Abstract
Accurate classification between non-clinically significant prostate cancer (non-csPCa) and clinically significant prostate cancer (csPCa) is essential for effective risk stratification and optimal management of prostate cancer. This study aimed to evaluate the consistency between preoperative and postoperative assessments of non-csPCa, and identify preoperative variables that can effectively predict the risk of csPCa. We analyzed data from 277 patients initially classified as non-csPCa after biopsy who underwent radical prostatectomy (RP) between August 2015 and January 2024. Univariate and multivariate logistic regression analyses were performed to identify predictors of csPCa. Receiver operating characteristic curves, calibration curves, and decision curve analyses were used to evaluate the performance of the nomogram model. Differences in biochemical recurrence rates between the non-csPCa group and csPCa group were analyzed using the log-rank test. Overall, 183 (66.1%) patients were reclassified as csPCa on the basis of postoperative pathology, with this group showing a higher incidence of biochemical recurrence versus non-csPCa (14 cases vs 0; P = 0.004). The following factors were independent predictors of csPCa: age, free prostate-specific antigen (fPSA)/total prostate-specific antigen (tPSA) ratio, cumulative cancer length, clinical tumor stage, and PSA density. In addition, a nomogram was developed with good predictive accuracy (area under the curve: 0.782). The substantial inconsistency between biopsy and RP pathology findings in the classification of non-csPCa highlights the limitations of biopsy-only management. The developed nomogram predicting the risk of csPCa provides urologists with a valuable tool for improved risk stratification and PCa management.
Keywords: biochemical recurrence, biopsy, clinically significant prostate cancer, Gleason score
INTRODUCTION
Prostate cancer (PCa) is a widespread malignant disease that impacts men globally and represents a notable public health issue.1 Accurate risk stratification for PCa is essential for making appropriate treatment decisions.2 Furthermore, a widely discussed topic in the diagnosis and clinical management of PCa is how to distinguish clinically significant PCa (csPCa) from non-csPCa.3 Although the definitions of non-csPCa vary between studies, the definitions generally share characteristics, such as low-grade disease, small volume, and local confinement.
Currently, the most widely accepted criteria for the definition of non-csPCa are based on the pathological evaluation of radical prostatectomy (RP) specimens. These criteria incorporate three established prognostic factors: (1) a Gleason score of 6 without Gleason pattern 4 or 5; (2) organ-confined disease, which is characterized by the absence of extraprostatic extension (EPE), seminal vesicle invasion (SVI), or lymph node involvement; and (3) tumor volume less than 0.5 cm3.4,5
In numerous risk assessment systems developed to predict csPCa or non-csPCa, variables obtained from prostate biopsy results play a pivotal role.6,7 Taking the widely accepted Epstein criteria as an example, there are three preoperative standards related to prostate biopsy that are used to predict the probability of non-csPCa. The criteria stipulate an absence of Gleason pattern 4 or 5, fewer than three positive core samples, and no core sample with more than 50% cancer involvement.8 However, the results of preoperative risk assessments are frequently discordant with the postoperative pathological characteristics.9,10 Lee et al.11 reported that only 37% of men who met the preoperative Epstein biopsy criteria were ultimately diagnosed with non-csPCa according to the current definition based on RP specimens. Misjudgment of csPCa can lead to patients receiving treatment options that may not align with their true clinical needs, such as active surveillance instead of more aggressive interventions. Such discrepancies highlight the necessity for enhanced predictive models and the importance of ongoing patient evaluation and management.
Therefore, this study aimed to evaluate the consistency between biopsy and postoperative assessments of non-csPCa based on pathological characteristics and to identify clinicopathological variables and tools that effectively predict csPCa prior to surgery.
PARTICIPANTS AND METHODS
Study design and inclusion and exclusion criteria
In this retrospective study, we collected data for men with PCa diagnosed via biopsy who underwent RP at Fudan University Shanghai Cancer Center (Shanghai, China) from August 2015 to January 2024. The inclusion criteria were as follows: (1) patients who underwent prostate biopsy and RP; (2) biopsy pathology indicating a Gleason score of 3 + 3; (3) fewer than three positive biopsy cores; (4) less than 50% tumor involvement in each core; (5) complete clinical and imaging data; and (6) clinical tumor (T) stage T1 or T2. The exclusion criterion was patients who received androgen deprivation therapy or other treatments prior to RP.
