Prostate MRI and Clinicopathologic Risk Calculator to Predict Laterality of Extraprostatic Extension at Radical Prostatectomy

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We evaluated predictors of side-specific EPE on surgical pathology including MRI characteristics and developed side-specific EPE risk calculators. Methods This was a retrospective cohort of patients evaluated with mpMRI prior to radical prostatectomy (RP) in our eleven hospital healthcare system from July 2018-November 2022. The dominant side was defined pre-operatively using a tiered system based on laterality of highest biopsy Gleason Grade Group (GG), highest PIRADS lesion, number of lesions, and cancer volume. Univariable and multivariable logistic regression were performed for overall EPE, dominant side EPE, and non-dominant side EPE. Internal validation with leave one out and calibration curves were completed. Results EPE was identified in 53% (317/601) of patients at RP. Side-specific factors (PIRADS, GG, abutment) were only associated with EPE on their respective side. Final variables in the model associated with EPE on the dominant and non-dominant sides included age, log PSA density (PSAD), side-specific PIRADS 5, side-specific GG3-5, and percentage positivity of systematic cores. AUCs for dominant and non-dominant side EPE were 0.77 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.84), respectively. MRI-identified abutment and prostate health index (PHI) did not improve model discrimination. Risk calculators available online at https://rossnm1.shinyapps.io/PredictionOfEPELaterality/. Conclusions PSA, side-specific PIRADS, side-specific GG, and percentage positivity of systematic cores were associated with side-specific EPE at RP and incorporated into a risk calculator to assist in surgical planning and nerve-sparing decisions at time of RP. Health sciences/Diseases/Cancer/Cancer therapy Health sciences/Medical research/Outcomes research Health sciences/Biomarkers/Predictive markers prostate MRI risk calculator surgical planning extraprostatic extension nerve sparing Figures Figure 1 Introduction Multiparametric prostate MRI (mpMRI) of the prostate has been shown in several prospective clinical trials to improve detection of clinically significant prostate cancer.( 1 – 3 ) The American Urological Association (AUA), European Association of Urology (EAU), and National Comprehensive Cancer Network (NCCN) recommend pre-biopsy prostate MRI, and the practice has gained widespread adoption.( 4 ) Extraprostatic extension (EPE) at time of radical prostatectomy (RP) is associated with shorter biochemical recurrence free survival and is an important adverse pathologic feature after radical prostatectomy.( 5 ) Traditional calculators for whole gland EPE rely primarily on serum PSA, clinical T stage, and pathologic variables such as Gleason Grade Group (GG), and percentage positivity of systematic cores prior to advent of MRI-guided targeted prostate biopsy.( 6 – 8 ) However, these risk calculators do not predict side-specific risk of EPE important for surgical planning in balancing oncologic outcomes with nerve sparing for erectile function preservation. In addition, mpMRI alone has limited ability for local staging of prostate cancer, with overall sensitivity of 0.57 (95% CI 0.49–0.64), and requires inclusion of other variables for side-specific EPE prediction.( 9 ) A few nomograms have evaluated laterality of lesions on mpMRI to predict side-specific EPE.( 10 – 12 ) However, these tools considered prostate lobes as independent units rather than performing patient level analysis. Furthermore, radiographic characteristics such as MRI capsular abutment, defined as a smooth or irregular convex outward protrusion of the prostate margin continuous with the tumor of a targetable mpMRI lesion, and lesion PIRADS have not been evaluated or included in side-specific EPE models. Here we identify side-specific clinicopathologic variables associated with side-specific EPE and subsequently develop risk calculators for prediction of EPE at RP. Subjects and Methods Study cohort We include a cohort of patients with prostate cancer and a pre-operative MRI who underwent radical prostatectomy. Data was acquired from our eleven hospital system from July 2018-November 2022. The patient data used in this study was approved by the Northwestern University Institutional Review Board according to the IRB protocol STU00214996. Patient clinical characteristics Baseline demographic information was collected, including: age, ethnicity, insurance status, Charlson Comorbidity Index (CCI), prostate health index (PHI, if available), digital rectal exam (normal or abnormal), and family history (defined as first degree relative with prostate cancer). Serum testing including PSA, % free PSA, and PHI. Patients received 3 Tesla mpMRI evaluated by specialized genitourinary radiologists. mpMRI specific variables including PIRADS (reported as PIRADS v2 if before 2019 or PIRADS v2.1 if 2019 or later), PIRADS laterality, presence and laterality of capsular abutment, MRI prostate size, and PSA density. PHI was categorized into previously defined quartiles (0-26.9, 27-35.9, 36-54.9, and ≥ 55). Additionally, PSA density (PSAD) was stratified into four groups: ≤0.10, 0.10–0.15, 0.15–0.2, and > 0.20 ng/mL/cc, and prostate volume was categorized into 80 cc. Patients with suggestion of frank EPE on MRI and rated as PIRADS 5 were included although this was an uncommon solitary factor leading to classification as PIRADS 5. MRI characteristics and dominant laterality MRI fusion biopsies were completed with either transrectal or transperineal approach (PrecisionPoint, Cumberland, MD) and with either cognitive or software fusion (Uronav, Phillips, Gainesville, FL) per provider preference. Suspicious lesions were biopsied with 2–5 cores, followed by systematic biopsies. Biopsy data was obtained with highest overall and side specific Gleason Grade Group as well as stratification of region of interest (ROI) cores and non-ROI (systematic) biopsy cores. Dominant side based on preoperative factors was determined through a tiered system (listed from most important variables): side-specific Gleason Grade, side-specific PIRADS, side-specific number of PIRADS 3–5 lesions, lesions size of MRI, and percentage positivity on systematic cores were utilized to determine dominant side. Outcomes Our primary outcome was side-specific EPE on RP surgical pathology. A secondary outcome was overall EPE at RP. RP was completed by expert urologic oncologists, and pathology was reviewed by genitourinary pathologists. Pathologic information such as pathologic T stage, laterality of EPE, and pathologic N stage were obtained from pathology reports. Statistical analysis Statistical analyses were performed with R (Version 4.2.2). Descriptive statistics were compared with Pearson’s T test, Chi-squared test, and Mann-Whitney U test to determine factors associated with EPE. Univariable logistic regression was performed to identify factors associated with overall and side-specific EPE for inclusion into predictive models. PSA and PSAD were log transformed given non-normal distribution. Variables that were significantly associated with our outcomes in the univariable regression analysis were included in the multivariable logistic regression models. While age was not significant in all models on multivariable analysis, age was included in models due to the known association between age and prostate cancer.( 13 ) Models were generated both with and without inclusion of overall and side-specific abutment to evaluate the independent value of radiologist interpretation of MRI abutment. Receiver Operator Curves (ROC) with Area under the Curve values and model performance were compared with DeLong’s test. We also performed internal validation with leave one out cross validation and using bootstrapping with 1000 replicates. The optimism corrected AUCs were calculated in accordance with the TRIPOD statement for prediction models.( 14 ) Calibration analysis was performed as a part of internal validation and the brier score, cox calibration slope, cox calibration intercept, and the Spiegelhalter z statistic were calculated. Statistical significance is defined as p value < 0.05. For overall EPE, we also evaluated AUC for the Memorial Sloan Kettering Cancer Center (MSKCC) pre-prostatectomy model for predicting EPE at RP in the present cohort. Prior evidence suggests MRI parameters may be limited in improving prediction of overall EPE in relation to the MSKCC model.