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    World J Mens Health. 2024 Jan;42(1):168-177. English.
    Published online Apr 10, 2023.
    https://doi.org/10.5534/wjmh.220223
    Copyright © 2024 Korean Society for Sexual Medicine and Andrology
    Original Article

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

    Jae Hoon Chung,1 Jeong Hyun Kim,2 Sang Wook Lee,2 Hongzoo Park,2 Geehyun Song,2 Wan Song,1 Minyong Kang,1 Hyun Hwan Sung,1 Hwang Gyun Jeon,1 Byong Chang Jeong,1 Seong IL Seo,1 Hyun Moo Lee,1 and Seong Soo Jeon1
      • 1Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
      • 2Department of Urology, Kangwon National University School of Medicine, Chuncheon, Korea.
    • Correspondence to: Jeong Hyun Kim. Department of Urology, Kangwon National University School of Medicine, 1 Gangwondaehak-gil, Chuncheon 24341, Korea. Tel: +82-33-258-2353, Fax: +82-33-258-2455, Email: urodr348@kangwon.ac.kr
      Correspondence to: Seong Soo Jeon. Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea. Tel: +82-2-3410-3558, Fax: +82-2-3410-6992, Email: seongsoo.jeon@samsung.com
    Received October 10, 2022; Revised January 31, 2023; Accepted February 05, 2023.

    This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Abstract

    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.

    Keywords
    Diagnosis; Nomogram; Prostate cancer; Risk

    INTRODUCTION

    Prostate-specific antigen (PSA), a representative biomarker for prostate cancer screening, diagnosis, and prognosis, has high sensitivity but low specificity [1]. Therefore, patients with PSA levels in the gray zone, ranging from 2–10 ng/mL, had to undergo unnecessary prostate biopsies [2]. Moreover, overtreatment of insignificant prostate cancer patients had also been reported [3, 4]. Even though prostate biopsy is a low-risk process with respect to possible complications, it is invasive and incurs a significant opportunity cost [5]. For these reasons, many biomarkers other than PSA have been developed to predict prostate cancer.

    Among various prostate cancer biomarkers, PSA isoforms and their derivatives are gaining interest owing to their high diagnostic and predictive values for significant prostate cancer [6, 7, 8]. These biomarkers, if developed efficiently, may help to avoid unnecessary prostate biopsy and reduce overtreatment of clinically insignificant prostate cancer. Among PSA isoform derivatives, [-2] proPSA (p2PSA), a subform of proPSA, is the most stable and promising prostate cancer-specific biomarker [9]. In addition, prostate health index (PHI), arithmetic derivatives of p2PSA, were more accurate in diagnosing prostate cancer than p2PSA or % free PSA (%fPSA) while being strongly correlated with high-grade cancer [10, 11, 12].

    However, PHI is not a perfect biomarker for prostate cancer, and the effectiveness of analyzing other predictive factors such as prostate volume along with PHI has been reported [13]. Many variables such as age, body mass index, biopsy history, familial history, history of administration of 5-alpha reductase inhibitors (5ARIs), and underlying disease are related to prostate cancer. Therefore, the need for a biopsy should be decided after a comprehensive evaluation of each patient.

    This study aimed to construct a nomogram that can predict the probability of prostate cancer by evaluating the patient’s PHI and other clinical parameters.

    MATERIALS AND METHODS

    1. Subjects

    From August 2018 to August 2021, we prospectively recruited and evaluated men over 40 who required a prostate biopsy at two hospitals. Patients with clinically suspected prostate cancer in the age range of 40–85 years who voluntarily signed the consent form for this clinical trial were included in the study. Patients who developed acute prostatitis in the 3 months preceding prostate biopsy, those with PSA level less than 2.5 ng/mL or greater than 10 ng/mL, those with blood diseases such as leukemia or hemophilia, or patients with a comorbidity that was judged unsuitable for study participation were excluded from the study.