Baseline clinical data, serological data, radiological data, pathology reports from biopsy and RP surgical specimens, and follow-up information were systematically collected. Patients with clinical stage T2c disease were categorized into a separate group and those with stage ≤ T2b disease were assigned to another group. The cumulative prostate cancer length (CCL) referred to the total length of tumor tissue obtained from all biopsy-positive cores. Prostate volume was calculated using the following formula: width × length × height × 0.523. Prostate-specific antigen density (PSAD) was calculated by dividing serum prostate-specific antigen (PSA) levels by prostate volume. Biochemical recurrence (BCR) was defined as undetectable PSA after RP with a subsequent detectable PSA concentration that increased to greater than 0.1 ng ml−1 on two or more measurements.12,13 Data for BCR cases identified using the adjusted PSA threshold of 0.2 ng ml−1 were analyzed separately.
Magnetic resonance imaging (MRI)
Patients included in the study underwent multiparametric MRI (mpMRI) of the prostate using a 3.0-T scanner. The imaging assessments were performed by two radiologists, each with more than 10 years’ experience with the Prostate Imaging-Reporting and Data System (PI-RADS) guidelines evaluation, in accordance with PI-RADS version 2.1.14
Biopsy procedure
Transperineal prostate biopsy procedures under ultrasound guidance were performed by experienced urologists with more than 10 years’ experience. For systematic biopsies (SB), samples were collected bilaterally from the apex to the base, ensuring that cores were obtained as posteriorly and laterally as possible within the peripheral gland, typically a total of 12 cores.15 When MRI revealed a suspicious lesion, cognitive fusion biopsy was used, wherein the operator manually aligned MRI-identified lesions with real-time transperineal ultrasound images to perform targeted biopsy (TB). This approach was chosen owing to its feasibility in routine clinical practice and established effectiveness in detecting csPCa. Additionally, core biopsy specimens were routinely evaluated for the International Society of Urological Pathology (ISUP) grade, length of tumor involvement in each core, and anatomical location.16
Pathological analysis
All enrolled patients underwent either robot-assisted or laparoscopic RP, which was performed by urologic surgeons with more than 15 years’ surgical experience. Prostatectomy specimens were sectioned at 3-mm intervals and graded using the Gleason scoring system by pathologists with more than 5 years’ experience. All pathological analyses were performed in accordance with ISUP grade, following the 2005 update and the 2019 consensus guidelines.16,17 The definition of non-csPCa was based on the pathological evaluation of the RP specimen: (1) Gleason score of 6, without Gleason pattern 4 or 5; (2) organ-confined disease, defined by the absence of EPE, SVI, and lymph node involvement; and (3) tumor volume less than 0.5 cm³.
Statistical analyses
Continuous variables were presented as mean and standard deviation (s.d.), while categorical variables were expressed as numbers and percentages. Fisher’s exact test was used to compare categorical variables, whereas the Wilcoxon rank-sum test was used for continuous variables. Univariate and multivariate logistic regression analyses were performed to identify the predictive factors for csPCa following RP. Variables with P < 0.05 in the univariate analysis were included in the multivariate analysis. On the basis of these findings, we developed a nomogram prediction model. The discriminative ability of the prediction model was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Biochemical recurrence-free survival was estimated using the Kaplan–Meier method, and differences between the groups were compared using the log-rank test. All statistical analyses were performed using R version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria), with P < 0.05 considered statistically significant.
Ethics statement
The present study protocol was reviewed and approved by the Institutional Review Board of Fudan University Shanghai Cancer Center (Approval No. 050432-4-2307E). The study was performed in accordance with the 1964 Helsinki Declaration and its later amendments. Informed consent was waived because of the retrospective study design.
