Abstract
Objectives
This study aimed to investigate the accuracy of multiparametric magnetic resonance imaging (mpMRI), genetic urinary test (GUT), and prostate cancer prevention trial risk calculator version 2.0 (PCPTRC2) for the clinically significant prostate cancer (csPCa) diagnostic in biopsy-naïve patients.
Materials and methods
In a single center study between 2021 and 2024 participants underwent prostate mpMRI, GUT, and ultrasound (US) guided biopsy. The csPCa risk was calculated using PCPTRC2. After conducting a digital rectal examination (DRE), a GUT was performed. It incorporated the RNA levels of prostate cancer antigen 3 (PCA3) and transmembrane serine protease 2 (TMPRSS2) gene and ETS-related gene (ERG) fusion genes (T: E), along with the patient’s age and PSA density. The McNemar test compared detection rates between modalities.
Results
208 (mean age 62.9 years +/- 8.2) men were included prospectively. A positive GUT score was found in 67.8% and PIRADS ≥3 in 81.7% of all cases. The combination of GUT with mpMRI showed significantly higher sensitivity (99.1%) than GUT and mpMRI alone, 84.4% and 93.8%, respectively (p ≤ 0.05). Similarly, very high sensitivity (99.0%) was achieved by combining mpMRI with PCPTCR2. Nevertheless, mpMRI plus GUT combination exceeded mpMRI plus PCPTCR2 by allowing to save a higher fraction of unnecessary biopsies, 25% and 2.4%, respectively.
Conclusion
GUT and mpMRI combination would allow saving a substantial fraction of unnecessary biopsies with minimal risk of missing csPCa cases.
Keywords: Prostate, Cancer, MRI, Biomarker, Biopsy-naïve, Risk calculator
Introduction
Prostate cancer is the second most diagnosed oncological disease in men [1] The main purpose of screening is to detect prostate cancer (PCa) at early stages when it is potentially curable [2]. However, it is agreed that prostate specific antigen (PSA) screening is associated with over-diagnosis of a large number of low-risk PCa and increased number of unnecessary prostate biopsies [2]. Moreover, men undergoing US-guided biopsy are at risk of several common complications, including lower urinary tract symptoms, hematuria, hematospermia, rectal bleeding (in case of transrectal procedure), pain, and infection that could even lead to sepsis and death. Therefore, antibiotic prophylaxis is recommended for US-guided biopsy [3].
Multiparametric MRI (mpMRI) in PCa patients has demonstrated better diagnosis while performing targeted biopsy and decreased diagnosis of non-clinically significant prostate cancer (non-csPCa) [4]. Currently, there is a strong recommendation to use mpMRI not only for second and all other biopsies but also in biopsy-naïve men [5] Urine, on the other hand, is a versatile body fluid for non-invasive urological malignancies detection [6] Prostate cancer antigen 3 (PCA3), also known as DD3, is markedly higher expressed in 95% of cancerous compared to adjacent non-cancerous prostate tissue [7]. Another genetic biomarker is a fusion of androgen-regulated transmembrane serine protease 2 (TMPRSS2) gene and ETS-related gene (ERG) (T: E) which is believed to play a major role in PCa tumorigenesis. Several studies have demonstrated that both PCA3 and T: E fusion gave significant predictive value in identifying prostate cancer. [8–10]. For individualized risk of PCa and clinically significant PCa (csPCa) evaluation in recent years there has been a proliferation of risk calculators that use various factors such as PSA, age, family history, and ethnicity to determine individualized risk of PCa and clinically significant PCa (csPCa) [11]. The prostate cancer prevention trial risk calculator version 2.0 (PCPTRC2) is a widely recognized tool that has been developed for assessing the risk of PCa. Its original version has been extensively tested and validated in various independent cohorts. In recent times, updated versions of the calculator have been introduced, which have shown promising results in populations that differ from those for which it was initially designed [12–14]. This suggests that the calculator may have potential application in a broader range of settings beyond its original purpose.
