1. Introduction
Globally, prostate cancer (PCa) is the second leading cause of death among men, with approximately 268,490 new cases and 34,500 deaths projected to occur in America by 2022. [
1]. With the widespread use of prostate-specific antigen (PSA), the early diagnosis and treatment of PCa are gradually increasing [
2]. However, the low specificity of PSA has led to lots of unnecessary and excessive prostate biopsies, resulting in a significant financial burden as well as many post-biopsy complications. In recent years, scholars have used different biomarkers, such as the 4Kscore, PCA3, and the prostate health index (PHI), and different predictive models to improve the detection rate of prostate cancer [
3,
4,
5]. The clinical application of prostate multiparametric magnetic resonance imaging (mpMRI) and the prostate imaging reporting and data system (PI-RADS) has also improved the diagnosis of PCa and clinically significant prostate cancer (CSPCa) in terms of imaging [
6]. With the combination of the above biomarkers with mpMRI, cancer detection rates have been improved and unnecessary biopsies have been reduced [
7].
The discovery and clinical application of (-2) proPSA (P2PSA) have made PHI an important indicator for low-risk and intermediate-risk PCa screening, especially in PSA 2–20 ng/mL, in clinical practice [
8,
9]. A large cohort study showed that a cutoff value of 35 for PHI in Asian populations reached good sensitivity and specificity [
8]. However, in actual clinical work, it is insufficient to use the PHI value of 35 as a cutoff value for diagnosing prostate cancer. Therefore, the role of the combined diagnosis of PCa appears to be important.
The purpose of this study is to construct clinically useful nomograms using PHI and PI-RADS indicators, along with other clinical indicators, which are based on data from a multicenter database, in order to improve the diagnostic accuracy of PCa and CSPCa in the Asian population.
4. Discussion
PCa is one of the common malignant tumors in men and prostate biopsy remains the gold standard for confirming PCa [
18]. However, many patients experience unnecessary biopsies and suffer from the complications of biopsies. Therefore, the combined diagnosis of PCa has become quite important. Hsieh et al. found that the AUC of the combination of PHI and mpMRI (0.873 (95% CI 0.8050–0.9407)) was higher than the AUC of the PHI (0.735 (95% CI 0.6194–0.8497)) and the AUC of the mpMRI (0.830 (95% CI 0.7598–0.9004)) [
19]. Other scholars also explored and constructed many different combined models to improve the diagnostic accuracy of PCa [
7,
19,
20,
21,
22].
It is well known that mpMRI is gradually spreading in the diagnostic application of PCa [
23]. There are a lot of authors that have studied it and have offered interesting results in this regard. Grey et al. derived the negative predictive value of 97.7% for the PI-RADS score in the diagnosis of CSPCa [
24]. They thought the PI-RADS scoring could be used in the decision-making process for detecting CSPCa. A systematic review from the Cochrane Database illustrated the benefit of detecting more CSPCa in mpMRI-targeted biopsies with a sensitivity of 0.80 (95% CI: 0.69–0.87) and a specificity of 0.94 (95% CI: 0.90–0.97) [
25]. Mendhiratta et al. reported that targeted biopsy based on the mpMRI could detect more CSPCa than systematic biopsy (88.6% vs. 77.3%,
p=0.037), which reflected the strong predictive efficiency of mpMRI in CSPCa [
6]. The clinical application of mpMRI and the criteria for PI-RADS scoring are described in the ESUR prostate MR guidelines, providing clinicians with further improvements in the learning of mpMRI as well [
26].
In this study, we developed clinical prediction models and devised nomograms using the combination of PHI, PI-RADS scores, and other important clinical predictors and developed a website that promotes our nomograms. For patients with elevated PSA but low predictive probability, measures such as active monitoring can be used.
