The modified prostate health index (PHI) outperforms PHI density in the detection of clinical prostate cancer within the PSA grey zone

An online clustering algorithm predicting model for prostate cancer based on PHI-related variables and PI-RADS in different PSA populations

BACKGROUND AND AIM

Prostate cancer is the most common male malignancy. Current diagnostic methods using single TPSA and PHI lack specificity. Some researches have created nomograms for predicting risk, but these are not easily visualized. Our study aims to find the best negative predictive value (NPV) for PHI, then build a clustering model to display prostate cancer risk categories, particularly useful for patients with PSA > 20 and be actually applied in clinical work.

METHOD

We collected 708 patients in the training cohort and 143 in the validation cohort, divided into three groups based on their PSA levels. Next, we determined optimal and customized PHI cut-off values, calculated NPV and PPV, and selected logistic regression as the best method among several machine-learning algorithms. Subsequently, the significant variables were identified, and then a clustering algorithm was constructed. Finally, the model was validated and made available online for further clinical application.

RESULTS

The Optimal PHI cut-off lower limits for PSA > 4, PSA4-20, PSA > 20 subgroups were 23.85, 24.35, and 40.75, with upper limits of 142.9, 143, and 135.6, respectively. The clustering model of the optimal cohort for PSA > 4 and PSA 4-20 sub-groups showed a superior Silhouette coefficients of 0.433 and 0.526 than that of the customized PHI cohort (0.432, 0.452). The PSA > 20 subgroup owned the highest Silhouette coefficient of 0.572. The validation cohort showed AUC values of 0.761, 0.823, 0.833 for these 3 sub-groups, with accuracy rates of 88.81%, 90.38%, and 82.05%.

CONCLUSION

In conclusion, our clustering model effectively categorizes patients into distinct risk groups with clear visualization and has demonstrated stability and reliability in the validation cohort, potentially aiding in early diagnosis of prostate cancer in clinical practice.

Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification

Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy.

Bioinformatics analysis reveals that CBX2 promotes enzalutamide resistance in prostate cancer

Enzalutamide (Enz) is commonly utilized as the initial treatment strategy for advanced prostate cancer (PCa). However, a notable subset of patients may experience resistance to Enz, resulting in reduced effectiveness. Utilizing Gene Expression Omnibus (GEO) databases, we identified CBX2 as a crucial factor in mediating resistance to Enz, primarily due to its inhibitory effect on the P53 signaling pathway. Silencing of CBX2 using small interfering RNA (siRNA) led to elevated levels of P53 expression in LNCaP cells. This indicates that CBX2 may have a critical effect on PCa Enz resistance and could serve as a promising therapeutic target for individuals with Enz resistance.

Prostate cancer: Novel genetic and immunologic biomarkers

Prostate cancer (PCa) is considered one of the most prevalent male malignancies worldwide with a global burden estimated to increase over the next two decades. Due to significant mortality and debilitation of survival, early diagnosis has been described as key. Unfortunately, current diagnostic serum-based strategies have low specificity and sensitivity. Histologic examination is invasive and not useful for treatment and monitoring purposes. Hence, a plethora of studies have been conducted to identify and validate an efficient noninvasive approach in the diagnosis, staging, and prognosis of PCa. These investigations may be categorized as genetic (non-coding biomarkers and gene markers), immunologic (immune cells, interleukins, cytokines, antibodies, and auto-antibodies), and heterogenous (PSA-related markers, PHI-related indices, and urinary biomarkers) subgroups. This review examines current approaches and potential strategies using biomarker panels in PCa.

Clinical value of prostate health index as an indicator for recommending magnetic resonance imaging in patients with gray-zone prostate-specific antigen level

PURPOSE

To evaluate the usefulness of prostate health index (PHI) as an indicator for recommending magnetic resonance imaging (MRI) in patients with prostate-specific antigen (PSA) gray zone level < 10 ng/mL.

METHODS

443 patients who underwent prostate biopsy (PB) after serum PHI test and MRI between April 2019 and December 2022 were enrolled. For patients with visible lesion on MRI with Prostate Imaging Reporting and Data System Score (PI-RADS) ≥ 3, MRI-targeted PB was performed in addition to systematic 12-core PB.

RESULTS

The optimal cutoff value of PHI for predicting PI-RADS ≥ 3 lesions was 39.6, which was significantly associated with overall prostate cancer (OR 3.07, p = 0.018) and clinically significant prostate cancer (csPCa) (OR 4.15, p = 0.006) at MRI-targeted PB cores. When MRI was restricted to patients with PHI ≥ 39.6 alone, 28.7% of unnecessary MRI could be saved at the cost of missing 13.6% of csPCa. When omitting MRI for patients with PHI < 39.6 and PSAD < 0.12 ng/mL2, unnecessary MRI could be reduced by 20.1% with the risk of missing 6.2% of csPCa. With addition of systematic PB, 21.0% of patients with negative MRI-targeted PB were diagnosed as csPCa.

CONCLUSIONS

For patients in PSA gray zone, PHI of 39.6 might be an indicator for MRI and further MRI-targeted PB in additional to PSAD of 0.12 ng/mL2, reducing 20.1% of unnecessary MRI with the minimal risk of missing 6.2% of csPCa. To maximize csPCa detection, combining both MRI-targeted and systematic PB should be also considered.