The aim of this study was to investigate the accuracy of different techniques (MRI, GUT, PCPTRC2) and their combinations for clinically significant prostate cancer (csPCa) diagnostic.
Materials and methods
Study population
The investigation was approved by the regional Bioethical Commission, Decision no. BE-2-116. In a single-center prospective study, 208 Caucasian patients were consecutively included over a period between 2021 January and 2024 January. Informed consent was obtained from all individual participants. Men who were scheduled for initial prostate biopsy, based on elevated total PSA level (selected elevated value in this study was > 2 ng/ml and < 20ng/ml confirmed) or abnormal digital rectal examination (DRE) were included in the study. Exclusion criteria were a history of PCa or other neoplasms under active treatments, prior prostate biopsy, and contraindications for MRI examination. All subjects underwent diagnostic tests– prostate GUT and mpMRI. After the examination, targeted cognitive fusion US-guided prostate biopsy, as well as systematic prostate biopsy, was performed in 170 cases of mpMRI visible PIRADS 3, 4, and 5 lesions. Only systematic US-guided biopsy was performed in 38 cases without visible lesions (mpMRI PIRADS 1 and 2), as specified in the study protocol. The risk of csPCa was calculated by using PCPTRC2 only in patients aged 55–90 years (n = 182, 87,5%) as required by the calculator [13]. All subjects had the right to terminate further involvement in the study at any time.
Prostate mpMRI
All subjects included in the study group were submitted to a mpMRI using either a 1.5T or 3T MR scanner. A standard combination of T1-weighted (T1W) images, T2-weighted (T2W) images, diffusion weighted imaging (DWI) and dynamic contrast-enhanced (DCE) studies were used and the PIRADS version 2.1 (v2.1) was used for grading the lesions from 1 to 5 [15]. PSA density (PSAD) was calculated in all cases. All mpMRI examinations were performed before prostate biopsy and analyzed by two expert radiologists (10 and 5 years of experience) independently without previous knowledge of the urine test scores and biopsy outcomes. Both readers were aware of the clinical information and consensus was obtained in cases of discrepancy.
Genetic urinary test (GUT)
All subjects included in the study group underwent urine sample examination. First-voided urine samples were collected after a prostate massage and before a prostate biopsy. Colli-Pee 20 mL devices (Novosanis) prefilled with 10 mL of stabilization media (Diagnolita) were used for urine collection and instant stabilization. Samples were transferred to the Diagnolita laboratory for analysis. A proprietary laboratory-developed Diagnolita genetic urinary test (GUT) was employed to measure PCA3 and T: E biomarker levels in urine and combine them with age and PSA density clinical data [16, 17]. The results of GUT contained the patient’s individualized risk for clinically significant PCa (Gleason score (GS) ≥ 7). The GUT score was positive or negative depending on whether the threshold of the test was exceeded. Originally, the threshold of the GUT was set to achieve a sensitivity of nearly 90% based solely on the results of systematic biopsy [16, 17].
Prostate cancer prevention trial risk calculator version 2.0 (PCPTRC2)
A basic version of PCPTRC2 was used that involved these variables: race, age, PSA level (ng/ml), family history of prostate cancer, DRE result, and prior biopsy. The calculated probability of high-grade PCa was used in all calculations. As there was no predefined cutoff for PCPTRC2 we chose the cutoff of 6% to match the sensitivity of mpMRI. This allowed us to compare saved biopsy numbers at a similar sensitivity level.
Prostate biopsy
All subjects included in the study were submitted to a US-guided prostate biopsy performed in the university hospital, by the same urologist. In all cases, 12 random systematic cores were obtained. In cases when a PI-RADS score of 3–5 at mpMRI was obtained, additional targeted samples (2 cores per lesion) were obtained using a cognitive targeted prostate technique. All samples were evaluated in our clinic by an experienced genitourinary pathologist. Histological grading was assessed according to the Gleason grading system as well as the Gleason Grade Groups [18].
