A novel nomogram predicting the risk of positive biopsy for patients in the diagnostic gray area of prostate cancer

Preanalytical stability of molecular forms of prostate-specific antigen in serum samples (PSA, free PSA, [-2]proPSA) and their impact on fPSA/tPSA ratio and PHI

BACKGROUND

Prostate cancer is a common cancer in men. Detection methods include the measurement of biomarkers: prostate-specific antigen (PSA), free PSA, [-2]proPSA, and the calculated indices: fPSA/tPSA ratio and Prostate Health Index (PHI). Proper preanalytical conditions are crucial for precise measurement and failure to adhere to protocols or regulations can influence the diagnostic algorithm. We assessed the stability of the above-mentioned biomarkers, fPSA/tPSA ratio and PHI, under various pre-analytical conditions.

METHODS

Serum samples from 45 males were collected and stored under specific conditions before tPSA, fPSA, and [-2]proPSA were measured. Subsequently, the fPSA/tPSA and PHI were calculated.

RESULTS

tPSA, fPSA, and [-2]proPSA remained stable during the two freeze-thaw cycles. Storage at 4°C and 22°C resulted in stable tPSA concentrations. However, fPSA levels decreased and [-2]proPSA levels increased over time. The fPSA/tPSA ratio remained stable for 72 h, at which point a decrease was observed in the samples kept at 4°C and 22°C. A gradual increase in PHI was observed in the samples kept at 4°C and 22°C.

CONCLUSIONS

All biomarkers remained stable during two freeze-thaw cycles. tPSA was the most stable analyte when stored at 4°C, as well as at RT. A gradual increase of [-2]proPSA and a slight decrease in fPSA were observed during the storage test. This led to a decrease in the fPSA/tPSA ratio and an elevation in the PHI. We therefore recommend measuring prostate biomarkers promptly following blood collection.

IMPACT

Understanding the pre-analytical stability of prostate biomarkers helps prevent false positive results and improve the accuracy of diagnostics for prostate cancer.

Association between monocyte-to-lymphocyte ratio and prostate cancer in the U.S. population: a population-based study

Introduction

Monocyte-to-lymphocyte ratio (MLR) is a convenient and noninvasive inflammatory biomarker, and inflammation has been reported to be associated with prostate cancer (PCa). Our objective was to ascertain any possible correlation between PCa and MLR.

Methods

We utilized data from the 1999-2020 cycles of the National Health and Nutrition Examination Survey (NHANES) regarding MLR and PCa. The independent associations of MLR and other inflammatory biomarkers (platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), system inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI)) with PCa was investigated using weighted multivariate logistic regression and generalized additive models. Receiver operating characteristic (ROC) curves were conducted to evaluate and contrast their diagnostic capabilities.

Results

The analysis we conducted comprised 25,367 persons in total. The mean MLR was 0.31 ± 0.14. The prevalence of PCa was 3.1%. A positive association was found between MLR and PCa (OR = 2.28; 95% CI: 1.44, 3.62). According to the interaction tests, age, body mass index (BMI), hypertension, diabetes, and smoking status did not significantly impact the relationship between MLR and PCa (all p for interaction >0.05). ROC analysis showed that MLR had a stronger discriminative ability and accuracy in predicting PCa than other inflammatory biomarkers (NLR, SII, AISI, PLR, and SIRI).

Conclusion

MLR might be better than other inflammatory biomarkers (NLR, SIRI, AISI, PLR, and SII) in predicting PCa. American adults who have elevated levels of MLR, NLR, PLR, SII, and AISI should be aware that they have a greater risk of PCa.

Development and internal validation of a nomogram predicting significant prostate cancer: Is it clinically applicable in low prevalent prostate cancer countries? A multicenter study

BACKGROUND

Accurately identifying aggressive prostate tumors and studying them as a separate outcome are urgently needed. Nomogram is a predictive tool using an algorithm, it has been widely applied in clinical practice to predict prognosis. We aimed to develop and internally validate a nomogram predicting clinically significant prostate cancer (csPCa).

METHODS

Data were retrieved from the records of the two main hospitals in Riyadh, during the period 2019-2022. Significant variables associated with csPCa cases were used to develop and internally validate a novel nomogram, utilizing the C index, and calibration curves. Decision curve analysis (DCA) was used to assess its clinical utility.

RESULTS

Prostate imaging reporting and data system (PI-RADS), smaller prostate volume, and prostate-specific antigen (PSA) > 10 ng/mL were significantly associated with the risk csPCa, respectively. The model developed by the nomogram showed an excellent accuracy for csPCa discrimination, as indicated by area under the curve (0.83), and calibration curves. DCA showed that our model was superior and surpassed all other models with a larger net benefit for various threshold probabilities. Based on our model, at a probability threshold of 30%, biopsying patients is the equivalent of a strategy that led to an absolute 5% reduction in the number of biopsies without missing any csPCa.

