Prostate Health Index Density Outperforms Prostate Health Index in Clinically Significant Prostate Cancer Detection

Serum (-2)proPSA/freePSAratio, (-2)proPSA/freePSA density, prostate health index, and prostate health index density as clues to reveal postoperative clinically significant prostate cancer in men with prostate-specific antigen 2-10 ng/mL

BACKGROUND

There is a strong clinical need to fill the gap of identifying clinically significant prostate cancer (csPCa) in men with prostate-specific antigen (PSA) gray zone values. Promising, but not definitive results have been obtained using PSA derivatives such as prostate health index (PHI) and PHI density (PHID) and the percentage (-2)proPSA/free PSA (%p2PSA/fPSA). Thus, this study aimed to compare the diagnostic value of PHI, PHID, %proPSA/fPSA, and (-2)proPSA/freePSA density (-2pPSA/fPSAD) for csPCa in the patients with PSA within 2-10 ng/mL.

METHODS

Serum samples and clinicopathological features were prospectively collected from 142 patients who underwent robot-assisted radical prostatectomy  between September 2021 and December 2023. According to the inclusion criteria, the patients with total PSA  within 2 and 10 ng/mL and negative or suspicious digital rectal examination  were enrolled. We used two different classifications for csPCa: 1) patients with Gleason score (GS) ≥ 7(4 + 3) and 2) patients with GS ≥ 7(3 + 4). The receiver operating characteristic curves and the area under the curve (AUC) values were used to assess the diagnostic performance.

RESULTS

Of the 142 men included, 116 (82%) patients were diagnosed with csPCa as GS ≥ 3 + 4 and 107 (75%) defined as csPCa as GS ≥ 7(4 + 3), respectively. We found that p2PSA/fPSA, p2PSA/fPSAD, PHI, and PHID were significantly higher in csPCa classified as GS ≥ 7(3 + 4) as well as GS ≥ 7(4 + 3), with p-values 0.027, 0.054, 0.0016, and 0.0027, respectively. AUCs of the analyzed variables were higher when used to predict csPCa as GS ≥ 6 compared to csPCa as GS ≥7(4 + 3), with an AUC equal, respectively, to 0.679 (95% CI: 0.571-0.786), 0.685 (95% CI: 0.571-0.799), 0.737 (95% CI: 0.639-0.836), and 0.736 (95% CI: 0.630-0.841) in the first subgroup and with an AUC equal, respectively, to 0.653 (95% CI: 0.552-0.754), 0.665 (95% CI: 0.560-0.770), 0.668 (95% CI: 0.568-0.769), and 0.670 (95% CI: 0.567-0.773) in the second, respectively. Both PHID and p2PSA/fPSAD allowed improvement in the diagnostic accuracy with respect to PHI and p2PSA/fPSA ratio, however the differences were not statistically significant (p = 0.409, 0.180 for csPCa as G ≥ Gleason grade (GG) 2 and 0.558 and 0.087 for csPCa as G ≥ GG3, respectively). We found that PHI, PHID, p2PSA/fPSA ratio, and p2PSA/fPSAD showed higher sensitivity, specificity, and positive predictive value when used to predict csPCa as GG ≥ 2, whereas negative predictive value of all four parameters was higher when used to predict GG ≥ 3.

CONCLUSIONS

In men with a PSA level between 2 and 10 ng/mL, PHI and PHID, p2PSA/fPSA, and p2PSA/fPSAD showed good diagnostic performance for postoperative csPCa. However, PHID and p2PSA/fPSAD had a small advantage over PHI which needs to be further investigated for the reduction of unnecessary surgical interventions. This finding suggests that it could be a promising biomarker for making the treatment-decision strategy.

Diagnostic Accuracy of Liquid Biomarkers for Clinically Significant Prostate Cancer Detection: A Systematic Review and Diagnostic Meta-analysis of Multiple Thresholds

CONTEXT

Many liquid biomarkers have entered clinical practice with the praise to improve the detection of clinically significant prostate cancer (csPCa), helping avoid unnecessary prostate biopsies.