Statistical analysis
All calculations were performed using R version 4.3.2. A p-value ≤ 0.05 was deemed to show a statistical significance. The normality of the data was estimated with Shapiro-Wilk test. The comparisons of continuous parameter values between groups were performed using a two-sided Student’s t-test for normally distributed data and a two-sided Wilcoxon rank sum test otherwise. Analogously, categorical parameter counts were compared with two-sided Fisher’s exact test.
Descriptive statistics were used to describe patients’ characteristics. The GUT findings were categorized as positive or negative depending on whether the predicted csPCa risk exceeded the predefined threshold. The mpMRI results were considered positive if reported as PIRADS 3–5 and negative if PIRADS 1–2. To evaluate the performance of using both tests together, positive cases required at least one positive result from either GUT or mpMRI.
Combined systematic and targeted biopsy results were used to allocate a patient to clinically significant (GS ≥ 7) prostate cancer (csPCa) or non-clinically significant prostate cancer (non-cs PCa) groups. The highest Gleason score identified by either the systematic or targeted biopsy was used for the assignment. All calculated sensitivity, specificity and, missed csPCa, saved biopsies, and saved unnecessary biopsies numbers were based on the aforementioned grouping of patients. 95% confidence intervals for sensitivity and specificity were calculated with an exact binomial test using binom.test function from R stats package. The sensitivity and specificity values were contrasted with the McNemar test using sesp.mcnemar function from R package DTComPair version 1.2.2. The comparisons were made only among complete cases for both compared models.
Results
Study population
In total, data from 208 men were included in the analysis. Patient characteristics, mpMRI, GUT, PCPTRC2, and biopsy outcomes are summarized in Table 1. The median age of all patients was 63 years (range 43–87) and the mean PSA level was 6.3 ng/ml. Upon a biopsy 112 (53.8%) of patients were diagnosed with csPCa (GS≥7). A positive GUT score was found in 141 cases (67.8%) and PIRADS score ≥3 in 170 cases (81.7%). GUT and mpMRI scores matched in 149 cases (74.1%) and positive scores matched in 127 cases (63.2%). Based on histopathology results patients were divided into two groups– csPCa and non-clinically significant PCa (non-cs PCa). Total PSA levels and PSA density levels significantly differed among groups (p ≤ 0.05). The GUT score was positive in statistically significantly (p < 0.001) larger number of csPCa cases– 92 of 112 (82.1%) than in non-cs PCa cases– 49 of 96 (51%). The GUT predicted risk for csPCa was significantly higher (p < 0.001) in csPCa (median value 28.7%) than in non-cs PCa cases (median value 12.8%). Positive mpMRI score (PIRADS ≥3) was reported in a statistically significantly larger number of csPCa group (93.8%), than in non-cs PCa group (67.7%). Stratification of subjects based on histopathology results at biopsy (csPCa and non-cs PCa) is reported in Table 2. Seven cases were referred as PIRADS 3 lesions and the same stratification of subjects is reported in Table 3.
Table 1.