    2. Study design

    Patient blood was sampled prior to prostate biopsy. Age, underlying disease, familial history, body mass index, biopsy history, and physical status of the patients were collected blood samples were centrifuged within 3 hours of sampling, and the serum was stored at -80℃. p2PSA levels were measured using the Access 2 immunoassay kit (Beckman Coulter, Brea, CA, USA). Total PSA (tPSA) and fPSA levels were measured using a Access Hybritech assay (Beckman Coulter). PHI was calculated using the formula, PHI=[(p2PSA/fPSA)×(tPSA)1/2)].

    Extended sextant transrectal ultrasound-guided prostate biopsy was performed in all patients, and an additional target biopsy was performed if necessary. Pathologic diagnosis was conducted by an experienced urogenital pathologist at each respective hospital. Significant prostate cancer was defined as prostate cancer with Gleason score of 7 or higher.

    3. Study endpoints

    The primary endpoint was to develop a nomogram using PHI to predict prostate cancer diagnosis. Secondary endpoints were to identify the PHI cutoff value.

    4. Sample size

    Considering the study as a pilot scheme of a prospective observational one, the sample size was calculated based on the judgment of the researcher and results of previous studies. For the study power (1-β) to be over 80% when α error is 5%, a sample size of at least 300 is required. Hence, the minimum number of enrolled patients should be 300. The statistical significance was secured by conducting the study on as many patients as possible.

    5. Statistical analysis

    Patients were analyzed after categorizing into two groups: the PSA 2.5–4 ng/mL and PSA 4–10 ng/mL groups. The two-sample t-test and Wilcoxon rank sum test were used for continuous variables, and chi-square and Fisher’s exact tests were used for categorical variables. Univariable logistic regression was used to determine the association of measured covariates with prostate cancer and clinically significant prostate cancer. The diagnostic ability of each parameter was assessed using the area under the curve (AUC) metric of the receiver operating characteristic (ROC). The PHI cutoff value was assessed by ROC curve analysis, and the cutoff value, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were estimated by Youden’s index method. Statistical analysis was executed using SAS version 9.4 (SAS Institute, Cary, NC, USA) and R 3.6.1 (Vienna, Austria; https://www.R-project.org). For analysis, pROC and rms packages in R was used. A two-sided p-value of <0.05 was considered significant.

    6. Ethics statement

    The study was performed in accordance with the applicable laws and regulations, good clinical practices, and ethical principles as described in the Declaration of Helsinki. The Institutional Review Board of Samsung Medical Center approved the present study (approval no. 2018-08-075). Each patient provided written informed consent for participation.

    RESULTS

    A total of 1,010 patients with suspected prostate cancer were screened, of which 816 were enrolled to the study. However, 190 patients had PSA levels exceeding 10 ng/mL and had to be excluded. Of the 626 enrolled patients, 38.82% (243/626) were diagnosed with prostate cancer, and 22.52% (141/626) had significant prostate cancer (Fig. 1). In the PSA 2.5–4 ng/mL group, the PHI cutoff value for significant prostate cancer detection was 32 (sensitivity 69.70%, specificity 59.86%, AUC 0.6829), and the PHI cutoff value for overall prostate cancer was 28 (sensitivity 78.87%, specificity 55.77%, AUC 0.6977) (Supplement Table 1). In the PSA 4–10 ng/mL group, the PHI cutoff value for significant prostate cancer detection was 39 (sensitivity 69.44%, specificity 62.39%, AUC 0.7168) and the PHI cutoff value for overall prostate cancer was 52 (sensitivity 38.37%, specificity 86.74%, AUC 0.6805) (Supplement Table 2).

    Fig. 1
    Flowchart of the study. PSA: prostate-specific antigen.

    1. Analysis of the PSA 2.5 to 4 ng/mL group

    The values of fPSA, PHI, prostate-specific antigen density (PSAD), International Prostate Symptom Score, prostate volume, and hypertension were significantly different between patients diagnosed with cancer and those who were not (p<0.05) (Table 1). Only fPSA and PHI were significantly different between patients with significant prostate cancer and those with non-significant cancer (p<0.05) (Table 2).