( 15 ) The percentage positivity of systematic cores were calculated and used for predictions to avoid overestimations based on positivity of targeted cores. Results Baseline Characteristics and Overall EPE Extraprostatic extension at RP was identified in 53% (317/601) of men. Factors associated with EPE were age, categorical PHI, PSA, PSAD, highest PIRADS score, MRI abutment, biopsy Gleason Grade, ROI and non-ROI core positivity ( Table 1 ). MRI abutment was noted for 81% of patients (487/601) with good sensitivity (280/317, 88%) but poor specificity (77/284, 27%) for presence of overall EPE. Supplemental Fig. 1 shows overall EPE, dominant side EPE, and non-dominant side EPE stratified by max PIRADS and GG. Table 1: Baseline Characteristics stratified by EPE No EPE (n=284) 1 EPE (n=317) 1 P value Age (years) 62 (56, 67) 64 (57, 69) 0.003 Black race 47 (17%) 43 (14%) 0.3 Insurance status (Private vs. Medicare/Medicaid) 65% Private, 35% Medicare/Medicaid 56% Private, 44% Medicare/Medicaid 0.09 BMI (kg/m 2 ) 27.1 (25, 30.1) 27.2 (25.1, 29.8) >0.9 PSA (ng/mL) 4.9 (3.9, 6.6) 6.6 (4.6, 10.4) <0.001 Abnormal DRE 18 (6.3%) 18 (5.7%) 0.7 Family Hx of Prostate Cancer 78 (27%) 80 (25%) 0.5 PHI Category <0.001 0-26.9 15 (5.3%) 8 (2.5%) 27-35.9 37 (13%) 28 (8.8%) 36-54.9 114 (40%) 72 (23%) ≥55 58 (20%) 135 (43%) Not available 60 (21%) 74 (23%) PSAD Category (ng/mL/cm 3 ) <0.001 ≤ 0.10 103 (36%) 62 (20%) 0.10-0.15 71 (25%) 137 (43%) 0.15-0.2 60 (21%) 60 (19%) ≥ 0.20 50 (18%) 58 (18%) Log PSAD -2.02 (-2.41, -1.60) -1.71 (-2.12, -1.14) <0.001 Max PIRADS <0.001 1 or 2 11 (3.9%) 2 (0.6%) 3 51 (18%) 15 (4.7%) 4 180 (63%) 153 (48%) 5 42 (15%) 147 (46%) Max Gleason Grade <0.001 1 29 (10%) 4 (1.3%) 2 146 (51%) 90 (28%) 3 75 (26%) 133 (4.2%) 4 26 (9.2%) 39 (12%) 5 8 (2.8%) 51 (16%) % Positive non-ROI Cores 23 (11, 40) 38 (22, 58) <0.001 Number of Positive Cores 5 (3, 7) 7 (5, 9) <0.001 Number of Negative Cores 11 (7, 15) 8 (5, 12) <0.001 Prostate volume (cm 3 ) 0.4 80 18 (6.3%) 17 (5.4%) Dominant Side GG <0.001 1 29 (10%) 4 (1.3%) 2 146 (51%) 90 (28%) 3 75 (26%) 133 (42%) 4 26 (9.2%) 39 (12%) 5 8 (2.8%) 51 (16%) Non-Dominant Side GG < 0.001 Negative 138 (48%) 126 (40%) 1 76 (27%) 52 (16%) 2 61 (21%) 68 (21%) 3 9 (3.2%) 48 (15%) 4 2 (0.7%) 12 (3.8%) 5 0 11 (3.5%) Dominant Side PIRADS <0.001 1 or 2 27 (9.5%) 12 (3.8%) 3 43 (15%) 16 (5%) 4 170 (60%) 147 (46%) 5 44 (15%) 142 (45%) Non-Dominant Side PIRADS <0.001 1 or 2 166 (58%) 166 (52%) 3 55 (19%) 30 (9.5%) 4 59 (21%) 85 (27%) 5 4 (1.4%) 36 (11%) Any Abutment 207 (73%) 280 (88%) <0.001 Dominant Abutment 195 (69%) 266 (84%) <0.001 Non-Dominant Abutment 78 (27%) 126 (40%) 0.001 1 median (IQR), n (%) Supplemental Table 1 shows the initial univariable and multivariable analysis for overall EPE, with Table 2 showing the final multivariable model for overall EPE with inclusion of age, logPSAD, max PIRADS, max GG, and percentage positivity of systematic cores. Sensitivity analyses with models varying PSA variations (logPSA, logPSAD) and core volume (absolute number of positive cores, percentage positivity of systematic cores, percentage positive of overall cores) were evaluated without significant difference in overall discriminatory ability. Percentage positivity of systematic cores was selected for consistency with the MSKCC nomogram, and logPSAD was used in models given known associations with clinically significant prostate cancer. Furthermore, PHI and MRI abutment were not significantly associated with EPE after multivariable adjustment and excluded from final models. AUCs for final models predicting EPE are shown in Fig. 1 . Table 2 Final Multivariable Model for Overall EPE Multivariable Analysis OR (95% CI) P value Age (years) 1.02 (1.00, 1.05) 0.07 logPSAD 1.63 (1.22, 2.21) 0.001 Max PIRADS < 0.001 3 Ref 1 or 2 0.70 (0.10, 3.12) 0.7 4 1.97 (1.04, 3.89) 0.04 5 5.21 (2.57, 11) < 0.001 Max GG < 0.001 2 Ref 1 0.37 (0.11, 1.03) 0.08 3 2.00 (1.32, 3.05) 0.001 4 1.82 (0.98, 3.39) 0.06 5 4.70 (2.10, 11.7) < 0.001 % Core Positivity non-ROI cores 1.02 (1.01, 1.02) < 0.001 Side-Specific EPE For dominant side EPE, Supplemental Table 2 shows the univariable and multivariable model with all variables and Table 3 shows the final multivariable model. Non-dominant side biopsy GG or PIRADS lesions were not significantly associated with dominant side EPE. Final variables in multivariable model for dominant side EPE were age, logPSAD, dominant side PIRADS, dominant side GG, and % systematic core positivity with AUC 0.77 (95% CI 0.73–0.80). Dominant side PIRADS 5 and dominant side GG 3–5 were significant associated with dominant side EPE. Table 3 Multivariable Model for Dominant Side EPE Multivariable Analysis OR (95% CI) P value Age (years) 1.02 (1.00, 1.05) 0.07 logPSAD 1.42 (1.09, 1.88) 0.01 Dominant side PIRADS < 0.001 3 Ref 1 or 2 0.47 (0.13, 1.48) 0.2 4 1.82 (0.92, 3.82) 0.1 5 4.87 (2.35, 10.7) < 0.001 Dominant side GG < 0.001 2 Ref 1 0.48 (0.13, 1.33) 0.2 3 2.01 (1.32, 3.06) 0.001 4 2.01 (1.09, 3.72) 0.03 5 3.24 (1.59, 6.88) 0.002 % Core Positivity non-ROI cores 1.01 (1.00, 1.02) 0.02 For non-dominant EPE, Supplemental Table 3 shows the initial univariable and multivariable analysis and Table 4 shows the final multivariable model. There were no dominant side factors such as GG and PIRADS that were significantly associated with non-dominant EPE. Age, logPSAD, non-dominant side PIRADS, non-dominant side GG, and % systematic core positivity were included in the model with AUC 0.79 (95% CI 0.74–0.84). While non-dominant GG reached significance in the original multivariable analysis, it did not reach significance in the final multivariable risk calculator model, perhaps due to limited number of patients with non-dominant side GG 3–5 (n = 82, p = 0.06). Table 4 Multivariable Model for Non-Dominant Side EPE Multivariable Analysis OR (95% CI) P value Age (years) 1.03 (1.00, 1.07) 0.046 logPSAD 1.71 (1.24, 2.39) 0.001 Non-Dominant side PIRADS < 0.001 3 Ref 1 or 2 0.86 (0.40, 2.00) 0.7 4 1.94 (0.89, 4.53) 0.11 5 7.84 (2.89, 22.7) < 0.001 Non-Dominant side GG 0.06 2 Ref Negative 0.90 (0.44, 1.84) 0.8 1 0.68 (0.31, 1.43) 0.3 3 1.59 (0.75, 3.37) 0.2 4 3.46 (0.91, 13.4) 0.07 5 4.39 (0.84, 26.1) 0.08 % Core Positivity non-ROI cores 1.01 (1.00, 1.03) 0.007 Internal validation with leave one out cross validation showed stable estimates and is summarized in Supplemental Table 4 . Calibration curves were completed with leave one out models, and showed concordance between predictive and observed probability, with low Brier score and non-significant Spiegelhalter Z test P values ( Supplemental Table 5, Supplemental Fig. 2 ). Online versions of our risk calculators are available at https://rossnm1.shinyapps.io/PredictionOfEPELaterality/ . Discussion Pre-operative planning with prostate MRI seeks to maximize oncologic control with negative margins while balancing nerve sparing for erectile function preservation. Definitive EPE on MRI is currently graded as a PIRADS 5 lesion, but many PIRADS 5 lesions do not demonstrate definitive EPE. Additionally, EPE is often present on surgical pathology for patients without PIRADS 5 lesions. In our study, we generate a series of side specific risk calculators which may assist with nerve sparing decisions at time of RP or inform radiation dose delivery planning. Importantly, the presence of MRI capsular abutment did not add significant predictive value on multivariable analysis. Traditionally, the Partin Tables(6) and MSKCC pre-prostatectomy nomograms( 16 ) are two commonly utilized tools for prediction of EPE at RP. However, these two tools, which were produced in the pre-MRI era, do not provide information regarding laterality of EPE or visible lesions. Initial nomograms incorporating prostate MRI data into radical prostatectomy for prediction of EPE and SVI at RP do not show significant improvement compared to the traditional MSKCC nomograms.( 15 ) Although we did not directly compare overall EPE in our development cohort with performance of the MSKCC model, the AUC values were similar. Side-specific nomograms have been developed incorporating clinical variables, but treat the prostate gland as two independent sides without assessing for the influence of contralateral lesions.