PSA Density strongly correlates to Pathology T stage and ISUP grade: Insights from a Cohort of 3,568 Radical Prostatectomy Cases

PSA Density strongly correlates to Pathology T stage and ISUP grade: Insights from a Cohort of 3,568 Radical Prostatectomy Cases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article PSA Density strongly correlates to Pathology T stage and ISUP grade: Insights from a Cohort of 3,568 Radical Prostatectomy Cases Maxime PATTOU, Yann Neuzillet, Tarek GHONEIM, Pierre-Olivier BOSSET, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6214991/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jul, 2025 Read the published version in World Journal of Urology → Version 1 posted 9 You are reading this latest preprint version Abstract Introduction : Prostate-specific antigen density (PSAD) is a valuable detection tool for prostate cancer (PCa) with PSA levels in the "gray zone" (4–10 ng/mL). However, its relationship with final pathology outcomes remains limited, especially on large cohorts. Objective : This study aimed to describe PSAD distributions according to final pathology findings (pathological T stage and ISUP grade) and identify clinical and pathological factors influencing PSAD variations. Methods : We analyzed a prospective cohort of 3,568 patients who underwent radical prostatectomy for PCa in our center between 2007 and 2025. PSAD was calculated using serum PSA (ng/mL) divided by prostate weight (g) from pathology reports. Associations between PSAD and pathology T stage, ISUP grade, total testosterone, cholesterol levels, and statin use were done using Spearman’s correlation coefficient, an ANCOVA analysis and a multiple linear regression. Results : The median PSAD was 0.17 ng/mL/g (IQR: 0.12–0.25). PSAD levels increased gradually with advanced pathology T stage (pT3a and T3b) and higher ISUP grade (p < 0.001 for both). After adjusting for confounding covariates (age and D’Amico risk classification), PSAD remained significantly associated with Pathology T stage and ISUP. In high-risk patients, PSAD was negatively correlated with biopsy ISUP (r=-0.59), pathology ISUP grade (r=-0.49), and total testosterone (r=-0.31). Multivariate analysis identified higher biopsy ISUP grade and PSA levels as positive predictors of PSAD, while age, BMI, and testosterone were negatively associated. Conclusion : PSAD strongly correlates with PCa aggressiveness on pathology, making it a clinically relevant biomarker. Future studies should investigate PSAD’s relation with adverse oncological outcomes, including biochemical recurrence. prostate cancer PSA density radical prostatectomy ISUP grade pathological staging biomarkers risk stratification Figures Figure 1 Figure 2 Figure 3 Introduction Prostate-specific antigen (PSA) is steadily used for prostate cancer (PCa) screening, but several limitations have led to the exploration of PSA density (PSAD) as a more accurate biomarker, especially in patients with PSA levels in the "gray zone" (4–10 ng/mL) [1][2][3][4]. PSAD, defined as the PSA level divided by prostate volume, has been shown to improve the detection and risk stratification of PCa of various clinical scenarios. A recent meta-analysis incorporating modern MRI-based diagnostic has highlighted PSAD’s role in contemporary PCa diagnosis [5]. Beyond detection, PSAD correlates with PCa aggressiveness and pathological features. It has been associated with PSA velocity, pathology Gleason score, total cancer volume and extra-prostatic extension [6][7]. Additionally, PSAD is linked to adverse pathological outcomes, including organ-confined disease, positive surgical margins, and lymph node metastasis [8]. These studies have shown associations, however these results remain to be validated in larger contemporary cohorts. Despite its growing clinical utility, a comprehensive analysis of PSAD distributions and detailed pathology findings is needed: it could enhance risk stratification strategies and improve prostate cancer evaluation. The aim of this study was to describe PSAD levels according to final pathology T stage and ISUP grade [9], as well as to identify clinical and pathological elements influencing PSAD variations. Methods Study design and population We analyzed the Foch Hospital prospective prostatectomy cohort, including all consecutive patients undergoing radical prostatectomy between March 16th, 2007, and Febuary 1st, 2025. Eligible patients had histologically confirmed prostate cancer treated with surgery, while those under legal guardianship or who signed an opposition form to data collection were excluded. All patients gave informed consent and the study was approved by the local ethics committees (IRB00012437). Study objectives and outcomes The primary objective was to assess the association between PSA density, calculated as serum PSA (ng/mL) divided by prostate weight (g) from pathology reports, and pathology TNM stage as well as final ISUP grade. Secondary objectives included evaluating correlations between PSA density and total testosterone (ng/dL) and total cholesterol levels (mg/ml), ISUP grade on preoperative biopsies, patient age, and statin use. Preoperative PSA levels, testosterone and cholesterol levels were all recorded from laboratory data. Statistical Analysis Statistical analyses included descriptive statistics, with continuous variables expressed as median (interquartile range, IQR) and categorical variables as frequencies (%), compared using the Kruskal-Wallis’s test. Analysis of covariance (ANCOVA) was performed with adjustment on known confounding covariates D’Amico risk category and age [10]. Associations between PSA density and study outcomes were assessed using Spearman’s rank correlation. A multiple linear regression was conducted to identify independent variables associated to an increased PSA density. Results A total of 3,568 patients who underwent