    Table 1
    Baseline characteristics of prostate cancer

    Table 2
    Baseline characteristics of clinically significant prostate cancer

    During multivariate logistic regression analysis for overall prostate predictive nomogram model, the AUC of model built using PSA was 0.76 (95% CI, 0.69–0.84; R2=0.1709), the AUC of model built using fPSA was 0.76 (95% CI, 0.69–0.84; R2=0.1743), the AUC of model built using p2PSA was 0.79 (95% CI, 0.72–0.86; R2=0.2269), and the AUC of model built using PHI was 0.81 (95% CI, 0.74–0.88; R2=0.2624) (Table 3). During multivariate logistic regression analysis for significant prostate cancer, the AUC of model built using PSA was 0.67 (95% CI, 0.57–0.77; R2=0.0585), the AUC of model built using fPSA was 0.70 (95% CI, 0.60–0.79; R2=0.0708), the AUC of model built using p2PSA was 0.68 (95% CI, 0.59–0.78; R2=0.0566), and the AUC of model built using PHI was 0.69 (95% CI, 0.58–0.79; R2=0.0676) (Table 4). The nomogram predicting overall prostate cancer included PHI, age, prostate volume, hypertension, and previous biopsy history as parameters, with an AUC of 0.796 (0.727–0.866; p<0.001) (Fig. 2), and the nomogram for predicting significant prostate cancer included the parameters PHI and age, with an AUC of 0.697 (0.598–0.795; p=0.001) (Fig. 3).

    Fig. 2
    Nomogram for overall prostate cancer in the patients with PSA 2.5 to 10 ng/mL. AUC: area under the curve, HTN: hypertension, PHI: prostate health index, PSA: prostate-specific antigen, ROC: receiver operating characteristic, TRUS: transrectal ultrasonography.

    Fig. 3
    Nomogram for clinical significant prostate cancer in the patients with PSA 2.5 to 10 ng/mL. Clinical significant prostate cancer: Gleason score ≥7. AUC: area under the curve, HTN: hypertension, PHI: prostate health index, PSA: prostate-specific antigen, ROC: receiver operating characteristic, TRUS: transrectal ultrasonography.

    Table 3
    Logistic regression and multivariable analysis of prostate cancer

    Table 4
    Logistic regression and multivariable analysis of clinically significant prostate cancer

    2. Analysis of the PSA 4 to 10 ng/mL group

    Significant differences were found between fPSA, p2PSA, PHI, PSAD, age, prostate volume, and 5ARI administration history between patients diagnosed with cancer and those who were not (Table 1). PSA, fPSA, p2PSA, PHI, and PSAD were significantly different between patients with non-significant cancer and those with significant prostate cancer (Table 2).

    During multivariate logistic regression analysis for prostate predictive nomogram model, the AUC of model built using PSA was 0.71 (95% CI, 0.66–0.76; R2=0.1026), the AUC of model built using fPSA was 0.71 (95% CI, 0.66–0.76; R2=0.0996), the AUC of model built using p2PSA was 0.73 (95% CI, 0.68–0.78; R2=0.1295), and the AUC of model built using PHI was 0.76 (95% CI, 0.71–0.81; R2=0.1741) (Table 3). During multivariate logistic regression analysis for significant prostate cancer, the AUC of model built using PSA was 0.71 (95% CI, 0.65–0.77; R2=0.0923), the AUC of model built using fPSA was 0.72 (95% CI, 0.66–0.78; R2=0.0957), the AUC of model built using p2PSA was 0.73 (95% CI, 0.68–0.79; R2=0.0995), and the AUC of model built using PHI was 0.79 (95% CI, 0.73–0.84; R2=0.1760) (Table 4). The nomograms for predicting overall prostate cancer as well as significant prostate cancer included PHI, age, and prostate volume as the parameters and achieved AUCs of 0.812 (0.783–0.842; p<0.001) (Fig. 2) and 0.839 (0.810–0.869; p<0.001) (Fig. 3), respectively.