Prostate biopsy is already a routine procedure and can be performed in many hospital outpatient operating rooms. With the widespread of transperineal prostate biopsy techniques, complications such as sepsis have decreased [
27]. However, in some elderly patients with other diseases or poor coagulation function, prostate biopsy under local anesthesia still carries a high risk of bleeding. Therefore, a clinical predictive tool should be used to determine whether to perform active monitoring or to perform biopsy under close supervision.
Prior studies have constructed a number of nomograms that incorporate PHI and other clinical risk factors or PI-RADS and other clinical risk factors for PCa or CSPCa [
20,
21,
22]. The superiority of the combined diagnosis of PHI and PI-RADS has also been demonstrated in several studies [
19,
28]. However, no studies constructed nomograms with the combination of PHI, PI-RADS scores, and other clinically significant predictive factors. Considering previous studies and the usefulness as well as the convenience of a clinical predictive model, we included four independent predictive factors in detecting PCa: age, PHI, PI-RADS, and PV. In predicting the positive rate of CSPCa, four predictive factors were included: age, PHI, PI-RADS, and Log PSAD. Although age had a P value of 0.084 for PCa in the univariable regression analysis, we still decided to include age in the model because age has been clinically identified as a risk factor in the development of PCa [
29]. According to several observational studies, the diagnosis of patients with older age for PCa is associated with a poor prognosis [
30,
31]. As the (-2) proPSA was found in 1997, PHI is gradually becoming an effective means of screening for PCa [
32] and has shown good AUC in detecting PCa and CSPCa [
9,
33]. As mentioned above, the nomogram studied in this study is more applicable to patients with TPSA between 4 and 20 ng/mL who are able to undergo the PHI test as well as the mpMRI examination. Although the applicability conditions are more stringent, it is beneficial to increase the detection rate of patients in this TPSA interval.
There are many previous nomograms for predicting PCa and studies combining PHI and PI-RADS score for detecting PCa [
19,
22]. Although the benefits of combining PHI with mpMRI are well recognized, the nomogram combining PHI with mpMRI has not been studied. As compared to previously published PCa and CSPCa predictive models, our study offers the following advantages. First, we visualized the prediction model as nomograms and developed a website with an operation interface for our nomogram on 20 August 2022, (
https://zhouyonghengql.shinyapps.io/PCa_DynNom/), (
https://zhouyonghengql.shinyapps.io/CSPCa_DynNomapp/), which greatly improved in terms of efficiency, accuracy, and clinical usability as a result of this optimization. Secondly, the combination of serum-specific biomarkers PHI and mpMRI also enables the combined diagnosis of physiological and anatomical functions, which can reduce the number of unnecessary biopsies by more than half.
It is worth mentioning that in our study, we analyzed the sensitivity and specificity of different cutoff values of PHI, and we found that as the cutoff value of PHI increased, the missed PCa and CSPCa also increased gradually. However, for the cut-off value of PHI of 35 [
8], which is commonly used in clinical practice, our study found that its specificity is low, and it is necessary to appropriately increase the threshold of PHI for the detection of cancer. When the prediction rate for PCa by the nomogram is greater than 27%, our study suggests that prostate biopsy should be performed in this population with a low risk of missing CSPCa.
The following limitations were also included in our study. First, although this study is a prospective multicenter cohort study, the population sample size of our study was small, which may have some limitations. Secondly, there are many clinical studies that are still controversial and have not reached a consensus on the definition of CSPCa, and the GS ≥ 3 + 4 seems to be prevalent in most recent criteria [
15,
34]. We, therefore, used the definition in our study. In addition, maximum core length was used in the definition of CSPCa; however, we did not incorporate it into the final analysis, as it was not available for all patients. The use of a nomogram in this study can predict the probability of developing CSPCa before biopsy and can provide good treatment advice to patients. However, this study did not correlate the predictive results of the nomogram with the risk of CSPCa at the time of radical prostatectomy or the risk of adverse pathological features of radical prostatectomy, which remains a direction for future research and has considerable clinical implications. Finally, a larger sample and external validation are still needed to prove our conclusions and update our nomograms.