radical prostatectomy for prostate cancer were included in the study. The median age was 65.5 years (IQR: 60.3–69.7), and the median PSA level was 7.33 ng/mL (IQR: 5.5–10.0). The median PSA density was 0.17 ng/mL/g (IQR: 0.12–0.25). The median prostate weight was 42.6 g (IQR: 33.5–55.6). Regarding tumor characteristics, 99.2% of patients had a clinically localized disease, while 0.8% were locally advanced (T3a–T3b) at diagnosis. Biopsy ISUP grade 2 and 3 were the most frequent : 51.2% and 20.2%, respectively. Based on D’Amico risk classification, 71.5% of patients had intermediate-risk prostate cancer. Final pathology after radical prostatectomy showed that 39.9% of tumors were staged pT2c, while 42.3% had extra prostatic extension (pT3a–T3b). The distribution of pathology ISUP grades was ISUP 2 (55%), ISUP 3 (28.6%), and ISUP 4–5 (7.8%). PSA density was significantly associated with pathology T stage and ISUP grade (p < 0.001 for both, pairwise comparisons detail is in Table 2 ). PSA density increased with pathology T stage, ranging from 0.14 ng/mL/g (IQR: 0.10–0.20) in pT2a tumors to 0.23 ng/mL/g (IQR: 0.15–0.35) in pT3b tumors (Table 1 and Fig. 1 ). PSA density was lowest in pathology ISUP 1 tumors (0.13 ng/mL/g, IQR: 0.10–0.18) and highest in ISUP 5 tumors (0.20 ng/mL/g, IQR: 0.14–0.30) (Fig. 1 ). As shown in Fig. 2 , after adjustment to two confounding covariates, D’Amico risk classification and Age, PSA density levels remained statistically different across all T stage and ISUP grade subgroups (both overall p < 0.001 in the ANCOVA). Figure 3 presents a correlation heatmap illustrating the relationship between PSA density and various clinical and pathological parameters in the full population (n = 3,568) and stratified by D’Amico risk classification. In the full population, PSA density showed a positive correlation with pathological T stage (r = 0.25), biopsy ISUP grade (r = 0.15), and pathology ISUP grade (r = 0.16), suggesting that higher PSA density is associated with more advanced and aggressive disease. In high-risk patients, PSA density was strongly negatively correlated with biopsy ISUP (r = -0.59) and pathology ISUP grade (r = -0.49) as well as with total testosterone (r = -0.31). In multivariate analysis (Table 3 ), age (OR = 0.998, p < 0.001), BMI (OR = 0.997, p = 0.002), and total testosterone (OR = 0.996, p = 0.031) were negatively associated with PSA density, while higher biopsy ISUP grades (p < 0.05 for ISUP 2–4) and PSA levels (p 6 ng/mL) were positively associated. Statin use was not significantly correlated with PSA density (p = 0.200). Discussion In this study we showed that PSAD levels are directly correlated to pathology findings after radical prostatectomy, even after adjustment on confounding variables. The PSAD regression model levels seems to be influenced by clinical as well as biological factors. Y.-S. Ha et al identified an optimal PSAD cutoff of 0.085 ng/mL 2 for predicting advanced disease at the time of surgery of low risk PCa [11] with a sensitivity of 76.7% and specificity of 50.6%. Applying this cutoff in our low risk cohort gives a 90.3% sensitivity, however specificity falls to 11.9%: this could indicate that while the 0.085 ng/mL cutoff catches almost all advanced diseases, it is unspecific. Interestingly in our study, PSAD of advanced disease had a median of 0.19 ng/ml/g IQR(0.13–0.28) : thus suggesting that the cutoff could be risen. Artificial intelligence tools could help to better predict PCa aggressiveness through PSAD. A neural network integrating PSAD with other PSA-derived biomarkers (total PSA, free PSA, and p2PSA) achieved a sensitivity and specificity of 86% and 89% respectively in predicting high-grade disease [12]. In high-risk patients, PSA density was strongly negatively correlated with biopsy ISUP (r = -0.59) and pathology ISUP grade (r = -0.49), this has been shown previously as high-grade PCa (ISUP ≥ 4) produce less PSA per unit of tumor volume, thus reducing the PSAD levels [13]. Additionally, a negative correlation with total testosterone (r = -0.31) was observed, indicating that lower levels may be associated with higher PSAD in high-risk prostate cancer. This seems to comfort previous studies that showed that lower testosterone levels were associated with more aggressive PCa [14][15]. This hypothesis was formulated by Wu et al. in 2018 : aggressive PCa tend to develop growth through androgen stimulation of the androgen receptor, and not directly androgens [16]. Our rates of ISUP 4 and 5 (7.8%) as the rate of pT ≥ 3a (42.4%) were similar to other studies [17]. The linear regression model did not find total cholesterol to be significant (0.311 in the univariate regression) nor the use of statins (p = 0.201 in the multivariate regression) although they had been associated previously with total PSA levels [18]. This descriptive work did not evaluate the association between PSAD and negative oncological outcomes such as upstaging and upgrading, as well as biochemical recurrence rates. These outcomes are our future directions. Several studies have demonstrated the predictive value of PSAD for pathological upgrading (AUCs of 0.712), especially in non-apical PCa, and upstaging (AUCs of 0.628) after radical prostatectomy [21][22]. Declarations Author Contribution M.P., Y.N., and T.L.: Writing the manuscript.M.P.: Statistical analysis and Figures.P.M.L. and D.B.: Assisted with figure and table formatting.P.O.B, TG, V.V. and Y.S.: Supervised study design and methodological framework.All authors reviewed the manuscript and participated to patient recruitment. References J. S. Jue et al. , “Re-examining Prostate-specific Antigen (PSA) Density: Defining the Optimal PSA Range and Patients for Using PSA Density to Predict Prostate Cancer Using Extended Template Biopsy,” Urology , vol. 105, pp. 123–128, Jul. 2017, doi: 10.1016/j.urology.2017.04.015. W. J. Catalona et al. , “Comparison of percent free PSA, PSA density, and age-specific PSA cutoffs for prostate cancer detection and staging,” Urology , vol. 56, no. 2, pp. 255–260, Aug. 2000, doi: 10.1016/S0090-4295(00)00637-3. S. Al-Khalil, C. Ibilibor, J. Cammack, and W. 