RESULTS
A total of 4122 patients were initially identified, and data for 277 were included for subsequent analysis after applying the inclusion and exclusion criteria (Figure 1). A summary of the participants’ baseline characteristics is presented in Table 1. The participants’ mean age was 66.0 years, with a mean body mass index of 24.18 kg m−2. The mean values for total PSA (tPSA), free PSA (fPSA), and the fPSA/tPSA ratio were 8.54 ng ml−1, 1.18 ng ml−1, and 0.17, respectively. Positive tumor lesions were identified in only one core specimen in 198 (71.5%) patients, while the remaining 79 (28.5%) patients had positive results in two core specimens. The mean length of tumor lesions in the core biopsies was 2.73 mm. The majority of the patients had lesions confined to a single region on one side. An analysis of the clinical staging revealed that 69 (24.9%) cases were classified as T2c (Figure 2). The mean values for prostate volume and PSAD were 47.57 ml and 0.22 ng ml−2, respectively.
Figure 1.
Flowchart of the study inclusion and exclusion criteria. RP: radical prostatectomy; ADT: androgen deprivation therapy; non-csPCa: non-clinically significant prostate cancer.
Table 1.
Comparison of the preoperative clinical and histopathological parameters between the non-clinically significant prostate cancer and clinically significant prostate cancer groups
| Characteristic | All (n=277) | Non-csPCa (n=94; 33.9%) | csPCa (n=183; 66.1%) | a P |
|---|---|---|---|---|
| Age (year), mean±s.d. | 66.0±6.9 | 64.4±7.3 | 66.9±6.5 | 0.005 |
| BMI (kg m−2), mean±s.d. | 24.18±2.60 | 24.01±2.44 | 24.27±2.69 | 0.406 |
| tPSA (ng ml−1), mean±s.d. | 8.54±5.70 | 7.47±5.84 | 9.10±5.56 | 0.028 |
| fPSA (ng ml−1), mean±s.d. | 1.18±0.95 | 1.14±1.02 | 1.19±0.91 | 0.654 |
| fPSA/tPSA (mean±s.d.) | 0.17±0.12 | 0.21±0.17 | 0.15±0.08 | 0.002 |
| Number of positive cores, n (%) | 0.009 | |||
| 1 | 198 (71.5) | 77 (81.9) | 121 (66.1) | |
| 2 | 79 (28.5) | 17 (18.1) | 62 (33.9) | |
| Cumulative prostate cancer length (mm), mean±s.d. | 2.73±2.43 | 2.08±1.77 | 3.07±2.65 | <0.001 |
| Distribution of positive lesions, n (%) | 0.039 | |||
| Unilateral TZ | 81 (29.2) | 31 (33.0) | 50 (27.3) | |
| Unilateral PZ | 148 (53.4) | 56 (59.6) | 92 (50.3) | |
| Bilateral TZ | 5 (1.8) | 1 (1.1) | 4 (2.2) | |
| Bilateral PZ | 8 (2.9) | 0 (0) | 8 (4.4) | |
| Ipsilateral TZ + PZ | 31 (11.2) | 5 (5.3) | 26 (14.2) | |
| Contralateral TZ + PZ | 4 (1.4) | 1 (1.1) | 3 (1.6) | |
| Clinical T stage, n (%) | <0.001 | |||
| ≤ T2b | 208 (75.1) | 87 (92.5) | 121 (66.1) | |
| T2c | 69 (24.9) | 7 (7.4) | 62 (33.9) | |
| Prostate volume (ml), mean±s.d. | 47.57±24.84 | 50.01±22.91 | 46.31±25.75 | 0.223 |
| PI-RADS, n (%) | 0.012 | |||
| 2 | 86 (31.0) | 35 (37.2) | 51 (27.9) | |
| 3 | 68 (24.5) | 26 (27.7) | 42 (22.9) | |
| 4 | 107 (38.6) | 33 (35.1) | 74 (40.4) | |
| 5 | 16 (5.8) | 0 (0) | 16 (8.7) | |
| PSAD (ng ml−2), mean±s.d. | 0.22±0.18 | 0.18±0.16 | 0.24±0.18 | 0.001 |
aCalculated using the Wilcoxon rank-sum test for continuous variables and Fisher’s exact test for categorical variables. s.d.: standard deviation; csPCa: clinically significant prostate cancer; BMI: body mass index; tPSA: total prostate-specific antigen; fPSA: free prostate-specific antigen; TZ: transitional zone; PZ: peripheral zone; T stage: tumor stage; PI-RADS: Prostate Imaging-Reporting and Data System; PSAD: prostate-specific antigen density
Figure 2.