CONCLUSION

The developed nomogram consisting of PI-RAD, total PSA, and prostate volume showed a robust predictive capacity for csPCa before prostate biopsy that may be valuable for clinical judgment to prevent needless biopsy. Yet, the small percentage (5%) of yielded unnecessary biopsies that could be saved by using such a model, cast an important question on its merit and clinical applicability.

Fusion Gene Detection in Prostate Cancer Samples Enhances the Prediction of Prostate Cancer Clinical Outcomes from Radical Prostatectomy through Machine Learning in a Multi-Institutional Analysis

Prostate cancer remains one of the most fatal malignancies in men in the United States. Predicting the course of prostate cancer is challenging given that only a fraction of prostate cancer patients experience cancer recurrence after radical prostatectomy or radiation therapy. This study examined the expressions of 14 fusion genes in 607 prostate cancer samples from the University of Pittsburgh, Stanford University, and the University of Wisconsin-Madison. The profiling of 14 fusion genes was integrated with Gleason score of the primary prostate cancer and serum prostate-specific antigen level to develop machine-learning models to predict the recurrence of prostate cancer after radical prostatectomy. Machine-learning algorithms were developed by analysis of the data from the University of Pittsburgh cohort as a training set using the leave-one-out cross-validation method. These algorithms were then applied to the data set from the combined Stanford/Wisconsin cohort (testing set). The results showed that the addition of fusion gene profiling consistently improved the prediction accuracy rate of prostate cancer recurrence by Gleason score, serum prostate-specific antigen level, or a combination of both. These improvements occurred in both the training and testing cohorts and were corroborated by multiple models.

Diagnostic Efficiency of Pan-Immune-Inflammation Value to Predict Prostate Cancer in Patients with Prostate-Specific Antigen between 4 and 20 ng/mL

INTRODUCTION

To evaluate the predictive value of the pan-immune-inflammation value (PIV) and other systemic inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), for prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in patients with a prostate-specific antigen (PSA) value between 4 and 20 ng/mL.

PATIENTS AND METHODS

The clinical data of 319 eligible patients who underwent prostate biopsies in our hospital from August 2019 to June 2022 were retrospectively analyzed. CSPCa was defined as a "Gleason grade group of ≥2". A univariable logistic regression analysis and multivariable logistic regression analysis were conducted to analyze the association between the PIV, SII, MLR, and PCa/CSPCa. For the inflammatory indicators included in the multivariable logistic regression analysis, we constructed models by combining the separate inflammatory indicator and other significant predictors and compared the area under the curve (AUC). A nomogram based on the PIV for PCa was developed.

RESULTS

We included 148 PCa patients (including 127 CSPCa patients) and 171 non-PCa patients in total. The patients with PCa were older, had higher MLR, SII, PIV, and total PSA (TPSA) values, consumed more alcohol, and had lower free/total PSA (f/T) values than the other patients. Compared with the non-CSPCa group, the CSPCa group had higher BMI, MLR, PIV, TPSA values, consumed more alcohol, and had lower f/T values. The univariable regression analysis showed that drinking history, higher MLR, PIV, and TPSA values, and lower f/T values were independent predictors of PCa and CSPCa. The AUC of the PIV in the multivariable logistic regression model was higher than those of the MLR and SII. In addition, the diagnostic value of the PIV + PSA for PCa was better than the PSA value. However, the diagnostic value for CSPCa was not significantly different from that of using PSA alone, while the AUC of the PIV + PSA was higher than the individual indicator of the PSA value.

CONCLUSIONS

Our study suggests that for the patients who were diagnosed with PSA values between 4 and 20 ng/mL, the PIV and MLR are potential indicators for predicting PCa and CSPCa. In addition, our study indicates that the new inflammatory index PIV has clinical value in the diagnosis of PCa and CSPCa.

Preoperative red cell distribution width is associated with postoperative lymphovascular invasion in prostate cancer patients treated with radical prostatectomy: A retrospective study

PURPOSE

To investigate the relationship between baseline clinicopathological and laboratory variables especially hematological parameters and lymphovascular invasion (LVI) in patients who underwent radical prostatectomy (RP).

METHODS

We retrospectively evaluated 348 prostate cancer (PCa) patients who underwent RP in our center between May 2018 and June 2021. We divided them into non-LVI and LVI groups based on LVI status, and compared clinicopathological characteristics between non-LVI and LVI groups. Clinicopathological parameters including age, body mass index (BMI), history of hypertension and diabetes mellitus, neoadjuvant hormonal therapy (NHT), pathological stage T (pT) and lymph node status (pN), ISUP (international society of urological pathology) grade, positive surgical margin (PSM) rate, and hematological parameters containing prostate-specific antigen (PSA), whole blood parameters and inflammatory indexes were collected. The association between the clinicopathological parameters and the presence of LVI was identified by multivariate logistic regression analysis.