( 10 – 12 ) Martini et al incorporate age, PSA, biopsy gleason grade, % core involvement, and presence of ECE on MRI, while Soeterik et al incorporate PSAD, clinical T stage at DRE, MRI, biopsy GG, presence of MRI lesion and MRI EPE, and % core positivity. Independent, significant factors in our models include age, logPSAD, side specific Gleason Grade, side specific PIRADS, and % systematic core involvement. Our final MRI model incorporates side-specific PIRADS, which is an important variable not included in other models and is used in clinical practice for risk stratification and surgical planning. Importantly, with our risk stratification, we did not find that side-specific EPE was influenced by side-specific GG or PIRADS on the contralateral side. Side-specific GG3-5 and side-specific PIRADS 5 lesions were associated with side-specific EPE, which should encourage providers to consider wider surgical excision on the corresponding side with these characteristics. Similarly, nerve preservation can be aggressively performed unilaterally even in high risk men if adverse indicators are not present contralaterally. Our models did not find that MRI lesion abutment added to side-specific EPE discrimination. There is no consensus on utility of MRI abutment, and lack of a standardized system for local staging has led to interobserver variability and heterogeneity in reporting. To further quantify degree of abutment, length of capsular contact or interface has been previously considered.( 17 – 19 ) A capsular contact of 12.5 mm yielded a sensitivity of 87% and specificity of 62% for presence of EPE, while another study found that a tumor contact length of 14.6 mm provides a sensitivity of 73.3% and specificity of 55.8% for GG 1–3 prostate cancer.( 20 , 21 ) Conversely, a length of capsular contact of < 5 mm had low probability of pathologic EPE. Our current investigation did not find that MRI abutment was a predictive variable, but adopting a standardized system for assessment into PIRADS could improve predictive value in the future. Additionally, imaging analysis incorporating artificial intelligence-driven algorithms may hold promise to improve upon radiologist interpretation.( 22 ) The limitations of our study were related to design as a retrospective, single healthcare system study. Independent validation is required and is the next immediate step for further evaluation of our models. Second, there was lack of central review for prostate MRIs and reflects real world practice. Prostate MRI interpretation has significant interrater variability, and results from center to center may vary based on reader volume and experience.( 23 ) Third, MRI lesion size was not evaluated in favor of PIRADS, and lesion location was not categorized beyond laterality. Finally, in the long term as tools become more widely available, radiomics and artificial intelligence algorithms may be used to further improve our model for local staging with implementation into the electronic medical record.( 24 ) Conclusions Here we offer a series of novel risk calculators for side-specific EPE to assist with surgical planning for RP with regards to nerve sparing and radiation dose delivery planning. While external validation is forthcoming, these calculators are available at https://rossnm1.shinyapps.io/PredictionOfEPELaterality/ . Declarations Declaration of Interest: AER is engaged in consulting with American Society of Clinical Oncology (ASCO), Astellas, Bayer HealthCare Pharmaceuticals, Blue Earth Diagnostics, Janssen Biotech, Lantheus Medical Imaging, Myovant Sciences, NCCN, Pfizer, Tempus Health, and Veracyte. EMS is engaged in consulting with Astellas, Lantheus Medical Imaging, Pfizer. The other contributing authors have no conflicts of interest to declare. Acknowledgements None Ethics Approval and Consent to Participate The study was reviewed under IRB STU00214996 issued by Northwestern University. The study was performed in accordance with the Declaration of Helsinki. Funding Statement There was no source of funding for this research. Data Availability Statement Data are available for bona fide researchers who request it from the authors. Authorship Statement Conceptualization: EL, EMS, HDP, AER Methodology: EL, SK Software/Validation: SK Formal analysis: EL, SK Investigation: EL, JAA, ZS Resources: CN, SK Data Curation: EL, JA, MRS, Writing – Original Draft: EL, JAA Writing- Review & Editing: EL, JAA, MRS, ZS, CN, EMS, AER, HDP Visualization: EL, SK Supervision: EMS, HDP, AER Project administration: HDP, AER Funding acquisition: HDP, AER References Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017;389(10071):815–22. Kasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH, et al. MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis. N Engl J Med. 2018;378(19):1767–77. Padhani AR, Petralia G, Sanguedolce F. Magnetic Resonance Imaging Before Prostate Biopsy: Time to Talk. Eur Urol. 2016;69(1):1–3. Siddiqui MR, Ansbro B, Shah PV, Aguiar JA, Li EV, Rich JM, et al. Real-world use of MRI for risk stratification prior to prostate biopsy. Prostate Cancer Prostatic Dis. 2023;26(2):353–9. Jeong BC, Chalfin HJ, Lee SB, Feng Z, Epstein JI, Trock BJ, et al. The relationship between the extent of extraprostatic extension and survival following radical prostatectomy. Eur Urol. 2015;67(2):342–6. Tosoian JJ, Chappidi M, Feng Z, Humphreys EB, Han M, Pavlovich CP, et al. Prediction of pathological stage based on clinical stage, serum prostate-specific antigen, and biopsy Gleason score: Partin Tables in the contemporary era. BJU Int. 2017;119(5):676–83. Kattan MW, Eastham JA, Stapleton AM, Wheeler TM, Scardino PT. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst. 1998;90(10):766–71. Cooperberg MR, Freedland SJ, Pasta DJ, Elkin EP, Presti JC, Jr., Amling CL, et al. Multiinstitutional validation of the UCSF cancer of the prostate risk assessment for prediction of recurrence after radical prostatectomy. Cancer. 2006;107(10):2384–91. de Rooij M, Hamoen EH, Witjes JA, Barentsz JO, Rovers MM. Accuracy of Magnetic Resonance Imaging for Local Staging of Prostate Cancer: A Diagnostic Meta-analysis. Eur Urol. 2016;70(2):233–45. Soeterik TFW, van Melick HHE, Dijksman LM, Küsters-Vandevelde H, Stomps S, Schoots IG, et al. Development and External Validation of a Novel Nomogram to Predict Side-specific Extraprostatic Extension in Patients with Prostate Cancer Undergoing Radical Prostatectomy. Eur Urol Oncol. 2022;5(3):328–37. Martini A, Gupta A, Lewis SC. Development and internal validation of a side-specific, multparametric magnetic resonance imaging-based nomogram for the prediction of extracapsular extension of prostate cancer. BJU Int. 2018;122(6):1025–33. Veerman H, Heymans MW, van der Poel HG. External Validation of a Prediction Model for Side-specific Extraprostatic Extension of Prostate Cancer at Robot-assisted Radical Prostatectomy. Eur Urol Open Sci. 2022;37:50–2. Godtman RA, Kollberg KS, Pihl CG, Månsson M, Hugosson J. The Association Between Age, Prostate Cancer Risk, and Higher Gleason Score in a Long-term Screening Program: Results from the Göteborg-1 Prostate Cancer Screening Trial. Eur Urol. 2022;82(3):311–7. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement. Eur Urol. 2015;67(6):1142–51. Diamand R, Ploussard G, Roumiguié M, Oderda M, Benamran D, Fiard G, et al. External Validation of a Multiparametric Magnetic Resonance Imaging-based Nomogram for the Prediction of Extracapsular Extension and Seminal Vesicle Invasion in Prostate Cancer Patients Undergoing Radical Prostatectomy. Eur Urol. 2021;79(2):180–5. Stephenson AJ, Scardino PT, Eastham JA, Bianco FJ, Jr., Dotan ZA, Fearn PA, et al. Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Natl Cancer Inst. 2006;98(10):715–7. Freifeld Y, Diaz de Leon A, Xi Y. Diagnostic Performance of Prospectively Assigned Likert Scale Scores to Determine Extraprostatic Extension and Seminal Vesicle Invasion with Multiparametric MRI of the Prostate. AJR Am J Roentgenol. 2019;212(3):576–81. Reisæter LA, Halvorsen OJ, Beisland C. Assessing Extraprostatic Extension with Multiparametric MRI of the Prostate: Mehralivand Extraprostatic Extension Grade or Extraprostatic Extension Likert Scale? Radiol Imaging Cancer. 2020;2(1):e190071. Mehralivand S, Shih JH, Harmon S. A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI. Radiology. 2019;290(3):709–19. Valentin B, Schimmoller L, Ullrich T. Magnetic resonance imaging improves the prediction of tumor stage in localized prostate cancer. Abdom Radiol. 2021;46(6):2751–9. Bakir B, Onay A, Vural M. Can Extraprostatic Extension Be Predicted by Tumor-Capsule Contact Length in Prostate Cancer? Relationship with International Society of Urological Pathology Grade Groups. AJR Am J Roentgenol. 2020;214(7):588–96. van den Berg I, Soeterik TFW, van der Hoeven E, Claassen B, Brink WM, Baas DJH, et al. The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer. Cancers (Basel). 