Schematic representation of biopsy sites in a cross-sectional view of the prostate, illustrating the distribution of positive biopsy cores. The red dots indicate cancer foci. TZ: transitional zone; PZ: peripheral zone; U: urethra.
According to the pathological analysis of postoperative RP specimens, 183 (66.1%) patients were reclassified as having csPCa (Supplementary Figure 1 (440.9KB, tif) ). Among them, 150 (54.2%) patients experienced an upgrade in the Gleason score, defined as ≥7 (Supplementary Figure 2a (529.4KB, tif) ). Furthermore, 132 (47.7%) patients had a tumor volume more than 0.5 ml (Supplementary Figure 2b (529.4KB, tif) ). Additionally, 51 (18.4%) patients exhibited EPE and 4 (1.4%) patients showed SVI (Supplementary Figure 2c (529.4KB, tif) ). Patients were grouped by the presence of csPCa, with intergroup comparisons (Table 1). Compared with the non-csPCa group, patients in the csPCa group were older (66.9 years vs 64.4 years; P = 0.005) and had higher tPSA levels (9.10 ng ml−1 vs 7.47 ng ml−1; P = 0.028), lower fPSA/tPSA ratios (0.15 vs 0.21; P = 0.002), higher PSAD values (0.24 ng ml−2 vs 0.18 ng ml−2; P = 0.001), and longer CCLs (3.07 mm vs 2.08 mm; P < 0.001). The csPCa group had a higher likelihood of having a positive core count of 2 (33.9% vs 18.1%; P = 0.009) and being classified as clinical T stage 2c (33.9% vs 7.4%; P < 0.001) compared with the non-csPCa group. Significant differences in PI-RADS scores were observed between the groups (P = 0.012), with a higher proportion of patients in the csPCa group having PI-RADS scores of 4 or 5 (49.2% vs 35.1%, P = 0.026) compared with the non-csPCa group. Follow-up PSA levels revealed that more patients in the csPCa group developed BCR compared with the non-csPCa group (14 cases vs 0; P = 0.004; Figure 3a). Considering that clinical application standards vary over time, adjusting the PSA threshold for BCR to 0.2 ng ml−1 still revealed consistent differences in the BCR rates (csPCa vs non-csPCa: 8 cases vs 0; P = 0.032; Figure 3b).
Figure 3.
Biochemical recurrence-free survival analysis in clinically significant prostate cancer (csPCa) and non-clinically significant prostate cancer (non-csPCa) patients at varying PSA thresholds. (a) Biochemical recurrence-free survival in patients with csPCa and non-csPCa at a PSA threshold of 0.1 ng ml−1. (b) Biochemical recurrence-free survival in patients with csPCa and non-csPCa at a PSA threshold of 0.2 ng ml−1. PSA: prostate-specific antigen; BCR: biochemical recurrence.
In preparation for the logistic regression analysis, we merged PI-RADS categories 4 and 5 into a single group to enhance the robustness of our analysis. Similarly, we categorized the distribution of positive cores into three groups of transitional zone (TZ), peripheral zone (PZ), and combined (TZ + PZ). Based on the results of the univariate analysis, age, tPSA, fPSA/tPSA ratio, number of positive cores, CCL, clinical T stage, distribution of positive lesions, PI-RADS score, and PSAD were identified as clinicopathological features significantly associated with csPCa. The multivariate analysis indicated that age (odds ratio [OR]: 1.07; P = 0.002), fPSA/tPSA ratio (OR: 0.01; P = 0.005), CCL (OR: 1.18; P = 0.028), clinical T stage (OR: 6.31; P < 0.001), and PSAD (OR: 19.2; P = 0.049) were the independent predictors of csPCa (Table 2).
Table 2.