Patients’ characteristics (number, %, median +-SD, median, range)
| Parameter | Value |
|---|---|
| Number of cases, n | 208 |
| Age (years) | |
| Mean +/-SD | 62.9 +/-7.2 |
| Median | 63 |
| Range | 43–87 |
| Total PSA (ng/ml) | |
| Mean +/-SD | 6.3 +/- 2.9 |
| Median | 5.5 |
| Range | 2.7–19.1 |
| PSA density (ng/ml/ml) | |
| Mean +/- SD | 0.15 +/- 0.11 |
| Median | 0.12 |
| Range | 0.04–0.79 |
| Prostate volume | |
| Mean +/- SD | 51.2 +/-22.1 |
| Median | 44.8 |
| Range | 13.5–129.1 |
| DRE suspicious, n (%) | |
| Yes | 107 (51.4) |
| No | 101 (48.6) |
| Family history, n (%) | |
| Yes | 28 (13.5) |
| No | 180 (86.5) |
| GUT score, n (%) | |
| Negative | 60 (28.8) |
| Positive | 141 (67.8) |
| NA | 7 (3.4) |
| GUT predicted risk for csPCa (%) | |
| Mean +/-SD | 26.1+/- 18.7 |
| Median | 23 |
| Range | 1–86.9 |
| mpMRI, n (%) | |
| PIRADS < 3 | 38 (18.3) |
| PIRADS > = 3 | 170 (81.7) |
| PRCTPRC2 csPCa risk (%) | |
| Mean +/- SD | 11.6 +/- 5.9 |
| Median | 10 |
| Range | 4–41 |
| Biopsy outcomes, n (%) | |
| Negative and GS < 7 | 96 (46.2) |
| GS > = 7 | 112 (53.8) |
| GUT score and mpMRI PIRADS score, n (%) | |
| GUT positive, mpMRI positive | 127 (63.2) |
| GUT negative, mpMRI negative | 22 (10.9) |
| GUT positive, mpMRI negative | 14 (7) |
| GUT negative, mpMRI positive | 38 (18.9) |
n number, SD standard deviation, PSA prostate-specific antigen, PSAD PSA density, DRE digital rectal examination, GUT genetic urinary test, mpMRI multiparametric magnetic resonance imaging, PIRADS prostate imaging reporting and data system, PCPTRC2 prostate cancer prevention trial risk calculator 2
Table 2.
Stratification of patients’ characteristics on the basis of prostatic biopsy results (number, %, mean +-SD, median, range)
| Parameter | Non-cs PCa | Cs PCa | P value |
|---|---|---|---|
| Number of cases, n (%) | 96 (46.2) | 112 (53.8) | |
| Age (years) | 0.267 | ||
| Mean +/-SD | 62.3 +/-7.3 | 63.4 +/- 7.1 | |
| Median | 63 | 63 | |
| Range | 43–82 | 50–87 | |
| Total PSA (ng/ml) | 0.013 | ||
| Mean +/-SD | 5.8 +/- 2.7 | 6.7 +/- 3 | |
| Median | 5.2 | 5.7 | |
| Range | 2.7–18.7 | 3-19.1 | |
| PSA density (ng/ml/ml) | < 0.001 | ||
| Mean +/- SD | 0.13 +/- 0.11 | 0.16 +/- 0.11 | |
| Median | 0.1 | 0.14 | |
| Range | 0.04–0.73 | 0.05–0.79 | |
| Prostate volume | 0,041 | ||
| Mean +/- SD | 56,2 +/-26,4 | 46,9 +/- 16,6 | |
| Median | 48,4 | 43,4 | |
| Range | 13,7–129,1 | 13,5–92 | |
| DRE suspicious, n (%) | 0.164 | ||
| Yes | 44 (45.8) | 63 (56.2) | |
| No | 52 (54.2) | 49 (43.8) | |
| Family history, n (%) | 0.688 | ||
| Yes | 14 (14.6) | 14 (12.5) | |
| No | 82 (85.4) | 98 (87.5) | |
| GUT score, n (%) | < 0.001 | ||
| Negative | 43 (44.8) | 17 (15.2) | |
| Positive | 49 (51) | 92 (82.1) | |
| Not evaluated | 4 (4.2) | 3 (2.7) | |
| GUT predicted risk for csPCa (%) | < 0.001 | ||
| Mean +/-SD | 20.7 +/- 18.6 | 30.8 +/- 17.5 | |
| Median | 12.8 | 28.7 | |
| Range | 1-72.8 | 3.3–86.9 | |
| mpMRI PI-RADS score, n (%) | < 0.001 | ||
| PIRADS < 3 | 31 (32.3) | 7 (6.2) | |
| PIRADS > = 3 | 65 (67.7) | 105 (93.8) | |
| PRCTPRC2 csPCa risk (%) | 0.031 | ||
| Mean +/- SD | 10.8 +/- 5.7 | 12.3 +/- 6 | |
| Median | 10 | 11 | |
| Range | 4–35 | 4–41 | |
| GUT score and mpMRI PIRADS score, n (%) | < 0.001 | ||
| GUT positive, mpMRI positive | 41 (44.6) | 86 (78.9) | |
| GUT negative, mpMRI negative | 21 (22.8) | 1 (0.9) | |
| GUT positive, mpMRI negative | 8 (8.7) | 6 (5.5) | |
| GUT negative, mpMRI positive | 22 (23.9) | 16 (14.7) |
n number, SD standard deviation, PSA prostate-specific antigen, PSAD PSA density, DRE digital rectal examination, GUT genetic urinary test, mpMRI multiparametric magnetic resonance imaging, PIRADS prostate imaging reporting and data system, PCPTRC2 prostate cancer prevention trial risk calculator 2, csPCa clinically significant prostate cancer, non-cs PCa non-clinically significant prostate cancer
Table 3.