OBJECTIVE

We aimed to assess the diagnostic accuracy of multianalyte biomarkers for csPCa detection using multiple thresholds.

EVIDENCE ACQUISITION

A comprehensive literature search was done through PubMed, Web of Science, and Scopus in March 2023 for prospective and retrospective studies reporting the diagnostic performance of liquid biomarkers for detecting csPCa. The outcomes of interest were the diagnostic performance of liquid biomarkers for csPCa detection and identification of optimal thresholds for each biomarker.

EVIDENCE SYNTHESIS

Overall, 49 studies were eligible for this meta-analysis. Using each representative threshold based on the Youden Index, the pooled sensitivity and specificity for detecting csPCa were 0.85 and 0.37 for prostate cancer gene 3 (PCA3), 0.85 and 0.52 for prostate health index (PHI), 0.87 and 0.58 for four kallikrein (4K), 0.82 and 0.56 for SelectMDx, 0.85 and 0.54 for ExoDx, and 0.82 and 0.59 for mi prostate score (MPS), respectively. The diagnostic odds ratio was highest for 4K (8.84), followed by MPS (7.0) and PHI (6.28). According to the meta-analysis incorporating multiple thresholds, the corresponding sensitivity was 0.77 for 4K, 0.69 for PHI, and 0.63 for PCA3; specificity was 0.72 for PHI, 0.70 for 4K, and 0.69 for PCA3.

CONCLUSIONS

Regarding the detection of csPCa, 4K had the highest diagnostic performance among the commercial liquid biomarkers. Based on the optimal thresholds calculated by the present meta-analysis, 4K had the highest sensitivity and PHI had the highest specificity for detecting csPCa. Nevertheless, clinical decision-making requires combination strategies between liquid and imaging biomarkers.

PATIENT SUMMARY

Novel biomarkers for prostate cancer detection were useful for more accurate diagnosis of clinically significant prostate cancer to avoid unnecessary biopsies.

Prostate health index density aids the diagnosis of prostate cancer detected using magnetic resonance imaging targeted prostate biopsy in Taiwanese multicenter study

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) followed by MRI-targeted prostate biopsy is the current standard for diagnosing prostate cancer (PCa). However, studies evaluating the value of biomarkers, including prostate health index (PHI) and its derivatives using this method are limited. We aimed to investigate the efficacy of PHI density (PHID) in guiding MRI-targeted prostate biopsies to identify clinically significant PCas (csPCa).

METHODS

The multicenter prospectively registered prostate biopsy database from three medical centers in Taiwan included patients with PHI and MRI-targeted and/or systematic prostate biopsies. We assessed the required values of prostate-specific antigen (PSA), prostate volume, PHI, PHID, and Prostate Imaging Reporting & Data System (PI-RADS) score using multivariable analyses, receiver operating characteristic curve analysis, and decision curve analyses (DCA). csPCa was defined as the International Society of Urological Pathology Gleason group ≥2 PCa, with an emphasis on reducing unwarranted biopsies.

RESULTS

The study cohort comprised 420 individuals. Diagnoses of PCa and csPCa were confirmed in 62.4% and 47.9% of the participants, respectively. The csPCa diagnosis rates were increased with increasing PI-RADS scores (20.5%, 44.2%, and 73.1% for scores 3, 4, and 5, respectively). Independent predictors for csPCa detection included PHI, prostate volume, and PI-RADS scores of 4 and 5 in multivariable analyses. The area under the curve (AUC) for csPCa of PHID (0.815) or PHI (0.788) was superior to that of PSA density (0.746) and PSA (0.635) in the entire cohort, and the superiority of PHID (0.758) was observed in PI-RADS 3 lesions. DCA revealed that PHID achieved the best net clinical benefit in PI-RADS 3-5 and 4/5 cases. Among PI-RADS 3 lesions, cutoff values of PHID 0.70 and 0.43 could eliminate 51.8% and 30.4% of omitted biopsies, respectively.

CONCLUSION

PHI-derived biomarkers, including PHID, performed better than other PSA-derived biomarkers in diagnosing PCa in MRI-detected lesions.