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Tables Table 1 : General population characteristics Variables (n=3568) Age , years , median (IQR) 65.47 (60.26 - 69.73) BMI , kg/m2 , median (IQR) 25.99 (23.96 - 28.41) PSA , ng/ml , median (IQR) 7.33 (5.5 - 10) PSA Density , ng/ml/g , median (IQR) 0.17 (0.12 - 0.25) Total cholesterol , mg/dL , median (IQR) 1.89 (1.67 - 2.15) Total Testosterone , ng/ml/g, median (IQR) 4.5 (3.5 - 5.6) Prostate weight , g, median (IQR) 42.6 (33.5 - 55.6) Biopsy ISUP grade , n (%) 1 792 (22.2%) 2 1828 (51.2%) 3 721 (20.2%) 4 142 (4%) 5 73 (2%) D’Amico risk classification , n (%) Low (<cT3a, ISUP 1, PSA <10) 621 (17.4%) Intermediate (<cT3a, ISUP 2 or 3, PSA 10-20) 2551 (71.5%) High (<cT3a, ISUP ≥4, PSA ≥20) 369 (10.3%) Locally advanced (≥cT3a, any ISUP, any PSA ≥20) 27 (0.8%) UCSF CAPRA score , median (IQR) 3 (3 - 5) Prostatectomy ISUP grade , n (%) 1 304 (8.5%) 2 1962 (55%) 3 1020 (28.6%) 4 118 (3.3%) 5 161 (4.5%) Pathology T Stage , n (%) 2a 335 (9.4%) 2b 264 (8.4%) 2c 1424 (39.9%) 3a 1196 (33.5%) 3b 315 (8.8%) 4a 2 (0.1%) Positive margins , n (%) 1169 (32.8%) Surgeon annual RP cases , median (IQR) 30 (20 - 40) Pathologist annual RP cases , median (IQR) 78 (44 - 132) Table 2 : PSA Density distribution according to pathological results Variables PSA Density , ng/ml/g , median (IQR) Overall (p-value) Pairwise comparisons (p-value) Pathology T Stage <0.001 2a 0.14 (0.1 - 0.2) 2a vs 2b (p=0.005), vs 2c (p=0.002) 2b 0.15 (0.11 - 0.23) 2a vs 3a/3b (p<0.001) 2c 0.16 (0.11 - 0.22) 2b vs 2c, (p=0.539) 3a 0.19 (0.13 - 0.28) 2b vs 3a/3b (p<0.001) 3b 0.23 (0.15 - 0.35) 2c vs 3a/3b (p<0.001) 3a vs 3b, p=0.001 Prostatectomy ISUP grade <0.001 1 0.13 (0.1 - 0.18) 1 vs all (p<0.001) 2 0.16 (0.12 - 0.24) 2 vs 3/5 (p<0.001), 2 vs 4 (p=0.874) 3 0.19 (0.13 - 0.28) 3 vs 4 (p=0.039), 3 vs 5 (p=0.248) 4 0.17 (0.11 - 0.27) 4 vs 5: (p=0.017) 5 0.2 (0.14 - 0.3) Overall comparison using Kruskal-Wallis test, Pairwise comparisons using Mann-Whitney test with Bonferroni adjustment. Table 3 : Multivariate Linear Regression Model of PSA Density. Multiple Linear regression for PSA Density Odds Ratio Lower CI Upper CI P-Value Age PR , years 0.998 0.997 0.999 <0,001 BMI , kg/m 2 0.997 0.996 0.999 0.002 Total Testosterone , ng/dL 0.996 0.993 1.000 0.031 Biopsy ISUP 1 ref - - - 2 1.014 1.001 1.027 0.038 3 1.024 1.006 1.043 0.009 4 1.030 1.002 1.060 0.036 5 0.978 0.911 1.050 0.536 PSA levels , ng/mL 0 to 6 ref - - - 6 to 10 1.057 1.043 1.072 <0,001 10 to 20 1.158 1.140 1.177 <0,001 20 to 30 1.447 1.393 1.501 <0,001 ≥30 1.704 1.625 1.788 <0,001 Statine use 1.009 0.995 1.023 0.201 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jul, 2025 Read the published version in World Journal of Urology → Version 1 posted Editorial decision: Revision requested 10 Jun, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviews received at journal 03 May, 2025 Reviewers agreed at journal 03 May, 2025 Reviewers invited by journal 01 May, 2025 Editor assigned by journal 13 Mar, 2025 Submission checks completed at journal 13 Mar, 2025 First submitted to journal 12 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6214991","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451525037,"identity":"f4f9c2e0-68a6-4785-a029-c74e2371d69d","order_by":0,"name":"Maxime PATTOU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYJCCAwhmBRAzMzcQqYWNGUicAWlhJKyFAa6FsQ3EIqBFt/2M4aEbFQx5/PP7Dz7mnVcbzd8O1PKjYhtOLWZn0hIO55xhKJY4xsxszLvteO6Mw4wNjD1nbuPWciD5wOHcNobEhmPMbNK8247lNgC1MDO24dFy/mHD4dx/DInzwVrmHMudT1DLDZAtDQyJG8BaGmpyNxDW8gzol2MSxYbHko0N5xw7kLsRqOUgXr+czzH+nFNjkyd3+ODDB29q6nLnnT988MGPCtxaoEAiAco4DCYPEFIPAjAtdcQoHgWjYBSMghEGAPmlXfOZRJHRAAAAAElFTkSuQmCC","orcid":"","institution":"Hôpital Foch","correspondingAuthor":true,"prefix":"","firstName":"Maxime","middleName":"","lastName":"PATTOU","suffix":""},{"id":451525038,"identity":"54970795-aba5-44b7-9983-402f646f0687","order_by":1,"name":"Yann Neuzillet","email":"","orcid":"","institution":"Hôpital Foch","correspondingAuthor":false,"prefix":"","firstName":"Yann","middleName":"","lastName":"Neuzillet","suffix":""},{"id":451525039,"identity":"6f5e173f-d939-4a24-911a-2ecccf3c39ba","order_by":2,"name":"Tarek GHONEIM","email":"","orcid":"","institution":"Hôpital Foch","correspondingAuthor":false,"prefix":"","firstName":"Tarek","middleName":"","lastName":"GHONEIM","suffix":""},{"id":451525040,"identity":"063ffe82-943d-48c9-a42f-dd4d12831f6f","order_by":3,"name":"Pierre-Olivier BOSSET","email":"","orcid":"","institution":"Hôpital Foch","correspondingAuthor":false,"prefix":"","firstName":"Pierre-Olivier","middleName":"","lastName":"BOSSET","suffix":""},{"id":451525041,"identity":"b1c91166-c0e8-4764-b9ca-b9142f36d638","order_by":4,"name":"Jean-Marie HERVE","email":"","orcid":"","institution":"Hôpital Foch","correspondingAuthor":false,"prefix":"","firstName":"Jean-Marie","middleName":"","lastName":"HERVE","suffix":""},{"id":451525042,"identity":"ee104343-7222-4a46-8a06-5f1ef4c132bd","order_by":5,"name":"Denis BOHIN","email":"","orcid":"","institution":"Hôpital Foch","correspondingAuthor":false,"prefix":"","firstName":"Denis","middleName":"","lastName":"BOHIN","suffix":""},{"id":451525043,"identity":"0895322f-1948-434d-9a92-c4f952813b65","order_by":6,"name":"Pierre-Marie LUGAGNE","email":"","orcid":"","institution":"Hôpital Foch","correspondingAuthor":false,"prefix":"","firstName":"Pierre-Marie","middleName":"","lastName":"LUGAGNE","suffix":""},{"id":451525044,"identity":"b5fe332d-1b1b-4d4e-bb8e-4270d88e977c","order_by":7,"name":"Yanish SOOROJEBALLY","email":"","orcid":"","institution":"Hôpital