Univariate and multivariate logistic regression analyses of variables associated with clinically significant prostate cancer risk
| Variable | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
|
|
|
|||||
| OR | 95% CI | P | OR | 95% CI | P | |
| Age | 1.06 | 1.02–1.10 | 0.005 | 1.07 | 1.03–1.12 | 0.002 |
| BMI | 1.04 | 0.95–1.15 | 0.420 | |||
| tPSA | 1.06 | 1.01–1.11 | 0.027 | 0.95 | 0.87–1.03 | 0.190 |
| fPSA | 1.07 | 0.82–1.43 | 0.640 | |||
| fPSA/tPSA | 0.01 | 0.00–0.14 | <0.001 | 0.01 | 0.00–0.18 | 0.005 |
| Number of positive cores | ||||||
| 1 | Reference | |||||
| 2 | 2.32 | 1.29–4.37 | 0.007 | 1.17 | 0.49–2.88 | 0.724 |
| Cumulative prostate cancer length | 1.24 | 1.09–1.43 | 0.002 | 1.18 | 1.02–1.39 | 0.028 |
| Clinical T stage | ||||||
| ≤ T2b | Reference | |||||
| T2c | 6.37 | 2.96–15.9 | <0.001 | 6.31 | 2.71–16.9 | <0.001 |
| Distribution of positive lesions | ||||||
| TZ | Reference | |||||
| PZ | 1.06 | 0.61–1.82 | 0.839 | 1.44 | 0.76–2.75 | 0.261 |
| TZ + PZ | 2.87 | 1.13–8.31 | 0.036 | 3.12 | 0.86–12.2 | 0.091 |
| Prostate volume | 0.99 | 0.98–1.00 | 0.243 | |||
| PI-RADS | ||||||
| 2 | Reference | |||||
| 3 | 1.11 | 0.58–2.14 | 0.756 | 1.21 | 0.57–2.58 | 0.624 |
| 4–5 | 1.87 | 1.04–3.38 | 0.036 | 1.38 | 0.69–2.76 | 0.365 |
| PSAD | 12.3 | 2.36–79.6 | 0.005 | 19.2 | 1.15–425.7 | 0.049 |
OR: odds ratio; CI: confidence interval; BMI: body mass index; tPSA: total prostate-specific antigen; fPSA: free prostate-specific antigen; T stage: tumor stage; TZ: transitional zone; PZ: peripheral zone; PI-RADS: Prostate Imaging-Reporting and Data System; PSAD: prostate-specific antigen density
A nomogram was developed using the multiple logistic regression model to estimate the likelihood of csPCa (Figure 4a). The prediction model (AUC: 0.782) demonstrated higher prediction efficiency than individual factors (Figure 4b). Furthermore, a calibration curve evaluating the agreement between predicted probabilities and observed outcomes showed a mean absolute error of 0.012, indicating good model calibration (Supplementary Figure 3a (221.7KB, tif) ). Decision curve analysis further indicated the clinical validity of the model, as the majority of the decision curve remained above the 0 net benefit threshold (Supplementary Figure 3b (221.7KB, tif) ).
Figure 4.
Nomogram and receiver operating characteristic (ROC) curve analysis to predict clinically significant prostate cancer (csPCa). (a) Nomogram incorporating five clinical variables for predicting the probability of csPCa. (b) ROC analysis demonstrating the performance of the nomogram and individual clinical variables, with their respective area under the curve (AUC) values. fPSA: free prostate-specific antigen; tPSA: total prostate-specific antigen; CCL: cumulative cancer length; PSAD: prostate-specific antigen density.
DISCUSSION
The inconsistency in risk assessment between specimens obtained through core biopsy and resection specimens from RP can lead to clinical misdiagnosis and inappropriate treatment decisions.18,19 Accurately distinguishing between indolent non-csPCa and potentially aggressive csPCa is a critical initial step in the comprehensive management of PCa. Our study revealed that among 277 cases, 183 (66.1%) did not meet the criteria for non-csPCa. The majority of cases exhibited discrepancies between the biopsy pathology and RP specimens, emphasizing the importance of our research. Follow-up investigations revealed significant differences in BCR rates between the non-csPCa group and the csPCa group, irrespective of whether a threshold of 0.1 ng ml−1 or 0.2 ng ml−1 was used to define BCR. Notably, there were no instances of BCR in the non-csPCa group. In the subsequent analysis, univariate and multivariate logistic regression analyses identified several effective predictive factors for csPCa occurrence, namely age, fPSA/tPSA ratio, CCL, clinical T stage, and PSAD.