2023;15(22). Westphalen AC, McCulloch CE, Anaokar JM, Arora S, Barashi NS, Barentsz JO, et al. Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology. 2020;296(1):76–84. Moroianu SL, Bhattacharya I, Seetharaman A. Computation Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI using Deep Learning. Cancers (Basel). 2022;14(12):2821. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SideSpecificEPESupplementalMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2025 Read the published version in Prostate Cancer and Prostatic Diseases → Version 1 posted Editorial decision: revise 18 Jun, 2024 Review # 1 received at journal 16 Jun, 2024 Review # 3 received at journal 28 May, 2024 Reviewer # 3 agreed at journal 28 May, 2024 Review # 2 received at journal 27 May, 2024 Reviewer # 2 agreed at journal 27 May, 2024 Reviewer # 1 agreed at journal 26 May, 2024 Reviewers invited by journal 24 May, 2024 Editor assigned by journal 24 May, 2024 Submission checks completed at journal 23 May, 2024 First submitted to journal 22 May, 2024 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. 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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-4459729","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":306364879,"identity":"3c783f85-cf7e-48ad-8f2b-b06b4c499eaa","order_by":0,"name":"Eric Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie3OMWvCQBTA8RcC6XJw60nVfIUnB6WD1K+ScJDJwdEubSVwU3FO6eBXyOiYcHBTugu6iF8g4gfQi1bo4Kljh/sPx+N4P3gALte/zPsAGDFCz/Pf/ysEWbvVbJTNE9wkTQh9LO4l9FFNasBnwpep3m3nqhvOzDCCficvLpPWNE6ZOYw8rbTIykpx1IH4ziDhNoKVJ+FIFkMOpVzGeUC4T0DFNjI4E56dyPtM0p0heytB8kuQnUgEmviGFFbCKi9lkSFskQj4kfterhNzGAr+ZSH082Fd1+O3Ac2EgleZhGGqNj4Zv3SmFnIsunDwlXWXy+Vy3ewAD2xWezeCjHoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5506-5755","institution":"Northwestern University","correspondingAuthor":true,"prefix":"","firstName":"Eric","middleName":"","lastName":"Li","suffix":""},{"id":306364880,"identity":"02b97594-31db-48aa-b37e-ed16e621f730","order_by":1,"name":"Sai Kumar","email":"","orcid":"https://orcid.org/0000-0002-3577-3543","institution":"Feinberg School of Medicine, Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Sai","middleName":"","lastName":"Kumar","suffix":""},{"id":306364881,"identity":"af1c2b81-5ac5-4e52-bb34-166d1ad3199f","order_by":2,"name":"Jonathan Aguiar","email":"","orcid":"https://orcid.org/0000-0001-5418-419X","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Aguiar","suffix":""},{"id":306364882,"identity":"6fe83127-8e75-44fa-a23d-d42dbf3a4667","order_by":3,"name":"Mohammad Siddiqui","email":"","orcid":"","institution":"Feinberg School of Medicine, Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Siddiqui","suffix":""},{"id":306364883,"identity":"1d0aa832-d844-45d1-9b2b-a60980c686f7","order_by":4,"name":"Zequn Sun","email":"","orcid":"","institution":"Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zequn","middleName":"","lastName":"Sun","suffix":""},{"id":306364884,"identity":"a54e3049-bd18-4ab3-b591-9fabf7b070a0","order_by":5,"name":"Clayton Neill","email":"","orcid":"https://orcid.org/0009-0003-3603-9580","institution":"Feinberg School of Medicine, Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Clayton","middleName":"","lastName":"Neill","suffix":""},{"id":306364885,"identity":"74d5a22f-08e9-458e-85c6-6391150a95c0","order_by":6,"name":"Edward Schaeffer","email":"","orcid":"https://orcid.org/0000-0003-0699-1899","institution":"Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Edward","middleName":"","lastName":"Schaeffer","suffix":""},{"id":306364886,"identity":"3dcf0d42-1b37-4441-99ca-5db39a8dab3b","order_by":7,"name":"Ashley Ross","email":"","orcid":"https://orcid.org/0000-0001-7044-9688","institution":"Northwestern University Feinberg School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ashley","middleName":"","lastName":"Ross","suffix":""},{"id":306364887,"identity":"e58903db-3123-488a-aa3f-4c69fa625e25","order_by":8,"name":"Hiten Patel","email":"","orcid":"","institution":"Feinberg School of Medicine, Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Hiten","middleName":"","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2024-05-22 09:20:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4459729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4459729/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41391-024-00928-7","type":"published","date":"2025-01-07T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58147400,"identity":"362caa80-1c7c-40a5-858c-4e10abbb42cd","added_by":"auto","created_at":"2024-06-11 18:42:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAUCs for overall EPE (A), MSKCC nomogram (B), Dominant Side EPE (C), and Non-Dominant Side EPE (D) models\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4459729/v1/e09a413514755061fcb96f86.jpg"},{"id":73149071,"identity":"2a0032d1-d102-498c-a415-874c65f19d1e","added_by":"auto","created_at":"2025-01-07 08:09:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4459729/v1/8525aed2-d675-4390-83a0-5724e0dcb653.pdf"},{"id":58147401,"identity":"44a73637-8a64-4ca2-87a9-fffe02f3fd50","added_by":"auto","created_at":"2024-06-11 18:42:14","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":614353,"visible":true,"origin":"","legend":"","description":"","filename":"SideSpecificEPESupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4459729/v1/5681577e15fb0dae389be994.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Prostate MRI and Clinicopathologic Risk Calculator to Predict Laterality of Extraprostatic Extension at Radical Prostatectomy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiparametric prostate MRI (mpMRI) of the prostate has been shown in several prospective clinical trials to improve detection of clinically significant prostate cancer.(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The American Urological Association (AUA), European Association of Urology (EAU), and National Comprehensive Cancer Network (NCCN) recommend pre-biopsy prostate MRI, and the practice has gained widespread adoption.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eExtraprostatic extension (EPE) at time of radical prostatectomy (RP) is associated with shorter biochemical recurrence free survival and is an important adverse pathologic feature after radical prostatectomy.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Traditional calculators for whole gland EPE rely primarily on serum PSA, clinical T stage, and pathologic variables such as Gleason Grade Group (GG), and percentage positivity of systematic cores prior to advent of MRI-guided targeted prostate biopsy.(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) However, these risk calculators do not predict side-specific risk of EPE important for surgical planning in balancing oncologic outcomes with nerve sparing for erectile function preservation. In addition, mpMRI alone has limited ability for local staging of prostate cancer, with overall sensitivity of 0.57 (95% CI 0.49\u0026ndash;0.64), and requires inclusion of other variables for side-specific EPE prediction.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eA few nomograms have evaluated laterality of lesions on mpMRI to predict side-specific EPE.(\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) However, these tools considered prostate lobes as independent units rather than performing patient level analysis. Furthermore, radiographic characteristics such as MRI capsular abutment, defined as a smooth or irregular convex outward protrusion of the prostate margin continuous with the tumor of a targetable mpMRI lesion, and lesion PIRADS have not been evaluated or included in side-specific EPE models. Here we identify side-specific clinicopathologic variables associated with side-specific EPE and subsequently develop risk calculators for prediction of EPE at RP.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy cohort\u003c/h2\u003e \u003cp\u003eWe include a cohort of patients with prostate cancer and a pre-operative MRI who underwent radical prostatectomy. Data was acquired from our eleven hospital system from July 2018-November 2022. The patient data used in this study was approved by the Northwestern University Institutional Review Board according to the IRB protocol STU00214996.