Foch","correspondingAuthor":false,"prefix":"","firstName":"Yanish","middleName":"","lastName":"SOOROJEBALLY","suffix":""},{"id":451525045,"identity":"7a10488e-b355-4ea0-8c45-a8f58579f39f","order_by":8,"name":"Thierry LEBRET","email":"","orcid":"","institution":"Hôpital Foch","correspondingAuthor":false,"prefix":"","firstName":"Thierry","middleName":"","lastName":"LEBRET","suffix":""}],"badges":[],"createdAt":"2025-03-12 21:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6214991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6214991/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00345-025-05814-y","type":"published","date":"2025-07-17T16:05:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82176725,"identity":"91ee911d-edb9-40b7-b6a5-c9c9b4470ee7","added_by":"auto","created_at":"2025-05-07 11:15:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eUnadjusted PSA Density distribution according to final pathology findings\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDistribution with Median (IQR). P-values : ***(\u0026lt;0.001), **(\u0026lt;0.01) and *(\u0026lt;0.05).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6214991/v1/d7cea4c8d4cd94f350a0aa78.png"},{"id":82176726,"identity":"e2448810-827f-4722-a0b2-d31a405d46ab","added_by":"auto","created_at":"2025-05-07 11:15:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAdjusted PSA Density distribution according to final pathology findings\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePSA density was adjusted to D’Amico risk category and Age. Distribution with Mean (95%CI). Overall p-value for difference \u0026lt;0.001 in both cases. Both models R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ewere 0.47 and 0.17 respectively.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6214991/v1/176294d0db8d29166a413bae.png"},{"id":82179020,"identity":"ede9a43f-b783-4089-9a5e-1200b24787a9","added_by":"auto","created_at":"2025-05-07 11:31:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation heatmap of PSA density with secondary outcomes, stratified on the D’Amico classification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSpearman correlation coefficients. PSA density - Biopsy ISUP was not correlated in the low risk category (all ISUP=1).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6214991/v1/b47f992c66a44fe8dc82096e.png"},{"id":88506098,"identity":"f0f65269-0c4c-4514-8d1e-b044db780d64","added_by":"auto","created_at":"2025-08-07 07:30:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":994881,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6214991/v1/025a47c5-6058-41e4-a560-f7f143a48e6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PSA Density strongly correlates to Pathology T stage and ISUP grade: Insights from a Cohort of 3,568 Radical Prostatectomy Cases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate-specific antigen (PSA) is steadily used for prostate cancer (PCa) screening, but several limitations have led to the exploration of PSA density (PSAD) as a more accurate biomarker, especially in patients with PSA levels in the \"gray zone\" (4\u0026ndash;10 ng/mL) [1][2][3][4]. PSAD, defined as the PSA level divided by prostate volume, has been shown to improve the detection and risk stratification of PCa of various clinical scenarios. A recent meta-analysis incorporating modern MRI-based diagnostic has highlighted PSAD\u0026rsquo;s role in contemporary PCa diagnosis [5].\u003c/p\u003e \u003cp\u003eBeyond detection, PSAD correlates with PCa aggressiveness and pathological features. It has been associated with PSA velocity, pathology Gleason score, total cancer volume and extra-prostatic extension [6][7]. Additionally, PSAD is linked to adverse pathological outcomes, including organ-confined disease, positive surgical margins, and lymph node metastasis [8]. These studies have shown associations, however these results remain to be validated in larger contemporary cohorts.\u003c/p\u003e \u003cp\u003eDespite its growing clinical utility, a comprehensive analysis of PSAD distributions and detailed pathology findings is needed: it could enhance risk stratification strategies and improve prostate cancer evaluation. The aim of this study was to describe PSAD levels according to final pathology T stage and ISUP grade [9], as well as to identify clinical and pathological elements influencing PSAD variations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eWe analyzed the Foch Hospital prospective prostatectomy cohort, including all consecutive patients undergoing radical prostatectomy between March 16th, 2007, and Febuary 1st, 2025. Eligible patients had histologically confirmed prostate cancer treated with surgery, while those under legal guardianship or who signed an opposition form to data collection were excluded. All patients gave informed consent and the study was approved by the local ethics committees (IRB00012437).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy objectives and outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary objective was to assess the association between PSA density, calculated as serum PSA (ng/mL) divided by prostate weight (g) from pathology reports, and pathology TNM stage as well as final ISUP grade. Secondary objectives included evaluating correlations between PSA density and total testosterone (ng/dL) and total cholesterol levels (mg/ml), ISUP grade on preoperative biopsies, patient age, and statin use. Preoperative PSA levels, testosterone and cholesterol levels were all recorded from laboratory data.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses included descriptive statistics, with continuous variables expressed as median (interquartile range, IQR) and categorical variables as frequencies (%), compared using the Kruskal-Wallis\u0026rsquo;s test. Analysis of covariance (ANCOVA) was performed with adjustment on known confounding covariates D\u0026rsquo;Amico risk category and age [10]. Associations between PSA density and study outcomes were assessed using Spearman\u0026rsquo;s rank correlation. A multiple linear regression was conducted to identify independent variables associated to an increased PSA density.