Prostate health index (PHI) as an accurate prostate cancer predictor

PURPOSE

This study aims to compare the ability of the PHI versus tPSA test to predict the presence of PCa in our population.

METHODS

A prospective observational study was performed. We included patients with tPSA ≥ 2.5 ng/ml, biopsy naïve or previous negative biopsy, undergoing a blood test, which includes tPSA, fPSA, and p2PSA, and a prostate biopsy between March 2019 and March 2022. Patients with PCa found in the biopsy-Group A-were compared with patients with a negative biopsy result-Group B. Diagnostic accuracy of tPSA and PHI was assessed by receiver operating characteristic [ROC] curves and logistic regression.

RESULTS

140 men were included. Fifty-seven (40.7%) had a positive prostate biopsy result (Group A), and 83 (59.3%) had a negative biopsy result (Group B). The mean age was similar in both groups (mean ± standard deviation), 66.86 ± 6.61 years. No difference was found in the tPSA value between the groups (Group A PSA: 6.11 ng/ml (3.56-17.01); Group B: 6.42 ng/ml (2.46-19.45), p = 0.41). The mean value of PHI was statistically different between groups (Group A 65.50 (29-146) vs. Group B 48 (16-233), p = 0.0001). The area under the curve 0.44 for tPSA and 0.77 for PHI. The multivariate logistic regression model applied to PHI showed a significant increase in its predictive accuracy: 72.14% in the model without PHI, 76.09% with PHI.

CONCLUSION

The PHI test improves PCa detection compared to tPSA in our population.

Serum multi-cytokines screening identifies TRAIL and IL-10 as probable new biomarkers for prostate health index diagnostic utility adjustment in grey zone aggressive prostate cancer detection: A single-center data in China

Objective

To identify less invasive and easily applicable serum cytokine-derived biomarkers which contribute to the diagnostic utility and risk assessment ability of the prostate health index (PHI) based multivariable model in grey zone aggressive prostate cancer (AG PCa) early detection.

Methods

Serum 45 cytokines screening was performed in a small training cohort consisting of 10 sera by Luminex liquid array-based multiplexed immunoassays and identified TRAIL and IL-10 as new biomarkers for PHI diagnostic utility adjustment for further validation with a multivariable predictive model in a cohort including 79 aggressive prostate cancer patients and 209 benign prostatic hyperplasia or indolent PCa patients within the PSA grey zone.

Results

TRAIL and IL-10 were identified as potential serum biomarkers for AG PCa detection by the result of multi-cytokines screening in the univariate analysis, while multivariable logistic regression confirmed the AUC of the full risk predictive model (0.915) including tPSA, fPSA, PHI, TRAIL, and IL-10 was higher than various diagnostic strategies. DCA suggested a superior net benefit and indicated a good discriminative ability of the full risk model consistently with the result of the nomogram.

Conclusion

We suggest a significant advantage for the PHI-based multivariate combinations of serum TRAIL and IL-10 comparing to PHI or other serum-derived biomarkers alone in the detection and risk stratification of grey zone AG PCa.

Modified Prostate Health Index Density Significantly Improves Clinically Significant Prostate Cancer (csPCa) Detection

Background

Early screening of clinically significant prostate cancer (csPCa) may offer opportunities in revolutionizing the survival benefits of this lethal disease. We sought to introduce a modified prostate health index density (mPHI) model using imaging indicators and to compare its diagnostic performance for early detection of occult onset csPCa within the prostate-specific antigen (PSA) gray zone with that of PHI and PHID.

Methods and Participation

Between August 2020 and January 2022, a training cohort of 278 patients (total PSA 4.0-10.0 ng/ml) who were scheduled for a prostate biopsy were prospectively recruited. PHI and PHID were compared with mPHI ( LD TRD × APD × TPV × PHI ) for the diagnosis performance in identifying csPCa. Pathology outcomes from systematic prostate biopsies were considered the gold standard.

Results

This model was tested in a training cohort consisting of 73 csPCa, 14 non-clinically significant prostate cancer(non-csPCa), and 191 benign prostatic hyperplasia (BPH) samples. In the univariate analysis for the PSA gray zone cohort, for overall PCa, the AUC of mPHI (0.856) was higher than PHI (0.774) and PHID (0.835). For csPCa, the AUC of mPHI (0.859) also surpassed PHI (0.787) and PHID (0.825). For detection of csPCa, compared with lower specificities from PHI and PHID, mPHI performed the highest specificity (76.5%), by sparing 60.0% of unnecessary biopsies at the cost of missing 11 cases of csPCa. The mPHI outperformed PHI and PHID for overall PCa detection. In terms of csPCa, mPHI exceeds diagnostic performance with a better net benefit in decision curve analysis (DCA) compared with PHI or PHID.

Conclusions

We have developed a modified PHI density (mPHI) model that can sensitively distinguish early-stage csPCa patients within the PSA gray zone.

Clinical Trial Registration

ClinicalTrials.gov, NCT04251546.