    DISCUSSION

    This prospective study generated a PHI-based nomogram for detecting prostate cancer in patients with PSA levels in the gray zone. Since the development of PSA as a biomarker for prostate cancer, diagnosis of insignificant prostate cancer and very low-risk prostate cancer has been on the rise. Recent studies report that low-risk prostate cancer shows a stage migration, accounting for approximately half of all prostate cancers [14]. Even though the natural course of prostate cancer remains unidentified, it is often indolent and progresses slowly, unlike other carcinomas. A European randomized study that investigated the effect of PSA screening on mortality from prostate cancer reported that PSA screening could reduce mortality due to prostate cancer by 20%. However, the study also pointed out that overtreatment of 48 prostate cancer patients was needed to reduce one prostate cancer-related death [15].

    Active surveillance (AS) for prostate cancer includes periodically following up on the course of the disease and implementing definite therapy at an appropriate time. First introduced in 2002, AS is now accepted as one of the treatment options for low- and very low-risk prostate cancer to avoid overtreatment. A recently published meta-analysis has reported that low-risk prostate cancer shows no significant difference in oncological outcomes such as pathological outcome, biochemical recurrence rate, and cancer-specific mortality rates even after years of delayed treatment [16]. However, few AS patients complain of anxiety, disease uncertainty, and decreased quality of life. Moreover, the possibility of missing high-grade prostate cancer during biopsy is to be considered. Therefore, the application of AS to patients with prostate cancer has limitations and can be considered only as an alternative to the overtreatment of prostate cancer but not to reduce overdiagnosis.

    Several methods have been studied to reduce unnecessary biopsy and overdiagnosis of prostate cancer, including those improving the ability to prediction of the prostate cancer. Even though various biomarkers or modalities for predicting prostate cancer have been developed, a representative predictive model for prostate cancer remains largely unidentified. Owing to its high NPV, multiparametric magnetic resonance imaging (mpMRI) has gained significant attention to reduce unnecessary biopsies; however, qualitative differences could occur between the imaging test results of different medical institutions [17].

    Combined with endogenous protease inhibitors (e.g.,α1 anti-chymotrypsin, α2 macroglobulin), PSA in serum exists in a complex form, and only a part of PSA exists as fPSA that is not present as part of a complex [18]. The fPSA comprises benign prostatic hyperplasia-associated PSA, inactive PSA, and precursor isoform of PSA (proPSA), among which proPSA is associated with prostate cancer [19]. ProPSA is cleaved into three truncated forms ([-2], [-4], [-5, -7] proPSAs) by the human kallikrein enzyme, of which p2PSA is the most stable and specific for prostate cancer [9]. PHI, calculated using PSA, fPSA, and p2PSA, has been widely used since its approval by the U.S. Food and Drug Administration (FDA) in 2012 for its usefulness in determining prostate biopsies in individuals with PSA of 2–10 ng/mL [20]. The AUC for the model detecting prostate cancer using PHI was reported to be 0.77 and was higher than that of the model using PSA (AUC=0.50) [21]. Using PHI avoided 32.6% to 71.1% of unnecessary prostate biopsies [12].

    The risk of prostate cancer is associated with factors such as race, age, underlying disease, PSA, prostate volume, and PHI. Moreover, the usefulness of PHI density, which added to the concept of prostate volume rather than PHI itself, was also reported [13]. Therefore, prostate biopsy should be performed based on a comprehensive risk assessment for each patient. Several nomograms have been reported to predict the risk of prostate cancer. By analyzing 633 patients with prostate cancer and PSA <20 ng/mL, Optenberg et al [22] reported a nomogram using PSA, digital rectal exam (DRE), race, and age as predictors for prostate cancer. The predictive power of the overall test was high within the model population (ROC 80.8%), with minimal loss of power in the external population. However, they did not evaluate the relevance of fPSA values, and the study is of limited utility to assess patients in the gray zone as patients with PSA >10 ng/mL were also part of the study.