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatient clinical characteristics\u003c/h2\u003e \u003cp\u003eBaseline demographic information was collected, including: age, ethnicity, insurance status, Charlson Comorbidity Index (CCI), prostate health index (PHI, if available), digital rectal exam (normal or abnormal), and family history (defined as first degree relative with prostate cancer). Serum testing including PSA, % free PSA, and PHI. Patients received 3 Tesla mpMRI evaluated by specialized genitourinary radiologists. mpMRI specific variables including PIRADS (reported as PIRADS v2 if before 2019 or PIRADS v2.1 if 2019 or later), PIRADS laterality, presence and laterality of capsular abutment, MRI prostate size, and PSA density. PHI was categorized into previously defined quartiles (0-26.9, 27-35.9, 36-54.9, and \u0026ge;\u0026thinsp;55). Additionally, PSA density (PSAD) was stratified into four groups: \u0026le;0.10, 0.10\u0026ndash;0.15, 0.15\u0026ndash;0.2, and \u0026gt;\u0026thinsp;0.20 ng/mL/cc, and prostate volume was categorized into \u0026lt;\u0026thinsp;30 cc, 30-49.9 cc, 50\u0026ndash;80 cc, and \u0026gt;\u0026thinsp;80 cc. Patients with suggestion of frank EPE on MRI and rated as PIRADS 5 were included although this was an uncommon solitary factor leading to classification as PIRADS 5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMRI characteristics and dominant laterality\u003c/h2\u003e \u003cp\u003eMRI fusion biopsies were completed with either transrectal or transperineal approach (PrecisionPoint, Cumberland, MD) and with either cognitive or software fusion (Uronav, Phillips, Gainesville, FL) per provider preference. Suspicious lesions were biopsied with 2\u0026ndash;5 cores, followed by systematic biopsies. Biopsy data was obtained with highest overall and side specific Gleason Grade Group as well as stratification of region of interest (ROI) cores and non-ROI (systematic) biopsy cores.\u003c/p\u003e \u003cp\u003eDominant side based on preoperative factors was determined through a tiered system (listed from most important variables): side-specific Gleason Grade, side-specific PIRADS, side-specific number of PIRADS 3\u0026ndash;5 lesions, lesions size of MRI, and percentage positivity on systematic cores were utilized to determine dominant side.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eOur primary outcome was side-specific EPE on RP surgical pathology. A secondary outcome was overall EPE at RP. RP was completed by expert urologic oncologists, and pathology was reviewed by genitourinary pathologists. Pathologic information such as pathologic T stage, laterality of EPE, and pathologic N stage were obtained from pathology reports.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed with R (Version 4.2.2). Descriptive statistics were compared with Pearson\u0026rsquo;s T test, Chi-squared test, and Mann-Whitney U test to determine factors associated with EPE. Univariable logistic regression was performed to identify factors associated with overall and side-specific EPE for inclusion into predictive models. PSA and PSAD were log transformed given non-normal distribution. Variables that were significantly associated with our outcomes in the univariable regression analysis were included in the multivariable logistic regression models. While age was not significant in all models on multivariable analysis, age was included in models due to the known association between age and prostate cancer.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eModels were generated both with and without inclusion of overall and side-specific abutment to evaluate the independent value of radiologist interpretation of MRI abutment. Receiver Operator Curves (ROC) with Area under the Curve values and model performance were compared with DeLong\u0026rsquo;s test. We also performed internal validation with leave one out cross validation and using bootstrapping with 1000 replicates. The optimism corrected AUCs were calculated in accordance with the TRIPOD statement for prediction models.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Calibration analysis was performed as a part of internal validation and the brier score, cox calibration slope, cox calibration intercept, and the Spiegelhalter z statistic were calculated. Statistical significance is defined as p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eFor overall EPE, we also evaluated AUC for the Memorial Sloan Kettering Cancer Center (MSKCC) pre-prostatectomy model for predicting EPE at RP in the present cohort. Prior evidence suggests MRI parameters may be limited in improving prediction of overall EPE in relation to the MSKCC model.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) The percentage positivity of systematic cores were calculated and used for predictions to avoid overestimations based on positivity of targeted cores.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics and Overall EPE\u003c/h2\u003e \u003cp\u003eExtraprostatic extension at RP was identified in 53% (317/601) of men. Factors associated with EPE were age, categorical PHI, PSA, PSAD, highest PIRADS score, MRI abutment, biopsy Gleason Grade, ROI and non-ROI core positivity (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). MRI abutment was noted for 81% of patients (487/601) with good sensitivity (280/317, 88%) but poor specificity (77/284, 27%) for presence of overall EPE. \u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e shows overall EPE, dominant side EPE, and non-dominant side EPE stratified by max PIRADS and GG.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1: Baseline Characteristics stratified by EPE\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo EPE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=284)\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPE\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=317)\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\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 width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e62 (56, 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e64 (57, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlack race\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e47 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e43 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance status (Private vs. Medicare/Medicaid)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e65% Private, 35% Medicare/Medicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e56% Private, 44% Medicare/Medicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e27.1 (25, 30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e27.2 (25.1, 29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSA\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e4.9 (3.9, 6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e6.6 (4.6, 10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbnormal DRE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e18 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e18 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Hx of Prostate Cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e78 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e80 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHI Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0-26.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e15 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e8 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e27-35.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e37 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e28 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e36-54.9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e114 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e72 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e58 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e135 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot available\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e60 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e74 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSAD Category (ng/mL/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le; 0.10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e103 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e62 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.10-0.15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e71 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e137 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.15-0.