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results ","content":"\u003cp\u003eA total of 3,568 patients who underwent radical prostatectomy for prostate cancer were included in the study. The median age was 65.5 years (IQR: 60.3\u0026ndash;69.7), and the median PSA level was 7.33 ng/mL (IQR: 5.5\u0026ndash;10.0). The median PSA density was 0.17 ng/mL/g (IQR: 0.12\u0026ndash;0.25). The median prostate weight was 42.6 g (IQR: 33.5\u0026ndash;55.6). Regarding tumor characteristics, 99.2% of patients had a clinically localized disease, while 0.8% were locally advanced (T3a\u0026ndash;T3b) at diagnosis. Biopsy ISUP grade 2 and 3 were the most frequent : 51.2% and 20.2%, respectively. Based on D\u0026rsquo;Amico risk classification, 71.5% of patients had intermediate-risk prostate cancer. Final pathology after radical prostatectomy showed that 39.9% of tumors were staged pT2c, while 42.3% had extra prostatic extension (pT3a\u0026ndash;T3b). The distribution of pathology ISUP grades was ISUP 2 (55%), ISUP 3 (28.6%), and ISUP 4\u0026ndash;5 (7.8%).\u003c/p\u003e \u003cp\u003ePSA density was significantly associated with pathology T stage and ISUP grade (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both, pairwise comparisons detail is in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PSA density increased with pathology T stage, ranging from 0.14 ng/mL/g (IQR: 0.10\u0026ndash;0.20) in pT2a tumors to 0.23 ng/mL/g (IQR: 0.15\u0026ndash;0.35) in pT3b tumors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PSA density was lowest in pathology ISUP 1 tumors (0.13 ng/mL/g, IQR: 0.10\u0026ndash;0.18) and highest in ISUP 5 tumors (0.20 ng/mL/g, IQR: 0.14\u0026ndash;0.30) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, after adjustment to two confounding covariates, D\u0026rsquo;Amico risk classification and Age, PSA density levels remained statistically different across all T stage and ISUP grade subgroups (both overall p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in the ANCOVA).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a correlation heatmap illustrating the relationship between PSA density and various clinical and pathological parameters in the full population (n\u0026thinsp;=\u0026thinsp;3,568) and stratified by D\u0026rsquo;Amico risk classification. In the full population, PSA density showed a positive correlation with pathological T stage (r\u0026thinsp;=\u0026thinsp;0.25), biopsy ISUP grade (r\u0026thinsp;=\u0026thinsp;0.15), and pathology ISUP grade (r\u0026thinsp;=\u0026thinsp;0.16), suggesting that higher PSA density is associated with more advanced and aggressive disease. In high-risk patients, PSA density was strongly negatively correlated with biopsy ISUP (r = -0.59) and pathology ISUP grade (r = -0.49) as well as with total testosterone (r = -0.31).\u003c/p\u003e \u003cp\u003eIn multivariate analysis (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), age (OR\u0026thinsp;=\u0026thinsp;0.998, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (OR\u0026thinsp;=\u0026thinsp;0.997, p\u0026thinsp;=\u0026thinsp;0.002), and total testosterone (OR\u0026thinsp;=\u0026thinsp;0.996, p\u0026thinsp;=\u0026thinsp;0.031) were negatively associated with PSA density, while higher biopsy ISUP grades (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for ISUP 2\u0026ndash;4) and PSA levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 across all PSA categories\u0026thinsp;\u0026gt;\u0026thinsp;6 ng/mL) were positively associated. Statin use was not significantly correlated with PSA density (p\u0026thinsp;=\u0026thinsp;0.200).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we showed that PSAD levels are directly correlated to pathology findings after radical prostatectomy, even after adjustment on confounding variables. The PSAD regression model levels seems to be influenced by clinical as well as biological factors.\u003c/p\u003e \u003cp\u003eY.-S. Ha \u003cem\u003eet al\u003c/em\u003e identified an optimal PSAD cutoff of 0.085 ng/mL\u003csup\u003e2\u003c/sup\u003e for predicting advanced disease at the time of surgery of low risk PCa [11] with a sensitivity of 76.7% and specificity of 50.6%. Applying this cutoff in our low risk cohort gives a 90.3% sensitivity, however specificity falls to 11.9%: this could indicate that while the 0.085 ng/mL cutoff catches almost all advanced diseases, it is unspecific. Interestingly in our study, PSAD of advanced disease had a median of 0.19 ng/ml/g IQR(0.13\u0026ndash;0.28) : thus suggesting that the cutoff could be risen. Artificial intelligence tools could help to better predict PCa aggressiveness through PSAD. A neural network integrating PSAD with other PSA-derived biomarkers (total PSA, free PSA, and p2PSA) achieved a sensitivity and specificity of 86% and 89% respectively in predicting high-grade disease [12].\u003c/p\u003e \u003cp\u003eIn high-risk patients, PSA density was strongly negatively correlated with biopsy ISUP (r = -0.59) and pathology ISUP grade (r = -0.49), this has been shown previously as high-grade PCa (ISUP\u0026thinsp;\u0026ge;\u0026thinsp;4) produce less PSA per unit of tumor volume, thus reducing the PSAD levels [13]. Additionally, a negative correlation with total testosterone (r = -0.31) was observed, indicating that lower levels may be associated with higher PSAD in high-risk prostate cancer. This seems to comfort previous studies that showed that lower testosterone levels were associated with more aggressive PCa [14][15]. This hypothesis was formulated by Wu et al. in 2018 : aggressive PCa tend to develop growth through androgen stimulation of the androgen receptor, and not directly androgens [16].