    Eastham et al [23] constructed a model based on data obtained from a group of 700 patients; however, the study group comprised only patients with PSA <4 ng/mL that represents merely 14% of the whole biopsy-positive population. Further, they did not report a definite AUC for their model. Karakiewicz et al [24] developed a predictive model for prostate cancer, including age, PSA, %fPSA, and DRE, by retrospectively analyzing 4,193 patients with PSA <50 (AUC 0.77). The study population included patients with high PSA levels and showed a relatively low cancer detection rate as sextant systematic biopsies were performed. Zhu et al [25] developed a PHI-based nomogram with high accuracy (0.839) for predicting PCa using age, prostate volume, and PHI by analyzing 347 patients with PSA 4–10 ng/mL in the Chinese community. Like previous studies, in our study, PHID was not used as an independent variable because the prostate volume was analyzed to create the nomogram.

    Nomograms with comparable results to previous studies were produced in our study. Furthermore, depending on the PSA levels, the patients were divided into the 2.5–4 and 4–10 ng/mL groups, and individual nomograms for overall prostate cancer and significant prostate cancer were generated to enable risk calculation for each patient. Moreover, in the case of PSA 4–10 ng/mL group, a high AUC value (significant prostate cancer: 0.839, overall prostate cancer: 0.812) was obtained. Depending on the PSA level, the statistically significant parameters that can predict prostate cancer and the effectiveness of each parameter are different. This may be due to differences in the incidence of prostate cancer and significant prostate cancer according to the PSA level.

    This study had few limitations. First, we did not perform an external validation. Second, most patients underwent a pre-biopsy mpMRI with combined biopsy, and analyzing them was challenging. However, we developed a PHI-based nomogram for prostate cancer prediction through a prospective study. External validation and additional analysis on the effectiveness of this nomogram are essential.

    CONCLUSIONS

    PHI-based nomogram with a high AUC value for prostate cancer was developed through a prospective study. Even though external validations are essential, a nomogram using PHI might improve prostate cancer prediction and reduce the need for unnecessary prostate biopsies.

    Supplementary Materials

    Supplementary materials can be found via https://doi.org/10.5534/wjmh.220223.

    Supplement Table 1

    Cutoff value for prostate cancer in patients with PSA 2.5 to 4 ng/mL

    Click here to view.(73K, pdf)

    Supplement Table 2

    Cutoff value for prostate cancer in patients with PSA 4 to 10 ng/mL

    Click here to view.(72K, pdf)

    Notes

    Conflict of Interest:The authors have nothing to disclose.

    Funding:This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No: NRF-2020R1A2C2007662). This work was also supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, Republic of Korea, the Ministry of Food and Drug Safety) (Project Number: 202015X30) and Beckman Coulter Korea Inc, Seoul, Korea.

    Author Contribution:

    • Conceptualization: JHK, SSJ.

    • Data curation: SWL, HP, GS, SW, JHC.

    • Formal analysis: JHC.

    • Funding acquisition: JHK, SSJ.

    • Investigation: MK, HHS, HWJ, BCJ, SIS, HML.

    • Methodology: SWL, HP, GS, SW, MK, JHC.

    • Project administration: JHK, SSJ.

    • Resources: SWL, HP, GS, SW, MK, HHS, HWJ, BCJ, SIS, HML.

    • Supervision: BCJ, SIS, HML, SSJ.

    • Visualization: JHC.

    • Writing – original draft: JHC.

    • Writing – review & editing: JHK, SSJ.

    Data Sharing Statement

    The data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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    MeSH Terms
    Area Under Curve
    Biopsy
    Diagnosis
    Humans
    Nomograms*
    Prospective Studies*
    Prostate*
    Prostate-Specific Antigen
    Prostatic Neoplasms*
    Respect
    Metrics
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