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

Abstract and Introduction

Abstract

Background: At present, prostate-specific antigen (PSA) is the primary evaluation index for judging the necessity of prostate cancer (PCa) biopsy. However, there is a high false-positive rate and a low predictive value due to many interference factors. In this study, we tried to find a novel prediction model that could improve the positive rate of prostate biopsy and reduce unnecessary biopsy.

Methods: We retrospectively studied 237 patients, including their age, body mass index (BMI), PSA, prostate volume (PV), prostate imaging-reporting and data system (PI-RADS) v2 score, neutrophil-lymphocyte ratio (NLR), biopsy Gleason score (BGS), and other information. The univariate and multivariate logistic analyses were used to screen out indicators related to PCa. After establishing a prediction formula model, we used receiver operating characteristic (ROC) curves to assess its prediction performance.

Results: Our study found that age, PSA, PI-RADS v2 score, and diabetes significantly correlated with PCa. Based on multivariate logistic regression analysis results, we created the following prediction formula: Y = 2.599 × PI-RADS v2 score + 1.766 × diabetes + 0.052 × age + 1.005 × PSAD – 9.119. ROC curves showed the formula's threshold was 0.3543. The composite formula had an excellent capacity to detect PCa with the area under the curve (AUC) of 0.91. In addition, the composite formula also achieved significantly better sensitivity, specificity, and diagnostic accuracy than PSA, PSA density (PSAD), and PI-RADS v2 score alone.

Conclusions: Our predictive formula predicted performance better than PSA, PSAD, and PI-RADS v2 score. It can thus contribute to the diagnosis of PCa and be used as an indicator for prostate biopsy, thereby reducing unnecessary biopsy.

Introduction

Prostate cancer (PCa) is the most common malignancy of the male reproductive system. 2017, PCa alone accounts for approximately 1 in 5 new cases and 8% of all cancer deaths in America,[1] and is overall a significant threat to the long-term health of men. Therefore, an early diagnosis is crucial for the clinical treatment and prognosis of PCa. To improve the accuracy of discriminating PCa, meaningful progress has been made in characterizing and developing methods, imaging techniques, and new biomarkers. Prostate-specific antigen (PSA) is the most common and cheapest screening method. However, there are many factors that have an influence on the PSA level, including age, prostate volume (PV), prostatitis, and digital rectal examination (DRE). As it merely relies on PSA and DRE, the diagnosis value's sensitivity and specificity for early diagnosis of PCa are not ideal; especially when the PSA is 4–10 ng/mL, the detection rate is only about 25%.[2] Furthermore, in recent decades, many new markers have been found, including prostate cancer antigen 3 (PCA3),[3] prostate specific membrane antigen (PMSA).[4] PSA precursors, PSA precursor protein [proenzyme of prostate-specific antigen (proPSA)] and other indicators, all which may have more clinical significance and application prospects than PSA.[5]

In addition, the growing availability of prostate magnetic resonance imaging (MRI) tools with their different functional imaging modalities and increased standardization has enlarged the role of MRI in detecting, localizing, and staging PCa. Most hospitals currently use multiparametric MRI (mpMRI) which consists of four main parameters: dynamic contrast-enhanced (DCE) imaging, T2-weighted (T2W) imaging, diffusion-weighted imaging (DWI), and MRI spectroscopy (MRS). The sensitivity of mpMRI for the diagnosis of PCa is 85%, and the specificity is 7l%.[6] Recent studies have shown the potential value of using pre-biopsy mpMRI to improve the detection and characterization of clinically significant PCa. Pre-biopsy mpMRI has been shown to increase the detection rate remarkably.[7] Another study showed that a quarter of men had normal mpMRI and could potentially avoid an unnecessary biopsy if mpMRI was performed first.[8] Prostate imaging reporting and data system (PI-RADS) v2 has been gradually incorporated into the diagnosis of PCa. A recent study using PI-RADS to predict prostate biopsy positive rates showed significantly improved predictive efficiency.[9]

According to Weinberg's theory,[10] inflammation and metabolism are typically deleterious in terms of tumor emergency. More recently, an elevated neutrophil-lymphocyte ratio (NLR) has been put forward as an independent marker for PCa at the early and advanced stages.[11] Metabolic syndrome (MS) is a group of syndromes that includes obesity, insulin resistance, dyslipidemia, hypertension, diabetes, etc. A study found that MS has a particular relationship with the onset of PCa.[12] Some studies have examined the relationship between diabetes mellitus (DM) and PCa, demonstrating a reduced risk of PCa among men with DM;[13] in contrast, other research indicates that patients with PCa and DM have worse overall survival.[14]

Nowadays, a prostate needle biopsy is the gold standard choice for the diagnosis of PCa. There are certain limitations, even though ultrasound-guided systematic prostate biopsy misses 21% to 28% of PCa.[15] Clinically, the indications for prostate biopsy are sometimes too broad. Additionally, a prostate biopsy is an invasive procedure with complications such as urinary retention, bleeding, and infection.[16] Therefore, we urgently need a new scoring system, which not only can reduce the false positive rate, but also be more practical and accurate. Our study aimed to examine multiple predictors including age, weight, PSA, PV, and MRI, for the individualized prediction of PCa. The diagnostic value of diabetes and NLR for PCa was also explored.

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