Liquid Biomarkers in Prostate Cancer Diagnosis: Current Status and Emerging Prospects

Prostate cancer (PCa) is a major health concern that necessitates appropriate diagnostic approaches for timely intervention. This review critically evaluates the role of liquid biopsy techniques, focusing on blood- and urine-based biomarkers, in overcoming the limitations of conventional diagnostic methods. The 4Kscore test and Prostate Health Index have demonstrated efficacy in distinguishing PCa from benign conditions. Urinary biomarker tests such as PCa antigen 3, MyProstateScore, SelectMDx, and ExoDx Prostate IntelliScore test have revolutionized risk stratification and minimized unnecessary biopsies. Emerging biomarkers, including non-coding RNAs, circulating tumor DNA, and prostate-specific antigen (PSA) glycosylation, offer valuable insights into PCa biology, enabling personalized treatment strategies. Advancements in non-invasive liquid biomarkers for PCa diagnosis may facilitate the stratification of patients and avoid unnecessary biopsies, particularly when PSA is in the gray area of 4 to 10 ng/mL.

Based on PI-RADS v2.1 combining PHI and ADC values to guide prostate biopsy in patients with PSA 4-20 ng/mL

PURPOSE

The study aimed to investigate the diagnostic accuracy of prostate health index (PHI) and apparent diffusion coefficient (ADC) values in predicting prostate cancer (PCa) and construct a nomogram for the prediction of PCa and clinically significant PCa (CSPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) three lesions cohort.

METHODS

This study prospectively enrolled 301 patients who underwent multiparametric magnetic resonance (mpMRI) and were scheduled for prostate biopsy. The receiver operating characteristic curve (ROC) was performed to estimate the diagnostic accuracy of each predictor. Univariable and multivariable logistic regression analysis was conducted to ascertain hidden risk factors and constructed nomograms in PI-RADS three lesions cohort.

RESULTS

In the whole cohort, the area under the ROC curve (AUC) of PHI is relatively high, which is 0.779. As radiographic parameters, the AUC of PI-RADS and ADC values was 0.702 and 0.756, respectively. The utilization of PHI and ADC values either individually or in combination significantly improved the diagnostic accuracy of the basic model. In PI-RADS three lesions cohort, the AUC for PCa was 0.817 in the training cohort and 0.904 in the validation cohort. The AUC for CSPCa was 0.856 in the training cohort and 0.871 in the validation cohort. When applying the nomogram for predicting PCa, 50.0% of biopsies could be saved, supplemented by 6.9% of CSPCa being missed.

CONCLUSION

PHI and ADC values can be used as predictors of CSPCa. The nomogram included PHI, ADC values and other clinical predictors demonstrated an enhanced capability in detecting PCa and CSPCa within PI-RADS three lesions cohort.

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study

PURPOSE

To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed.

MATERIALS AND METHODS

A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected prostate cancer with aged 40 to 85 years, and with PSA levels ranging from 2.5 to 10 ng/mL were analyzed.

RESULTS

Among 626 patients, 38.82% (243/626) and 22.52% (141/626) were diagnosed with prostate cancer and clinically significant prostate cancer, respectively. In the PSA 2.5 to 4 ng/mL group, the areas under the curve (AUCs) of the nomograms for overall prostate cancer and clinically significant prostate cancer were 0.796 (0.727-0.866; p<0.001), and 0.697 (0.598-0.795; p=0.001), respectively. In the PSA 4 to 10 ng/mL group, the AUCs of nomograms for overall prostate cancer and clinically significant prostate cancer were 0.812 (0.783-0.842; p<0.001), and 0.839 (0.810-0.869; p<0.001), respectively.

CONCLUSIONS

Even though external validations are necessary, a nomogram using PHI might improve the prediction of prostate cancer, reducing the need for prostate biopsies.