Age is a significant factor associated with the presence of csPCa. Numerous studies have demonstrated that older age at diagnosis correlates with an increased risk of PCa-specific mortality, indicating that age may influence both the biological behavior of the disease and treatment outcomes.20,21 Similarly, the clinical stage is significantly associated with the malignancy of PCa.20,22 Notably, this study included only cases with clinical T stage T1–T2 disease, with T2c specifically categorized as a separate entity owing to involvement of both prostate lobes. Vellekoop et al.23 identified that cancer length >4 mm in patients with a biopsy Gleason score of 6 was a significant predictor of adverse clinical outcomes. Consistent with these results, our findings also support a crucial role for core length measurement in risk stratification for PCa patients. Overall, advanced age, long core length, and high clinical T-stage are accepted factors associated with an increased risk of csPCa.
Our analysis revealed that a high fPSA/tPSA ratio is often associated with a low risk of csPCa. The fPSA/tPSA ratio not only aids in the diagnostic assessment of PCa within the PSA gray zone but also plays a significant role in risk evaluation.24 We also found that elevated PSAD values indicated a high likelihood of aggressive disease. A previous study has indicated that PSAD is a more effective predictor of csPCa compared with using PSA levels alone.25 Corcoran et al.26 verified the efficacy of PSAD to predict Gleason score upgrading for tumors classified as Gleason 3 + 3 and 3 + 4. Consistent with previous research, elevated PSAD was an effective risk assessment factor for csPCa, in our study. A recent Cochrane systematic review indicated that mpMRI has a high negative predictive value to identify csPCa, with the study reporting a negative predictive value of 91% for cancers with an ISUP grade ≥ 2.27
The role of PI-RADS in predicting csPCa has been well-established in previous studies. High PI-RADS scores (≥4) are generally associated with an increased likelihood of csPCa, whereas low PI-RADS scores (≤2) indicate a lower risk of aggressive disease. In our study, we observed a significant difference in PI-RADS score distribution between the csPCa and non-csPCa groups (P = 0.012), supporting its predictive value. However, in the multivariate analysis, PI-RADS did not remain an independent predictor of csPCa (P = 0.365). Although PI-RADS was not included in our final nomogram, it remains a valuable clinical tool that can complement risk stratification models. For instance, in cases in which the nomogram predicts a low probability of csPCa, but the patient has a PI-RADS score 5 lesion, clinicians should not overlook the possibility of pathological upgrading. Conversely, a low PI-RADS score combined with low nomogram prediction may support a more conservative approach, such as active surveillance.
In this study, we developed a nomogram to predict the risk of csPCa using factors included in the multivariate logistic regression analysis, achieving an AUC of 0.782, indicating good predictive utility. This performance compares favorably with previous predictive models. For instance, previous efforts to develop similar nomograms include the work of Nakanishi et al.28 in 2007. The authors’ model incorporated parameters such as age, PSA, prostate volume, and tumor length, and the nomogram achieved an AUC of 0.73. Similarly, Steyerberg et al.29 evaluated a cohort of 247 individuals with their nomogram, which incorporated PSA, tumor volume, Gleason score, and tumor length, with an AUC of 0.77. The variables in our model align closely with those used in previous studies; however, our model exhibited superior predictive performance.
To illustrate how the nomogram can be used in clinical decision-making, we present the following hypothetical case: a 72-year-old patient with a CCL of 5.0 mm, PSAD of 0.28 ng ml−2, fPSA/tPSA of 0.12, and clinical T2c lesion underwent preoperative risk assessment using our nomogram. Each clinical variable is projected vertically onto the upper “Points” scale to derive its individual contribution. These points are summed to generate a total score, which is then mapped to the bottom scale to estimate the probability of csPCa. The patient’s total score corresponds to a predicted probability of 96.5% (Supplementary Figure 4 (128.9KB, tif) ). Given the high likelihood of csPCa, this patient should undergo careful consideration for active surveillance, with close monitoring and follow-up assessments to ensure timely intervention if disease progression occurs. The integration of the nomogram into the decision-making process provides individualized risk estimations, helping clinicians and patients weigh the benefits and risks of different treatment strategies.