Patient characteristics of a subgroup with PIRADS = 3 stratified on the basis of prostatic biopsy results (number, %, mean +-SD, median, range)
| Parameter | Non-cs PCa | Cs PCa | P value |
|---|---|---|---|
| Number of cases, n (%) | 3 (42.9) | 4 (57.1) | |
| Age (years) | 0.727 | ||
| Mean +/-SD | 61.7 +/- 4.9 | 63.5 +/- 8.1 | |
| Median | 64 | 61.5 | |
| Range | 56–65 | 56–75 | |
| Total PSA (ng/ml) | 0.629 | ||
| Mean +/-SD | 7.4 +/- 2.9 | 10 +/- 4.4 | |
| Median | 6 | 10 | |
| Range | 5.481–10.66 | 5.4-14.52 | |
| PSA density (ng/ml/ml) | 1 | ||
| Mean +/- SD | 0.16 +/- 0.07 | 0.23 +/- 0.18 | |
| Median | 0.17 | 0.16 | |
| Range | 0.08–0.23 | 0.11–0.5 | |
| Prostate volume | 1 | ||
| Mean +/- SD | 50.5 +/- 19.4 | 51.8 +/- 23.7 | |
| Median | 47.1 | 49.5 | |
| Range | 33.1–71.4 | 29–79 | |
| DRE suspicious, n (%) | 1 | ||
| Yes | 1 (33.3) | 2 (50) | |
| No | 2 (66.7) | 2 (50) | |
| Family history, n (%) | 1 | ||
| Yes | 1 (33.3) | 1 (25) | |
| No | 2 (66.7) | 3 (75) | |
| GUT score, n (%) | 0.143 | ||
| Negative | 2 (66.7) | 0 (0) | |
| Positive | 1 (33.3) | 4 (100) | |
| Not evaluated | 0 (0) | 0 (0) | |
| GUT predicted risk for csPCa (%) | 0.229 | ||
| Mean +/-SD | 11.6 +/- 8.5 | 29.8 +/- 20.9 | |
| Median | 11 | 20.4 | |
| Range | 3.4–20.3 | 17.4–61.2 | |
| PRCTPRC2 csPCa risk (%) | 1 | ||
| Mean +/- SD | 13 +/- 1 | 15 +/- 7 | |
| Median | 13 | 14.5 | |
| Range | 12–14 | 9–22 | |
| GUT negative, mpMRI positive | 22 (23.9) | 16 (14.7) |
n number, SD standard deviation, PSA prostate-specific antigen, PSAD PSA density, DRE digital rectal examination, GUT genetic urinary test, mpMRI multiparametric magnetic resonance imaging, PIRADS prostate imaging reporting and data system, PCPTRC2 prostate cancer prevention trial risk calculator 2, csPCa clinically significant prostate cancer, non-cs PCa non-clinically significant prostate cancer
Comparison of diagnostic parameters between urinary biomarkers, mpMRI, PCPTRC2 and their combinations
The GUT predicted risks for csPCa were significantly higher in csPCa versus non csPCa for both negative mpMRI (PIRADS ≤ 2) and positive mpMRI (PIRADS ≥ 4) subgroups (Fig. 1). The sensitivity and specificity of different methods to predict csPCa at biopsy are presented in Tables 4 and 5 and are also illustrated visually in Figs. 2 and 3, as detailed below. GUT showed significantly lower sensitivity (84.4% vs. 93.8%, p = 0.033) and significantly higher specificity (46.7% vs. 32.3%, p = 0.011) than mpMRI in predicting csPCa at the biopsy. The specificity of GUT or mpMRI was higher than PCPTRC2, 46,7% and 32,3% vs. 11,0%, respectively and the difference was statistically significant (p ≤ 0,05). The combination of GUT with mpMRI demonstrated a significantly higher sensitivity (99.1%) compared to GUT and mpMRI alone, which had sensitivities of 84.4% and 93.8%, respectively (p < 0.05). A similar level of sensitivity was attained with the combination of mpMRI plus PCPTRC2 (99.0%). Nonetheless, regarding specificity, mpMRI plus GUT combination significantly surpassed mpMRI plus PCPTRC2, 25,0% and 2,4%, respectively (p = 0,001). Specificity of mpMRI and GUT combination (25.0%) was not significantly different from mpMRI alone (32.3%), however GUT alone demonstrated significantly higher specificity than mpMRI and GUT combination, 46.7% and 25.0%, respectively (p < 0.001). Missed csPCa cases and saved biopsy numbers for various models are summarized in Table 6 as described below. In terms of unnecessary biopsies that can be saved the best results can be achieved while using GUT, 46.7%, respectively. However, GUT usage would miss the highest numbers of csPCa cases, 16.5%, while mpMRI combinations with GUT or PCPTCR2 miss the lowest number of csPCa cases– close to 1%. In cases of negative mpMRI, GUT correctly prevented 72,4% unnecessary biopsies (21 of 29) and correctly identified 85,7% patients who required a biopsy (6 of 7) (Table 2).
Fig. 1.
GUT risk probabilities stratified by mpMRIPIRADS scores and biospy results. GUT genetic urinary test, mpMRI multiparameteric magnetic resonance imaging, csPCa clinically significant prostate cancer that comprises negative biospy and GS = 6 PCa cases
Table 4.
Sensitivity comparisons for prediction of csPCa with 95% CI given in brackets
| Model | n, csPCa | Sensitivity, % | P value vs. PCPTRC2 | P value vs. mpMRI | P value vs. GUT |
|---|---|---|---|---|---|
| PCPTRC2 | 99 | 93.9 (87.3–97.7) | NA | 1 | 0.071 |
| mpMRI | 112 | 93.8 (87.5–97.5) | 1 | NA | 0.033 |
| GUT | 109 | 84.4 (76.2–90.6) | 0.071 | 0.033 | NA |
| mpMRI & PCPTRC2 | 99 | 99.0 (94.5–100) | 0.025 | 0.025 | 0.001 |
| mpMRI & GUT | 109 | 99.1 (95.0-100) | 0.059 | 0.014 | < 0.001 |
GUT genetic urinary test, mpMRI multiparametric magnetic resonance imaging, PRCTPRC2 prostate cancer prevention trial risk calculator 2, csPCa clinically significant prostate cancer, non-cs PCa non-clinically significant prostate cancer
Table 5.
Specificity comparisons for prediction of csPCa with 95% CI given in brackets
| Model | N, non-cs PCa | Specificity, % | P value vs. PCPTRC2 | P value vs. mpMRI | P value vs. GUT |
|---|---|---|---|---|---|
| PCPTRC2 | 82 | 11.0 (5.1–19.8) | NA | 0.028 | < 0.001 |
| mpMRI | 96 | 32.3 (23.1–42.6) | 0.028 | NA | 0.011 |
| GUT | 92 | 46.7 (36.3–57.4) | < 0.001 | 0.011 | NA |
| mpMRI & PCPTRC2 | 82 | 2.4 (0.3–8.5) | 0.008 | < 0.001 | < 0.001 |
| mpMRI & GUT | 93 | 25.0 (16.6–35.1) | 0.251 | 0.058 | < 0.001 |
GUT genetic urinary test, mpMRI multiparametric magnetic resonance imaging, PRCTPRC2 prostate cancer prevention trial risk calculator 2, csPCa clinically significant prostate cancer, non-cs PCa non-clinically significant prostate cancer
Fig. 2.