Comparison of Prostate-Specific Antigen and Its Density and Prostate Health Index and Its Density for Detection of Prostate Cancer

As the incidence of prostate cancer (PCa) has increased, screening based on prostate-specific antigen (PSA) has become controversial due to the low specificity of PSA. Therefore, we investigated the diagnostic performance of prostate health index (PHI) density (PHID) for the detection of PCa and clinically significant PCa (csPCa) compared to PSA, PSA density (PSAD), and PHI as a triaging test. We retrospectively reviewed 306 men who underwent prostate biopsy for PSA levels of 2.5 to 10 ng/mL between January 2020 and April 2023. Of all cohorts, 86 (28.1%) and 48 (15.7%) men were diagnosed with PCa and csPCa, respectively. In ROC analysis, the highest AUC was identified for PHID (0.812), followed by PHI (0.791), PSAD (0.650), and PSA (0.571) for PCa. A similar trend was observed for csPCa: PHID (AUC 0.826), PHI (AUC 0.796), PSAD (AUC 0.671), and PSA (0.552). When the biopsy was restricted to men with a PHID ≥ 0.56, 26.5% of unnecessary biopsies could be avoided; however, 9.3% of PCa cases and one csPCa case (2.1%) remained undiagnosed. At approximately 90% sensitivity for csPCa, at the given cut-off values of PHI ≥ 36.4, and PHID ≥ 0.91, 48.7% and 49.3% of unnecessary biopsies could be avoided. In conclusion, PHID had a small advantage over PHI, about 3.6%, for the reduction in unnecessary biopsies for PCa. The PHID and PHI showed almost the same diagnostic performance for csPCa detection. PHID can be used as a triaging test in a clinical setting to pre-select the risk of PCa and csPCa.

A prospective study of the prostate health index density and multiparametric magnetic resonance imaging in diagnosing clinically significant prostate cancer

PURPOSE

To evaluate the predictive performance of the prostate health index (PHI) and PHI density (PHID), for clinically significant prostate cancer (csPCa) in patients with a PI-RADS score ≤3.

MATERIALS AND METHODS

Patients tested for total prostate-specific antigen (tPSA, ≤100 ng/mL), free PSA (fPSA), and p2PSA at Peking University First Hospital were prospectively enrolled. Possible predictive factors of csPCa were analyzed using the receiver operating characteristic (ROC) curve. Results were expressed as area under the curve (AUC) with 95% confidence intervals (CI). The cutoff values of PHI and PHID were determined.

RESULTS

We enrolled 222 patients in this study. The prevalence of csPCa in the PI-RADS ≤3 subgroup (n=89) was 22.47% (20/89). Age, tPSA, F/T, prostate volume, PSA density, PHI, PHID, and PI-RADS score were significantly associated with csPCa. PHID (AUC: 0.829 [95% CI: 0.717-0.941]) was the best predictor of csPCa. PHID >0.956 was set as the threshold of suspicious csPCa with a sensitivity of 85.00% and a specificity of 73.91%, avoiding 94.44% of unnecessary biopsies but missing 15.00% csPCa. A threshold of PHI ≥52.83 showed the same sensitivity but a rather lower specificity of 65.22% that avoided 93.75% of unnecessary biopsies.

CONCLUSIONS

PHI and PHID have the best predictive performance of csPCa in patients with PI-RADS score ≤3. A threshold value of PHID ≥0.956 may be used as the criterion for biopsy in these patients.

Do PHI and PHI density improve detection of clinically significant prostate cancer only in the PSA gray zone?

OBJECTIVES

Prostate health index (PHI) is a predictive biomarker of positive prostate biopsy. The majority of evidence refers to its use in the PSA gray zone (4-10 ng/mL) and negative digital rectal exam (DRE). We aim to evaluate and compare the predictive accuracy of PHI and PHI density (PHId) with PSA, percentage of free PSA and PSA density, in a wider range of patients for the detection of clinically significant prostate cancer (csPCa).

METHODS

Multicenter prospective study that included patients suspicious of harboring prostate cancer. Non-probabilistic convenience sampling, where men who attended the urology consultation were tested for PHI before prostate biopsy. To evaluate and compare diagnostic accuracy AUC and decision curve analysis (DCA) were calculated. All these procedures were performed for the overall sample and the following subsamples: PSA < 4 ng/ml; PSA 4-10 ng/ml; PSA 4-10 ng/ml plus negative DRE and PSA > 10 ng/ml.