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e60 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e60 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge; 0.20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e50 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e58 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog PSAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e-2.02 (-2.41, -1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e-1.71 (-2.12, -1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax PIRADS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 or 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e11 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e2 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e51 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e15 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e180 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e153 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e42 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e147 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax Gleason Grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e29 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e4 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e146 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e90 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e75 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e133 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e26 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e39 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e8 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e51 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Positive non-ROI Cores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e23 (11, 40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e38 (22, 58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Positive Cores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e5 (3, 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e7 (5, 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Negative Cores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e11 (7, 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e8 (5, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProstate volume (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e73 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e86 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e121 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e150 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e50-80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e72 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e64 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e18 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e17 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant Side GG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e29 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e4 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e146 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e90 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e75 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e133 (42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e26 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e39 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e8 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e51 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Dominant Side GG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e138 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e126 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e76 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e52 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e61 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e68 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e9 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e48 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e2 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e12 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e11 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant Side PIRADS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 or 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e27 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e12 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e43 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e16 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e170 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e147 (46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e44 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e142 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Dominant Side PIRADS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 or 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e166 (58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e166 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e55 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e30 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e59 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e85 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e4 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e36 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny Abutment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e207 (73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e280 (88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant Abutment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e195 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e266 (84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Dominant Abutment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.075471698113205%\" valign=\"top\"\u003e\n \u003cp\u003e78 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.41509433962264%\" valign=\"top\"\u003e\n \u003cp\u003e126 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.09433962264151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e\u003cstrong\u003emedian (IQR), n (%)\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e shows the initial univariable and multivariable analysis for overall EPE, with Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e showing the final multivariable model for overall EPE with inclusion of age, logPSAD, max PIRADS, max GG, and percentage positivity of systematic cores. Sensitivity analyses with models varying PSA variations (logPSA, logPSAD) and core volume (absolute number of positive cores, percentage positivity of systematic cores, percentage positive of overall cores) were evaluated without significant difference in overall discriminatory ability. Percentage positivity of systematic cores was selected for consistency with the MSKCC nomogram, and logPSAD was used in models given known associations with clinically significant prostate cancer. Furthermore, PHI and MRI abutment were not significantly associated with EPE after multivariable adjustment and excluded from final models. AUCs for final models predicting EPE are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal Multivariable Model for Overall EPE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultivariable Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.00, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogPSAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63 (1.22, 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax PIRADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 or 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.10, 3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97 (1.04, 3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.21 (2.57, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.11, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00 (1.32, 3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82 (0.98, 3.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.70 (2.10, 11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Core Positivity non-ROI cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSide-Specific EPE\u003c/h2\u003e \u003cp\u003eFor dominant side EPE, \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e shows the univariable and multivariable model with all variables and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the final multivariable model. Non-dominant side biopsy GG or PIRADS lesions were not significantly associated with dominant side EPE. Final variables in multivariable model for dominant side EPE were age, logPSAD, dominant side PIRADS, dominant side GG, and % systematic core positivity with AUC 0.77 (95% CI 0.73\u0026ndash;0.80). Dominant side PIRADS 5 and dominant side GG 3\u0026ndash;5 were significant associated with dominant side EPE.