\u003c/p\u003e \u003cp\u003eOur rates of ISUP 4 and 5 (7.8%) as the rate of pT\u0026thinsp;\u0026ge;\u0026thinsp;3a (42.4%) were similar to other studies [17]. The linear regression model did not find total cholesterol to be significant (0.311 in the univariate regression) nor the use of statins (p\u0026thinsp;=\u0026thinsp;0.201 in the multivariate regression) although they had been associated previously with total PSA levels [18].\u003c/p\u003e \u003cp\u003eThis descriptive work did not evaluate the association between PSAD and negative oncological outcomes such as upstaging and upgrading, as well as biochemical recurrence rates. These outcomes are our future directions. Several studies have demonstrated the predictive value of PSAD for pathological upgrading (AUCs of 0.712), especially in non-apical PCa, and upstaging (AUCs of 0.628) after radical prostatectomy [21][22].\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.P., Y.N., and T.L.: Writing the manuscript.M.P.: Statistical analysis and Figures.P.M.L. and D.B.: Assisted with figure and table formatting.P.O.B, TG, V.V. and Y.S.: Supervised study design and methodological framework.All authors reviewed the manuscript and participated to patient recruitment.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ. S. 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Corcoran \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;The ability of prostate‐specific antigen (PSA) density to predict an upgrade in Gleason score between initial prostate biopsy and prostatectomy diminishes with increasing tumour grade due to reduced PSA secretion per unit tumour volume,\u0026rdquo; \u003cem\u003eBJU International\u003c/em\u003e, vol. 110, no. 1, pp. 36\u0026ndash;42, Jul. 2012, doi: 10.1111/j.1464-410X.2011.10681.x.\u003c/li\u003e\n\u003cli\u003eH. Botto, Y. Neuzillet, T. Lebret, P. Camparo, V. Molinie, and J.-P. Raynaud, \u0026ldquo;High Incidence of Predominant Gleason Pattern 4 Localized Prostate Cancer is Associated With Low Serum Testosterone,\u0026rdquo; \u003cem\u003eJournal of Urology\u003c/em\u003e, vol. 186, no. 4, pp. 1400\u0026ndash;1405, Oct. 2011, doi: 10.1016/j.juro.2011.05.082.\u003c/li\u003e\n\u003cli\u003eA. Pichon \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Preoperative low serum testosterone is associated with high-grade prostate cancer and an increased Gleason score upgrading,\u0026rdquo; \u003cem\u003eProstate Cancer Prostatic Dis\u003c/em\u003e, vol. 18, no. 4, pp. 382\u0026ndash;387, Dec. 2015, doi: 10.1038/pcan.2015.44.\u003c/li\u003e\n\u003cli\u003eY. Wu \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Abstract 3746: Targeting adrenal androgen to treat prostate cancer,\u0026rdquo; \u003cem\u003eCancer Research\u003c/em\u003e, vol. 78, no. 13_Supplement, pp. 3746\u0026ndash;3746, Jul. 2018, doi: 10.1158/1538-7445.AM2018-3746.\u003c/li\u003e\n\u003cli\u003eJ. M. Caster, A. D. Falchook, L. H. Hendrix, and R. C. Chen, \u0026ldquo;Risk of Pathologic Upgrading or Locally Advanced Disease in Early Prostate Cancer Patients Based on Biopsy Gleason Score and PSA: A Population-Based Study of Modern Patients,\u0026rdquo; \u003cem\u003eInternational Journal of Radiation Oncology*Biology*Physics\u003c/em\u003e, vol. 92, no. 2, pp. 244\u0026ndash;251, Jun. 2015, doi: 10.1016/j.ijrobp.2015.01.051.\u003c/li\u003e\n\u003cli\u003eD. Zapata, L. E. Howard, E. H. Allott, R. J. Hamilton, K. Goldberg, and S. J. Freedland, \u0026ldquo;Is PSA related to serum cholesterol and does the relationship differ between black and white men?: PSA and Cholesterol Relationship by Race,\u0026rdquo; \u003cem\u003eProstate\u003c/em\u003e, vol. 75, no. 16, pp. 1877\u0026ndash;1885, Dec. 2015, doi: 10.1002/pros.23069.\u003c/li\u003e\n\u003cli\u003eS. M. Bruno \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;PSA Density Help to Identify Patients With Elevated PSA Due to Prostate Cancer Rather Than Intraprostatic Inflammation: A Prospective Single Center Study,\u0026rdquo; \u003cem\u003eFront. Oncol.\u003c/em\u003e, vol. 11, p. 693684, May 2021, doi: 10.3389/fonc.2021.693684.\u003c/li\u003e\n\u003cli\u003eP. H. F. Schatteman, L. Hoekx, J. J. Wyndaele, W. Jeuris, and E. Van Marck, \u0026ldquo;Inflammation in Prostate Biopsies of Men without Prostatic Malignancy or Clinical Prostatitis,\u0026rdquo; \u003cem\u003eEuropean Urology\u003c/em\u003e, vol. 37, no. 4, pp. 404\u0026ndash;412, 2000, doi: 10.1159/000020161.\u003c/li\u003e\n\u003cli\u003eA. Brassetti \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Prostate-specific Antigen Density Is a Good Predictor of Upstaging and Upgrading, According to the New Grading System: The Keys We Are Seeking May Be Already in Our Pocket,\u0026rdquo; \u003cem\u003eUrology\u003c/em\u003e, vol. 111, pp. 129\u0026ndash;135, Jan. 2018, doi: 10.1016/j.urology.2017.07.071.\u003c/li\u003e\n\u003cli\u003eC. Huang, S. He, Q. He, Y. Gong, G. Song, and L. Zhou, \u0026ldquo;Determination of Whether Apex or Non-Apex Prostate Cancer Is the Best Candidate for the Use of Prostate-Specific Antigen Density to Predict Pathological Grade Group Upgrading and Upstaging after Radical Prostatectomy,\u0026rdquo; \u003cem\u003eJCM\u003c/em\u003e, vol. 12, no. 4, p. 1659, Feb. 2023, doi: 10.3390/jcm12041659.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cem\u003eTable 1 : General population characteristics\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"415\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n=3568)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e, \u003cem\u003eyears\u003c/em\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e65.47 (60.26 - 69.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e, \u003cem\u003ekg/m2\u003c/em\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e25.99 (23.96 - 28.