RESULTS

Among the 559 men included, 194 (34.7%) were diagnosed of csPCa. PHI and PHId outperfomed PSA in all subgroups. PHI best diagnostic performance was found in PSA 4-10 ng/ml with negative DRE (sensitivity 93.33, NPV 96.04). Regarding AUC, significant differences were found between PHId and PSA in the subgroup of PSA 4-10 ng/ml, whatever DRE status. In DCA, PHI density shows the highest net benefit.

CONCLUSIONS

PHI and PHId outperfom PSA in csPCa detection, not only in the PSA grey zone with negative DRE, but also in a wider range of PSA values. There is an urgent need of prospective studies to established a validated threshold and its incorporation in risk calculators.

Development of a novel nomogram for predicting clinically significant prostate cancer with the prostate health index and multiparametric MRI

INTRODUCTION

On prostate biopsy, multiparametric magnetic resonance imaging (mpMRI) and the Prostate Health Index (PHI) have allowed prediction of clinically significant prostate cancer (csPCa).

METHODS

To predict the likelihood of csPCa, we created a nomogram based on a multivariate model that included PHI and mpMRI. We assessed 315 males who were scheduled for prostate biopsies.

RESULTS

We used the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2) to assess mpMRI and optimize PHI testing prior to biopsy. Univariate analysis showed that csPCa may be identified by PHI with a cut-off value of 77.77, PHID with 2.36, and PI-RADS with 3 as the best threshold. Multivariable logistic models for predicting csPCa were developed using PI-RADS, free PSA (fPSA), PHI, and prostate volume. A multivariate model that included PI-RADS, fPSA, PHI, and prostate volume had the best accuracy (AUC: 0.882). Decision curve analysis (DCA), which was carried out to verify the nomogram's clinical applicability, showed an ideal advantage (13.35% higher than the model that include PI-RADS only).

DISCUSSION

In conclusion, the nomogram based on PHI and mpMRI is a valuable tool for predicting csPCa while avoiding unnecessary biopsy as much as possible.

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

Objective

The aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy.

Methods

A total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P < 0.05) were included in five machine learning algorithm models. The performance of the models was evaluated by receiver operating characteristic curves. Decision curve analysis (DCA) was performed to estimate the clinical utility of the models. Ten-fold cross-validation was applied in the training process.

Results

Prostate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined.

Conclusion

The RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

Prostate health index (PHI) as a reliable biomarker for prostate cancer: a systematic review and meta-analysis

OBJECTIVES

Prostate cancer (PCa) represents the second most common solid cancer in men worldwide. In the last decades, the prostate health index (PHI) emerged as a reliable biomarker for detecting PCa and differentiating between non-aggressive and aggressive forms. However, before introducing it in clinical practice, more evidence is required. Thus, we performed a systematic review and meta-analysis for assessing the diagnostic performance of PHI for PCa and for detecting clinically significant PCa (csPCa).

METHODS

Relevant publications were identified by a systematic literature search on PubMed and Web of Science from inception to January 11, 2022.

RESULTS

Sixty studies, including 14,255 individuals, met the inclusion criteria for our meta-analysis. The pooled sensitivity and specificity of PHI for PCa detection was 0.791 (95%CI 0.739-0.834) and 0.625 (95%CI 0.560-0.686), respectively. The pooled sensitivity and specificity of PHI for csPCa detection was 0.874 (95%CI 0.803-0.923) and 0.569 (95%CI 0.458-0.674), respectively. Additionally, the diagnostic odds ratio was 6.302 and 9.206, respectively, for PCa and csPCa detection, suggesting moderate to good effectiveness of PHI as a diagnostic test.

CONCLUSIONS

PHI has a high accuracy for detecting PCa and discriminating between aggressive and non-aggressive PCa. Thus, it could be useful as a biomarker in predicting patients harbouring more aggressive cancer and guiding biopsy decisions.