RESULTS

The pathological results of the RP specimen consisted of 53 (15.2%) patients with LVI and 295 (84.8%) cases without LVI. The level of PSA, percentages of advanced pT and grade, pN1, and PSM were significantly higher in the LVI group when compared with the non-LVI counterpart (p<0.001, p<0.001, p<0.001, p<0.001, p=0.007, respectively). Among the whole blood parameters, only red cell distribution width (RDW) was significantly different (41.2 ± 2.5 vs. 42.1 ± 3.1, p=0.035). Multivariate regression analysis demonstrated that RDW and NHT were negatively correlated with the presence of LVI (OR = 0.870, p=0.024; OR = 0.410, p=0.025), while PSA, ISUP, and pT were positively correlated with the presence of LVI (OR=1.013, p=0.005; OR =1.589, p=0.001; OR=1.655, p=0.008) after adjusting for confounding factors.

CONCLUSIONS

RDW rather than other whole blood parameters was independently and negatively associated with the presence of LVI in PCa patients, suggesting that RDW might play an essential role in PCa invasion.

Lymphocyte-to-monocyte ratio is a predictor of clinically significant prostate cancer at prostate biopsy

BACKGROUND

Inflammation plays critical roles at different stages of carcinogenesis and cancer progression. Several previous studies showed conflicting results for the predictive role of systemic inflammation markers in the detection of clinically significant prostate cancers (CSPCs). We aimed to determine the predictive roles of lymphocyte-to-monocyte (LMR) and eosinophil-to-lymphocyte ratios (ELR) in the detection of CSPC at standard 12-core transrectal ultrasound-guided prostate biopsy (12-core-TRUS-Bx) using our large-cohort database.

METHODS

Clinical and pathological data of a total of 1740 men, who underwent initial standard 12-core TRUS-Bx, were analyzed. LMR and ELR were calculated from the prebiopsy complete blood count. Definitions of CSPC, LMR, and ELR were "Gleason grade group ≥2," "the lymphocyte counts/the monocyte counts," and "the eosinophil counts/the lymphocyte counts," respectively.

RESULTS

Median (interquartile range) of serum prostate-specific antigen (PSA) level and prostatic volume before TRUS-Bx were 7.59 (5.02-13.12) ng/ml and 38.2 (29.0-52.9) ml, respectively. Benign prostatic lesions, clinically insignificant prostate cancers (CIPCs), and CSPCs were detected in 1179 (67.8%), 180 (10.3%), and 381 (21.9%) patients, respectively. The patients with CSPCs had older age, a higher prevalence of diabetes mellitus or hypertension, a higher rate of digital rectal examination abnormality, higher serum PSA level, lower serum testosterone level, and lower LMR than those with benign lesions or CIPCs. However, there was no difference in ELR among the three (benign lesions, CIPCs and CSPCs). In all the patients, multivariate regression analysis showed that lower LMR was an independent predictor of CSPCs compared with ELR. In the subset of men with prostate volume ≥39.3 ml, lower LMR was an independent predictor of CSPCs compared with ELR. In the subset of men with prostate volume <39.3 ml, men with lower LMR showed the tendency of having a higher probability of CSPCs without any statistical significance on the contrary to ELR.

CONCLUSIONS

Our data indicate that LMR can play an independent predictive role in the detection of CSPCs at initial 12-core-TRUS-Bx compared with ELR. The predictive role of the LMR appears to be significant for men with larger prostate volume rather than those with smaller prostate volume.

Pre-treatment red blood cell distribution width as a predictor of clinically significant prostate cancer

OBJECTIVE

The present study aims to assess the relationship between red blood cell distribution width (RDW) and clinically significant prostate cancer (csPCa).

METHODS

A total of 458 patients with prostate-specific antigen (PSA) ≤ 10 ng/ml, who subsequently underwent 11-core transperineal template-guided prostate biopsy from June 15, 2015 to November 24, 2020, were included in the present study. Receiver-operating characteristic (ROC)-derived area under the curve analysis was performed to evaluate the predictive accuracy. In addition, univariate and multivariate logistic regression analysis was carried out to determine the association between RDW and csPCa detection.

RESULTS

A total of 89 patients were diagnosed with csPCa, and these patients presented with higher median RDW. The optimal RDW cut-off was set at 12.35%, which gained the maximal Yuden's index. In addition to csPCa, RDW demonstrated a positive correlation with age (r = 0.210, P < 0.001). It was observed that RDW was independent of prostate-specific antigen density for csPCa detection. Compared with the low-RDW group, patients in the high-RDW group had a 1.586-fold increased risk of being diagnosed with csPCa (OR = 2.586, P = 0.007). In the ROC analysis, the accuracy level increased by 3.1% for the prediction of csPCa, when RDW was added to the multivariate logistic model.

CONCLUSION

A high-RDW value is an independent risk factor for csPCa detection. However, more large-scale studies are needed to confirm these findings. If validated, RDW can become an inexpensive, non-invasive, and convenient indicator for csPCa prediction.