Despite the strong predictive performance of our nomogram, several limitations should be acknowledged. First, our data were derived from a single center and collected retrospectively; therefore, selection bias is possible. To mitigate this concern, further validation in external datasets is warranted. Second, we did not thoroughly explore the hidden information provided by mpMRI that incorporates variables such as apparent diffusion coefficient values. Additionally, it is important to note that the majority of the patients in our study underwent SB alone, while only a small subset received a combination of SB and TB. Furthermore, some patients underwent MRI after the initial biopsy, which may have influenced biopsy selection and lesion detection rates. Future studies with larger cohorts are needed to further investigate the potential influence of targeted biopsy on risk stratification and csPCa detection.
CONCLUSIONS
Our study revealed that a substantial proportion of patients initially classified as non-csPCa on the basis of biopsy results were subsequently diagnosed with csPCa after RP, highlighting the limitations of biopsy-based risk stratification. Furthermore, we developed a nomogram with strong predictive performance for assessing csPCa risk, providing a practical tool to assist in clinical decision-making. This model can help identify patients at high risk of pathological upgrading, allowing for more individualized treatment strategies.
AUTHOR CONTRIBUTIONS
YQZ contributed to data curation, formal analysis, and writing of the original draft of the manuscript. ZL contributed to the data curation, statistical analysis, visualization, and the review of the manuscript. BRY contributed to data curation and visualization. SWL contributed to data curation and helped with the formal analysis. ZH participated in data curation and helped with data visualization. FNW contributed to data curation and investigation. HX supervised the project and contributed to the review and editing of the manuscript. BD conceived the study, supervised the project, and contributed to the writing, review, and editing of the manuscript. All authors read and approved the final manuscript.
COMPETING INTERESTS
All authors declare no competing interests.
Definition and distribution of nonclinically significant prostate cancer (non-csPCa) based on biopsy and radical prostatectomy criteria in each core biopsy specimen.
Distribution of pathological features in radical prostatectomy specimens. (a) Distribution of cases by the International Society of Urological Pathology (ISUP) grade group in radical prostatectomy (RP) specimens. (b) Tumor volume categories in RP specimens. (c) Pathological tumor (T) stage distribution in RP specimens.
Evaluation of prediction models for clinically significant prostate cancer (csPCa). (a) Calibration curve depicting the concordance between predicted and observed probabilities for csPCa. (b) Decision curve analysis demonstrating the net benefit across different risk thresholds.
Application of the nomogram to estimate clinically significant prostate cancer (csPCa) risk in a representative patient. fPSA, free prostate-specific antigen; tPSA, total prostate-specific antigen; CCL, cumulative cancer length; PSAD, prostate-specific antigen density.
ACKNOWLEDGMENTS
This study was supported financially by the Shanghai Oriental Talent Program Top Project (No. BJKJ2024007), the Discipline Leader Project of Shanghai Municipal Health Commission (No. 2022XD013), the AoXiang Project of Shanghai Anti-Cancer Association (No. SACA-AX202302), and the PARP Inhibitor Cancer Research Fund (Phase 4) of China Anti-Cancer Association.
Supplementary Information is linked to the online version of the paper on the Asian Journal of Andrology website.
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Associated Data
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Supplementary Materials
Definition and distribution of nonclinically significant prostate cancer (non-csPCa) based on biopsy and radical prostatectomy criteria in each core biopsy specimen.
Distribution of pathological features in radical prostatectomy specimens. (a) Distribution of cases by the International Society of Urological Pathology (ISUP) grade group in radical prostatectomy (RP) specimens. (b) Tumor volume categories in RP specimens. (c) Pathological tumor (T) stage distribution in RP specimens.
Evaluation of prediction models for clinically significant prostate cancer (csPCa). (a) Calibration curve depicting the concordance between predicted and observed probabilities for csPCa. (b) Decision curve analysis demonstrating the net benefit across different risk thresholds.
Application of the nomogram to estimate clinically significant prostate cancer (csPCa) risk in a representative patient. fPSA, free prostate-specific antigen; tPSA, total prostate-specific antigen; CCL, cumulative cancer length; PSAD, prostate-specific antigen density.