Sensitivity comparisons for prediction of csPCa. GUT genetic urinary test, mpMRI multiparametric magnetic resonance imaging, PRCTPRC2 prostate cancer prevention trial risk calculator 2, csPCa clinically significant prostate cancer, non-cs PCa non-clinically significant prostate cancer
Fig. 3.
Specificity comparisons for prediction of csPCa. GUT genetic urinary test, mpMRI multiparametric magnetic resonance imaging, PRCTPRC2 prostate cancer prevention trial risk calculator 2, csPCa clinically significant prostate cancer, non-cs PCa non-clinically significant prostate cancer
Table 6.
Missed csPCa cases and saved biopsy numbers for various models
| Model | Missed csPCa cases | Saved biopsies | Saved unnecessary biopsies |
|---|---|---|---|
| PCPTRC2 | 6 of 99; 6.1% | 15 of 181; 8.3% | 9 of 82; 11.0% |
| mpMRI | 7 of 112; 6.2% | 38 of 208; 18.3% | 31 of 96; 32.3% |
| GUT | 18 of 109; 16.5% | 63 of 201; 31.3% | 43 of 92; 46.7% |
| mpMRI & PCPTRC2 | 1 of 99; 1.0% | 3 of 181; 1.7% | 2 of 82; 2.4% |
| mpMRI & GUT | 1 of 109; 0.9% | 24 of 201; 11.9% | 23 of 92; 25.0% |
GUT genetic urinary test, mpMRI multiparametric magnetic resonance imaging, PRCTPRC2 prostate cancer prevention trial risk calculator 2, csPCa clinically significant prostate cancer
Discussion
The 2023 EAU-ESTR-SIOG Guidelines recommend clinicians to consider the use of biomarkers and mpMRI before performing a biopsy [19]. Our study results showed a high mpMRI and GUT combination sensitivity in detecting csPCa.
Various biomarker-based urinary tests have been suggested to reduce unnecessary prostate biopsies while missing a low fraction of csPCA [20–23]. According to Sanda et al., an assay combining PCA3 and T: E showed 93% sensitivity predicting aggressive PCa during biopsy in the cohort of 561 biopsy-naive patients [23]. Similarly, in our study we included a group of biopsy naïve patients only and for all of them genetic urinary tests were performed after DRE. Therefore, our study findings confirmed the tendency that including GUT (sensitivity 84.4%) for PCa diagnosis into clinical practice would reduce unnecessary prostate biopsy and overdiagnosis while preserving detection of aggressive cancer. To our knowledge this is the first study that examined the value of mpMRI and genetic urinary biomarkers (PCA3 and T: E) combination in detecting csPCa among biopsy naïve patients. However, there are several studies, that analyzed the associations of mpMRI and PCA3 combination with csPCa. One example is the study of Fenstermaker et al., where 187 men underwent mpMRI and PCA3 testing before prostate biopsy. Study results showed that PCA3 is associated with MRI suspicious score and the detection of cancer on MRI fusion targeted biopsy in biopsy naïve men population (AUC = 0,67, 95% CI 0,59 − 0,76) [24]. Another study of Porpiglia et al. reterospectively reviewed 120 biopsy naïve patients, who underwent mpMRI and PCA3 testing. It was revealed, that mpMRI resulted in higher gain inaccuracy for predicting csPCa compared with PCA3 (AUC = 0.78, p < 0,01) [25]. In comparison, our study is prospective, more extended and included not only mpMRI and PCA3 testing, but also testing of T: E and their combinations. One issue that needs to be addressed is that in our study GUT did not significantly prognosed csPCa in PIRADS 3 lesions, probably due to too small patients group. However, the prognostic tendency was preserved as in other PIRADS lesions, extended research is needed to confirm the significant results. Another matter requiring recognition is the diagnostic accuracy of csPCa using mpMRI only. Tay et al. study results showed mpMRI sensitivity of 93% when detecting csPCa [26]. Our study demonstrated almost the same but slightly higher mpMRI sensitivity of 93.8%. However, in our study specificity of mpMRI was significantly lower than GUT alone, probably due to study limitations, addressed further. In relation to PIRADS 3 lesions, no significant differences were observed between the csPCa and non-csPCa groups, likely due to the limited size of the subcohort (n = 7). Finally, the main goal of this research was to find which technique or combination could be the best diagnostic pathway for csPCa detection in biopsy naïve patients. Our results showed that performing mpMRI along with GUT would allow to reduce unnecessary biopsies by a quarter while detecting almost all cases of aggressive cancer (99.1%).