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Model for Dominant Side EPE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultivariable Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.00, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogPSAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 (1.09, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant side PIRADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 or 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.13, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.82 (0.92, 3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.87 (2.35, 10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant side GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.13, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01 (1.32, 3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01 (1.09, 3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.24 (1.59, 6.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Core Positivity non-ROI cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor non-dominant EPE, \u003cb\u003eSupplemental Table\u0026nbsp;3\u003c/b\u003e shows the initial univariable and multivariable analysis and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the final multivariable model. There were no dominant side factors such as GG and PIRADS that were significantly associated with non-dominant EPE. Age, logPSAD, non-dominant side PIRADS, non-dominant side GG, and % systematic core positivity were included in the model with AUC 0.79 (95% CI 0.74\u0026ndash;0.84). While non-dominant GG reached significance in the original multivariable analysis, it did not reach significance in the final multivariable risk calculator model, perhaps due to limited number of patients with non-dominant side GG 3\u0026ndash;5 (n\u0026thinsp;=\u0026thinsp;82, p\u0026thinsp;=\u0026thinsp;0.06).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Model for Non-Dominant Side EPE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultivariable Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.00, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elogPSAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71 (1.24, 2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Dominant side PIRADS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 or 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.40, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94 (0.89, 4.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.84 (2.89, 22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Dominant side GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.44, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.31, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (0.75, 3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46 (0.91, 13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.39 (0.84, 26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Core Positivity non-ROI cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInternal validation with leave one out cross validation showed stable estimates and is summarized in \u003cb\u003eSupplemental Table\u0026nbsp;4\u003c/b\u003e. Calibration curves were completed with leave one out models, and showed concordance between predictive and observed probability, with low Brier score and non-significant Spiegelhalter Z test P values (\u003cb\u003eSupplemental Table\u0026nbsp;5, Supplemental Fig.\u0026nbsp;2\u003c/b\u003e). Online versions of our risk calculators are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rossnm1.shinyapps.io/PredictionOfEPELaterality/\u003c/span\u003e\u003cspan address=\"https://rossnm1.shinyapps.io/PredictionOfEPELaterality/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePre-operative planning with prostate MRI seeks to maximize oncologic control with negative margins while balancing nerve sparing for erectile function preservation. Definitive EPE on MRI is currently graded as a PIRADS 5 lesion, but many PIRADS 5 lesions do not demonstrate definitive EPE. Additionally, EPE is often present on surgical pathology for patients without PIRADS 5 lesions. In our study, we generate a series of side specific risk calculators which may assist with nerve sparing decisions at time of RP or inform radiation dose delivery planning. Importantly, the presence of MRI capsular abutment did not add significant predictive value on multivariable analysis.\u003c/p\u003e \u003cp\u003eTraditionally, the Partin Tables(6) and MSKCC pre-prostatectomy nomograms(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) are two commonly utilized tools for prediction of EPE at RP. However, these two tools, which were produced in the pre-MRI era, do not provide information regarding laterality of EPE or visible lesions. Initial nomograms incorporating prostate MRI data into radical prostatectomy for prediction of EPE and SVI at RP do not show significant improvement compared to the traditional MSKCC nomograms.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Although we did not directly compare overall EPE in our development cohort with performance of the MSKCC model, the AUC values were similar.\u003c/p\u003e \u003cp\u003eSide-specific nomograms have been developed incorporating clinical variables, but treat the prostate gland as two independent sides without assessing for the influence of contralateral lesions.(\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Martini et al incorporate age, PSA, biopsy gleason grade, % core involvement, and presence of ECE on MRI, while Soeterik et al incorporate PSAD, clinical T stage at DRE, MRI, biopsy GG, presence of MRI lesion and MRI EPE, and % core positivity. Independent, significant factors in our models include age, logPSAD, side specific Gleason Grade, side specific PIRADS, and % systematic core involvement. Our final MRI model incorporates side-specific PIRADS, which is an important variable not included in other models and is used in clinical practice for risk stratification and surgical planning. Importantly, with our risk stratification, we did not find that side-specific EPE was influenced by side-specific GG or PIRADS on the contralateral side. Side-specific GG3-5 and side-specific PIRADS 5 lesions were associated with side-specific EPE, which should encourage providers to consider wider surgical excision on the corresponding side with these characteristics. Similarly, nerve preservation can be aggressively performed unilaterally even in high risk men if adverse indicators are not present contralaterally.\u003c/p\u003e \u003cp\u003eOur models did not find that MRI lesion abutment added to side-specific EPE discrimination. There is no consensus on utility of MRI abutment, and lack of a standardized system for local staging has led to interobserver variability and heterogeneity in reporting. To further quantify degree of abutment, length of capsular contact or interface has been previously considered.(\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) A capsular contact of 12.5 mm yielded a sensitivity of 87% and specificity of 62% for presence of EPE, while another study found that a tumor contact length of 14.6 mm provides a sensitivity of 73.3% and specificity of 55.8% for GG 1\u0026ndash;3 prostate cancer.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Conversely, a length of capsular contact of \u0026lt;\u0026thinsp;5 mm had low probability of pathologic EPE. Our current investigation did not find that MRI abutment was a predictive variable, but adopting a standardized system for assessment into PIRADS could improve predictive value in the future. Additionally, imaging analysis incorporating artificial intelligence-driven algorithms may hold promise to improve upon radiologist interpretation.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe limitations of our study were related to design as a retrospective, single healthcare system study. Independent validation is required and is the next immediate step for further evaluation of our models. Second, there was lack of central review for prostate MRIs and reflects real world practice. Prostate MRI interpretation has significant interrater variability, and results from center to center may vary based on reader volume and experience.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Third, MRI lesion size was not evaluated in favor of PIRADS, and lesion location was not categorized beyond laterality. Finally, in the long term as tools become more widely available, radiomics and artificial intelligence algorithms may be used to further improve our model for local staging with implementation into the electronic medical record.