When the patient is in a low-risk group (PI-RADS v2 score ≤3), appropriate decisions should be made to avoid unnecessary needle biopsy.

We also observed other statistically significant phenomena; DM was an independent risk factor for PCa; that is, diabetic patients are more prone to PCa. Nevertheless, the influence of DM on the pathogenesis of PCa is still controversial, and its mechanisms remain unclear. It may be related to factors such as blood glucose control level, insulin resistance, and hyperinsulinemia in DM patients.[25] In addition, some researchers have found that hypoglycemic drugs also have an impact on PCa. A study in Taiwan showed that metformin reduced the risk of PCa in patients with type 2 DM. Another study found that metformin reduced PCa patients' biochemical recurrence rate, risk of distant metastasis, and tumor-specific mortality.[26,27] Based on this, we recommend that diabetic patients should actively control blood sugar to ensure it is within a reasonable range when applying rational hypoglycemic drugs.

Unexpectedly, our findings showed that NLR was not an independent risk factor of PCa. Since Virchow first addressed the link between cancer and inflammation in the 19th century, contemporary research has generally recognized that inflammation plays a vital role in carcinogenesis.[28] Researchers have revealed that inflammation promotes PCa,[29] while Cainozoic evidence has shown that NLR is a valuable prognostic factor in PCa. High NLR in pretreatment was a poor prognostic factor for survival in patients. Most studies have found that NLR is an important predictor of PCa prognosis (overall survival, progression-free survival, recurrence-free survival, etc.),[11] but the diagnostic value of PCa may need further study.

Furthermore, we created a predictive formula based on the patient's age, PSAD, mpMRI PI-RADS v2 score, and DM. We tested and compared the predictive value of the formula and other screening methods. The formula had a higher screening evaluation index. The order of AUC values was PSA < PSAD < MRI PI-RADS v2 score < our formula. The AUC of the ROC curve of the formula was the largest, showing the diagnostic value of the formula is the best. In addition, the detective formulas achieved optimum sensitivity, NPV, and overall accuracy, but the specificity and PPV were lower. High sensitivity means more patients are screened out, leading to fewer patients missing out on chances for early treatment, which can result in a poor prognosis. The specificity of the formula was approximately 3 times that of PSA, and its application could significantly reduce unnecessary prostate biopsy and prevent harm to patients. Because PPV and NPV vary with prevalence, they cannot be used as an index to evaluate the value of diagnostic tests. The +LR of the formula was much greater than that of PSA, while the –LR was the smallest. This means that the formula can screen out more positive patients while eliminating unnecessary puncture patients, but they still should be closely observed.

Finally, some limitations to our study should be addressed. First, the cohort of the study was not an optimum screening population but a hospitalized population in a single hospital. There was undoubtedly a selection offset. Second, owing to the retrospective nature and the lack of external validation, the predictive significance of the formula remains to be prospectively studied in future populations and larger cohorts. Third, our study did not include other indicators of MS, such as triglycerides, cholesterol, etc. Further research should fully estimate the predictive value of MS. Last, the men with PCa were diagnosed by biopsy using pathological specimens obtained by TRUS-guided biopsy, and thus there was a lack of comparison to other biopsy pathways. A difference in operation might have caused a missed diagnosis, which could have led to bias in our research.

Overall, our findings suggest that age, PSAD, mpMRI PI-RADS v2 score, and DM are independent risk factors of PCa, as they showed superior performance in detecting PCa. These factors could provide clinicians with better screening methods to predict PCa before biopsy. In addition, DM was closely associated with PCa, and thus diabetic patients are more likely to develop PCa.

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