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA\u003c/strong\u003e, \u003cem\u003eng/ml\u003c/em\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.33 (5.5 - 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA Density\u003c/strong\u003e, \u003cem\u003eng/ml/g\u003c/em\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.17 (0.12 - 0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal cholesterol\u003c/strong\u003e, \u003cem\u003emg/dL\u003c/em\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.89 (1.67 - 2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Testosterone\u003c/strong\u003e, ng/ml/g, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.5 (3.5 - 5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProstate weight\u003c/strong\u003e, \u003cem\u003eg,\u0026nbsp;\u003c/em\u003emedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e42.6 (33.5 - 55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiopsy ISUP grade\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e792 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1828 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e721 (20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e142 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u0026rsquo;Amico risk classification\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eLow\u0026nbsp;(\u0026lt;cT3a, ISUP 1, PSA \u0026lt;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e621 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eIntermediate\u0026nbsp;(\u0026lt;cT3a, ISUP 2 or 3, PSA 10-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2551 (71.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eHigh\u0026nbsp;(\u0026lt;cT3a, ISUP \u0026ge;4, PSA \u0026ge;20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e369 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003eLocally advanced\u0026nbsp;(\u0026ge;cT3a, any ISUP, any PSA \u0026ge;20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e27 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUCSF CAPRA score\u003c/strong\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e3 (3 - 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProstatectomy ISUP grade\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e304 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1962 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1020 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e118 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e161 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathology T Stage\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e335 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e264 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e2c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1424 (39.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1196 (33.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e315 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e4a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive margins\u003c/strong\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1169 (32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgeon annual RP cases\u003c/strong\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e30 (20 - 40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathologist annual RP cases\u003c/strong\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e78 (44 - 132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2 : PSA Density distribution according to pathological results\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"492\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA Density\u003c/strong\u003e,\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eng/ml/g\u003c/em\u003e, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (p-value)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePairwise\u003c/strong\u003e comparisons (p-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathology T Stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.14 (0.1 - 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2a vs 2b (p=0.005), vs 2c (p=0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.15 (0.11 - 0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2a vs 3a/3b (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.16 (0.11 - 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2b vs 2c, (p=0.539)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.19 (0.13 - 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2b vs 3a/3b (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.23 (0.15 - 0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2c vs 3a/3b (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e3a vs 3b, p=0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProstatectomy ISUP grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.13 (0.1 - 0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e1 vs all (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.16 (0.12 - 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e2 vs 3/5 (p\u0026lt;0.001), 2 vs 4\u0026nbsp;(p=0.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.19 (0.13 - 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e3 vs 4 (p=0.039), 3 vs 5 (p=0.248)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.17 (0.11 - 0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e4 vs 5: (p=0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.2 (0.14 - 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eOverall comparison using Kruskal-Wallis test, Pairwise comparisons using Mann-Whitney test with Bonferroni adjustment.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3 : Multivariate Linear Regression Model of PSA Density.