Predicting the diagnosis of prostate cancer with a scoring system based on novel biomarkers

Investigation of association between clinically significant prostate cancer, obesity and platelet to-lymphocyte ratio and neutrophil -to-lymphocyte ratio

INTRODUCTION

Several blood markers of inflammation are elevated in prostate cancer (PCa) and have prognostic value. Little is known about the relationship between these markers, PCa, and other factors associated with chronic inflammation, such as smoking and obesity. We analyzed the interaction between neutrophil and platelet counts indexed to lymphocyte count (NLR and PLR, resp.) and clinically significant PCa (csPCa), accounting for the potential confounding factors of systemic inflammation.

METHODS

NLR and PLR were evaluated in a multicenter prospective study in 443 patients. CsPCa was defined as a Gleason ≥ 4 + 3. Differences between patients with csPCa and non-csPCA were evaluated using the chi-square test, analysis of variance or the Kruskal-Wallis test. Multivariable logistic regression analysis adjusted for smoking, hypertension, diabetes, and cardiovascular disease, and in separate models, either body mass index or waist-to-hip ratio was used to characterize the relationship between inflammation and csPCa.

RESULTS

None of the factors such as plateletcrit, NLR, and PLR were significantly different between patients with csPCa or non-significant PCa. After adjustment, there was no association between PLR, NLR, plateletcrit or platelet count and csPCa. In an exploratory analysis, there was no association between markers of inflammation and PSA levels > 10 ng/mL. When testing different NLR cutoffs to predict csPCa in ROC analysis, none reached a clinically meaningful value.

CONCLUSION

In contrast to previous studies, we found no significant association between easily available blood markers of inflammation and indices of PCa aggressiveness. Further research is required to determine whether inflammation promotes PCa. (ClinicalTrials.gov: NCT03127631. Date of registration: April 25, 2017.

Development and validation of a nomogram incorporating multi-parametric MRI and hematological indicators for discriminating benign from malignant central prostatic nodules: a retrospective analysis

BACKGROUND

Prostate cancer poses a significant risk to men's health. In this study, a model for differentiating benign and malignant nodules in the central region of the prostate was constructed by combining multi-parametric MRI and hematological lab values.

METHODS

This retrospective study analyzed the data acquired from Lianyungang First People's Hospital and The Second Affiliated Hospital of Hainan Medical College from January 2018 to December 2021. We included 310 MRI-confirmed prostatic nodule patients. The data were split into a training set (260 cases) and an external validation set (50 cases), with the latter exclusively from The Second Affiliated Hospital of Hainan Medical College to test the model's generalizability. Univariate and multivariate logistic regression identified critical measurements for differentiating prostate cancer (PCa) from benign prostatic hyperplasia (BPH), which were then integrated into a nomogram model.

RESULTS

The key indicators determined by multivariate logistic regression analysis included apparent diffusion coefficient (ADC), standard deviation (StDev), neutrophil to lymphocyte ratio (NLR), and prostate specific antigen (PSA). The nomogram's performance, as indicated by the area under the curve (AUC), was 0.844 (95% CI: 0.811-0.938) in the training set and 0.818 (95% CI: 0.644-0.980) in the external validation set. Calibration and decision curves demonstrated that the nomogram was well-calibrated and could serve as an effective tool in clinical practice.

CONCLUSION

The nomogram model based on ADC, StDev, NLR and PSA may be helpful to identify PCa and BPH.

The influence of lifestyle changes (diet, exercise and stress reduction) on prostate cancer tumour biology and patient outcomes: A systematic review

Background

The mostly indolent natural history of prostate cancer (PCa) provides an opportunity for men to explore the benefits of lifestyle interventions. Current evidence suggests appropriate changes in lifestyle including diet, physical activity (PA) and stress reduction with or without dietary supplements may improve both disease outcomes and patient's mental health.

Objective

This article aims to review the current evidence on the benefits of all lifestyle programmes for PCa patients including those aimed at reducing obesity and stress, explore their affect on tumour biology and highlight any biomarkers that have clinical utility.

Evidence acquisition

Evidence was obtained from PubMed and Web of Science using keywords for each section on the affects of lifestyle interventions on (a) mental health, (b) disease outcomes and (c) biomarkers in PCa patients. PRISMA guidelines were used to gather the evidence for these three sections (15, 44 and 16 publications, respectively).

Evidence synthesis

For lifestyle studies focused on mental health, 10/15 demonstrated a positive influence, although for those programmes focused on PA it was 7/8. Similarly for oncological outcomes, 26/44 studies demonstrated a positive influence, although when PA was included or the primary focus, it was 11/13. Complete blood count (CBC)-derived inflammatory biomarkers show promise, as do inflammatory cytokines; however, a deeper understanding of their molecular biology in relation to PCa oncogenesis is required (16 studies reviewed).

Conclusions

Making PCa-specific recommendations on lifestyle interventions is difficult on the current evidence. Nevertheless, notwithstanding the heterogeneity of patient populations and interventions, the evidence that dietary changes and PA may improve both mental health and oncological outcomes is compelling, especially for moderate to vigorous PA. The results for dietary supplements are inconsistent, and although some biomarkers show promise, significantly more research is required before they have clinical utility.

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.

Pathological diagnostic nomograms for predicting malignant histology and unfavorable pathology in patients with endophytic renal tumor

Purpose

To develop and validate nomograms for pre-treatment prediction of malignant histology (MH) and unfavorable pathology (UP) in patients with endophytic renal tumors (ERTs).

Methods

We retrospectively reviewed the clinical information of 3245 patients with ERTs accepted surgical treatment in our center. Eventually, 333 eligible patients were included and randomly enrolled into training and testing sets in a ratio of 7:3. We performed univariable and multivariable logistic regression analyses to determine the independent risk factors of MH and UP in the training set and developed the pathological diagnostic models of MH and UP. The optimal model was used to construct a nomogram for MH and UP. The area under the receiver operating characteristics (ROC) curves (AUC), calibration curves and decision curve analyses (DCA) were used to evaluate the predictive performance of models.

Results

Overall, 172 patients with MH and 50 patients with UP were enrolled in the training set; and 74 patients with MH and 21 patients with UP were enrolled in the validation set. Sex, neutrophil-to-lymphocyte ratio (NLR), R score, N score and R.E.N.A.L. score were the independent predictors of MH; and BMI, NLR, tumor size and R score were the independent predictors of UP. Single-variable and multiple-variable models were constructed based on these independent predictors. Among these predictive models, the malignant histology-risk nomogram consisted of sex, NLR, R score and N score and the unfavorable pathology-risk nomogram consisted of BMI, NLR and R score performed an optimal predictive performance, which reflected in the highest AUC (0.842 and 0.808, respectively), the favorable calibration curves and the best clinical net benefit. In addition, if demographic characteristics and laboratory tests were excluded from the nomograms, only the components of the R.E.N.A.L. Nephrometry Score system were included to predict MH and UP, the AUC decreased to 0.781 and 0.660, respectively (P=0.001 and 0.013, respectively).

Conclusion

In our study, the pathological diagnostic models for predicting malignant and aggressive histological features for patients with ERTs showed outstanding predictive performance and convenience. The use of the models can greatly assist urologists in individualizing the management of their patients.