Our study has a few limitations. First, data were obtained from a single center only, which could potentially lead to selection bias. However, bias was minimized as much as possible by including patients consecutively as they presented with documented PCa suspicion. Second, the evaluation of diagnostic performance in this study relied on the results of prostate biopsies, which could introduce potential biases regarding falsely negative biopsy results. Third, the most sensitive method– MRI combined fusion with transrectal ultrasonography prostate biopsy– was not performed in this study. As a result, this omission could influence the false-negative biopsy rates and potentially impact the conclusions drawn from the study. Fourth, the patient cohort did not cover the entire pathological spectrum (i.e., Gleason score and stage). The fifth limitation was that the population was homogenous, consisting only men of the same racial and ethnic background. Also, even though our results with the implementation of a combination of GUT and mpMRI were defined as significant regarding csPCa detection, further investigation in a larger prospective cohort is required.
The combination of two non-invasive tests– GUT and mpMRI– in csPCa diagnosis of biopsy naïve patients has the potential to avoid unnecessary biopsies and prevent the detection of non-cs PCa while keeping a minimal risk of missing csPCa. Further prospective mulicentric studies with a larger and more diverse patient group should be conducted to confirm the true impact on clinical practice.
Abbreviations
- PCa
Prostate cancer
- PSA
Prostate specific antigen
- CsPCa
Clinically significant prostate cancer defined as having a Gleason Score ≥ 7
- Non
csPCa–non–clinically significant prostate cancer that comprises negative biopsy and GS = 6 PCa
- mpMRI
Multiparametric magnetic resonance imaging
- GUT
Genetic urinary test
- PCPTRC2
Prostate cancer prevention trial risk calculator version 2.0
- US
Ultrasound
- DRE
Digital rectal examination
- PCA3
Prostate cancer antigen 3
- TMPRSS2
Androgen–regulated transmembrane serine protease 2
- ERG
ETS–related gene
- T:E
Fusion of TMPRSS2 and ERG genes
- PIRADS
Prostate imaging reporting and data system version 2.1
- T1W
T1–weighted imaging
- T2W
T2–weighted imaging
- DWI
Diffusion weighted imaging
- DCE
Dynamic contrast–enhanced imaging
- GS
Gleason score
Author contributions
JK was involved in conceptualization, data curation, investigation, methodology and supervision of the study, also wrote the original draft and was involved in reviewing and editing process.CN was involved in data curation, methodology and supervision of the study, also wrote the original draft and was involved in reviewing and editing process.GCh was involved in conceptualization, data curation, investigation and supervision of the study, also wrote the original draft and was involved in reviewing and editing process.KZ was involved in conceptualization, data curation, investigation and methodology of the study, also was involved in reviewing and editing process.ML was involved in conceptualization, data curation and methodology of the study, also wrote the original draft and was involved in reviewing and editing process.SV was involved in data curation, investigation, methodology and supervistion of the study.IA was involved in data curation, investigation, methodology and supervistion of the study.KT was involved in data curation, investigation, methodology and supervistion of the study.IP was involved in data curation, investigation, methodology and supervistion of the study.MJ was involved in conceptualization, data curation, investigation, methodology and supervision of the study, also wrote the original draft and was involved in reviewing and editing process.All authors read and approved the final manuscript.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
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References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
No datasets were generated or analysed during the current study.