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"374\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 214px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultiple Linear regression for PSA Density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAge PR\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e, years\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMI\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTotal Testosterone\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e, ng/dL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiopsy ISUP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e4\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e5\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePSA levels\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e, ng/mL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003e0 to 6\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eref\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003e6 to 10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003e10 to 20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003e20 to 30\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026ge;30\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0,001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatine use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjur","sideBox":"Learn more about [World Journal of Urology](https://link.springer.com/journal/345)","snPcode":"345","submissionUrl":"https://submission.nature.com/new-submission/345/3","title":"World Journal of Urology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"prostate cancer, PSA density, radical prostatectomy, ISUP grade, pathological staging, biomarkers, risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-6214991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6214991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003eIntroduction :\u003c/u\u003e Prostate-specific antigen density (PSAD) is a valuable detection tool for prostate cancer (PCa) with PSA levels in the \"gray zone\" (4–10 ng/mL). However, its relationship with final pathology outcomes remains limited, especially on large cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eObjective\u003c/u\u003e: This study aimed to describe PSAD distributions according to final pathology findings (pathological T stage and ISUP grade) and identify clinical and pathological factors influencing PSAD variations.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eMethods\u003c/u\u003e: We analyzed a prospective cohort of 3,568 patients who underwent radical prostatectomy for PCa in our center between 2007 and 2025. PSAD was calculated using serum PSA (ng/mL) divided by prostate weight (g) from pathology reports. Associations between PSAD and pathology T stage, ISUP grade, total testosterone, cholesterol levels, and statin use were done using Spearman’s correlation coefficient, an ANCOVA analysis and a multiple linear regression.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eResults\u003c/u\u003e: The median PSAD was 0.17 ng/mL/g (IQR: 0.12–0.25). PSAD levels increased gradually with advanced pathology T stage (pT3a and T3b) and higher ISUP grade (p \u0026lt; 0.001 for both). After adjusting for confounding covariates (age and D’Amico risk classification), PSAD remained significantly associated with Pathology T stage and ISUP. In high-risk patients, PSAD was negatively correlated with biopsy ISUP (r=-0.59), pathology ISUP grade (r=-0.49), and total testosterone (r=-0.31). Multivariate analysis identified higher biopsy ISUP grade and PSA levels as positive predictors of PSAD, while age, BMI, and testosterone were negatively associated.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConclusion\u003c/u\u003e: PSAD strongly correlates with PCa aggressiveness on pathology, making it a clinically relevant biomarker. Future studies should investigate PSAD’s relation with adverse oncological outcomes, including biochemical recurrence.\u003c/p\u003e","manuscriptTitle":"PSA Density strongly correlates to Pathology T stage and ISUP grade: Insights from a Cohort of 3,568 Radical Prostatectomy Cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 11:14:57","doi":"10.21203/rs.3.rs-6214991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-10T20:22:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-18T21:14:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279462245852814798615486978495737066589","date":"2025-05-07T20:25:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-03T19:35:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22286956617874749370312216278739558155","date":"2025-05-03T18:46:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T20:52:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-13T17:36:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-13T17:19:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Urology","date":"2025-03-12T21:41:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-urology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjur","sideBox":"Learn more about [World Journal of Urology](https://link.springer.com/journal/345)","snPcode":"345","submissionUrl":"https://submission.nature.com/new-submission/345/3","title":"World Journal of Urology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"21fc63e8-c70f-403a-b16c-d9d69527c2c8","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T07:13:24+00:00","versionOfRecord":{"articleIdentity":"rs-6214991","link":"https://doi.org/10.1007/s00345-025-05814-y","journal":{"identity":"world-journal-of-urology","isVorOnly":false,"title":"World Journal of Urology"},"publishedOn":"2025-07-17 16:05:44","publishedOnDateReadable":"July 17th, 2025"},"versionCreatedAt":"2025-05-07 11:14:57","video":"","vorDoi":"10.1007/s00345-025-05814-y","vorDoiUrl":"https://doi.org/10.1007/s00345-025-05814-y","workflowStages":[]},"version":"v1","identity":"rs-6214991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6214991","identity":"rs-6214991","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}