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHere we offer a series of novel risk calculators for side-specific EPE to assist with surgical planning for RP with regards to nerve sparing and radiation dose delivery planning. While external validation is forthcoming, these calculators are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rossnm1.shinyapps.io/PredictionOfEPELaterality/\u003c/span\u003e\u003cspan address=\"https://rossnm1.shinyapps.io/PredictionOfEPELaterality/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDeclaration of Interest:\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAER is engaged in consulting with American Society of Clinical Oncology (ASCO), Astellas, Bayer HealthCare Pharmaceuticals, Blue Earth Diagnostics, Janssen Biotech, Lantheus Medical Imaging, Myovant Sciences, NCCN, Pfizer, Tempus Health, and Veracyte.\u003c/li\u003e\n \u003cli\u003eEMS is engaged in consulting with Astellas, Lantheus Medical Imaging, Pfizer.\u003c/li\u003e\n \u003cli\u003eThe other contributing authors have no conflicts of interest to declare.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthics Approval and Consent to Participate\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed under IRB STU00214996 issued by Northwestern University. The study was performed in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no source of funding for this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData Availability Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available for bona fide researchers who request it from the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAuthorship Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: EL, EMS, HDP, AER\u003c/p\u003e\n\u003cp\u003eMethodology: EL, SK\u003c/p\u003e\n\u003cp\u003eSoftware/Validation: SK\u003c/p\u003e\n\u003cp\u003eFormal analysis: EL, SK\u003c/p\u003e\n\u003cp\u003eInvestigation: EL, JAA, ZS\u003c/p\u003e\n\u003cp\u003eResources: CN, SK\u003c/p\u003e\n\u003cp\u003eData Curation: EL, JA, MRS,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; Original Draft: EL, JAA\u003c/p\u003e\n\u003cp\u003eWriting- Review \u0026amp; Editing: EL, JAA, MRS, ZS, CN, EMS, AER, HDP\u003c/p\u003e\n\u003cp\u003eVisualization: EL, SK\u003c/p\u003e\n\u003cp\u003eSupervision: EMS, HDP, AER\u003c/p\u003e\n\u003cp\u003eProject administration: HDP, AER\u003c/p\u003e\n\u003cp\u003eFunding acquisition: HDP, AER\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet. 2017;389(10071):815\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH, et al. MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis. N Engl J Med. 2018;378(19):1767\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePadhani AR, Petralia G, Sanguedolce F. Magnetic Resonance Imaging Before Prostate Biopsy: Time to Talk. Eur Urol. 2016;69(1):1\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddiqui MR, Ansbro B, Shah PV, Aguiar JA, Li EV, Rich JM, et al. Real-world use of MRI for risk stratification prior to prostate biopsy. Prostate Cancer Prostatic Dis. 2023;26(2):353\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeong BC, Chalfin HJ, Lee SB, Feng Z, Epstein JI, Trock BJ, et al. The relationship between the extent of extraprostatic extension and survival following radical prostatectomy. Eur Urol. 2015;67(2):342\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTosoian JJ, Chappidi M, Feng Z, Humphreys EB, Han M, Pavlovich CP, et al. Prediction of pathological stage based on clinical stage, serum prostate-specific antigen, and biopsy Gleason score: Partin Tables in the contemporary era. BJU Int. 2017;119(5):676\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKattan MW, Eastham JA, Stapleton AM, Wheeler TM, Scardino PT. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst. 1998;90(10):766\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooperberg MR, Freedland SJ, Pasta DJ, Elkin EP, Presti JC, Jr., Amling CL, et al. Multiinstitutional validation of the UCSF cancer of the prostate risk assessment for prediction of recurrence after radical prostatectomy. Cancer. 2006;107(10):2384\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Rooij M, Hamoen EH, Witjes JA, Barentsz JO, Rovers MM. Accuracy of Magnetic Resonance Imaging for Local Staging of Prostate Cancer: A Diagnostic Meta-analysis. Eur Urol. 2016;70(2):233\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoeterik TFW, van Melick HHE, Dijksman LM, K\u0026uuml;sters-Vandevelde H, Stomps S, Schoots IG, et al. Development and External Validation of a Novel Nomogram to Predict Side-specific Extraprostatic Extension in Patients with Prostate Cancer Undergoing Radical Prostatectomy. Eur Urol Oncol. 2022;5(3):328\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartini A, Gupta A, Lewis SC. Development and internal validation of a side-specific, multparametric magnetic resonance imaging-based nomogram for the prediction of extracapsular extension of prostate cancer. BJU Int. 2018;122(6):1025\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeerman H, Heymans MW, van der Poel HG. External Validation of a Prediction Model for Side-specific Extraprostatic Extension of Prostate Cancer at Robot-assisted Radical Prostatectomy. Eur Urol Open Sci. 2022;37:50\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodtman RA, Kollberg KS, Pihl CG, M\u0026aring;nsson M, Hugosson J. The Association Between Age, Prostate Cancer Risk, and Higher Gleason Score in a Long-term Screening Program: Results from the G\u0026ouml;teborg-1 Prostate Cancer Screening Trial. Eur Urol. 2022;82(3):311\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement. Eur Urol. 2015;67(6):1142\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiamand R, Ploussard G, Roumigui\u0026eacute; M, Oderda M, Benamran D, Fiard G, et al. External Validation of a Multiparametric Magnetic Resonance Imaging-based Nomogram for the Prediction of Extracapsular Extension and Seminal Vesicle Invasion in Prostate Cancer Patients Undergoing Radical Prostatectomy. Eur Urol. 2021;79(2):180\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephenson AJ, Scardino PT, Eastham JA, Bianco FJ, Jr., Dotan ZA, Fearn PA, et al. Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Natl Cancer Inst. 2006;98(10):715\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreifeld Y, Diaz de Leon A, Xi Y. Diagnostic Performance of Prospectively Assigned Likert Scale Scores to Determine Extraprostatic Extension and Seminal Vesicle Invasion with Multiparametric MRI of the Prostate. AJR Am J Roentgenol. 2019;212(3):576\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReis\u0026aelig;ter LA, Halvorsen OJ, Beisland C. Assessing Extraprostatic Extension with Multiparametric MRI of the Prostate: Mehralivand Extraprostatic Extension Grade or Extraprostatic Extension Likert Scale? Radiol Imaging Cancer. 2020;2(1):e190071.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehralivand S, Shih JH, Harmon S. A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI. Radiology. 2019;290(3):709\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValentin B, Schimmoller L, Ullrich T. Magnetic resonance imaging improves the prediction of tumor stage in localized prostate cancer. Abdom Radiol. 2021;46(6):2751\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakir B, Onay A, Vural M. Can Extraprostatic Extension Be Predicted by Tumor-Capsule Contact Length in Prostate Cancer? Relationship with International Society of Urological Pathology Grade Groups. AJR Am J Roentgenol. 2020;214(7):588\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan den Berg I, Soeterik TFW, van der Hoeven E, Claassen B, Brink WM, Baas DJH, et al. The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer. Cancers (Basel). 2023;15(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWestphalen AC, McCulloch CE, Anaokar JM, Arora S, Barashi NS, Barentsz JO, et al. Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology. 2020;296(1):76\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoroianu SL, Bhattacharya I, Seetharaman A. Computation Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI using Deep Learning. Cancers (Basel). 2022;14(12):2821.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"prostate-cancer-and-prostatic-diseases","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"pcan","sideBox":"Learn more about [Prostate Cancer and Prostatic Diseases](http://www.nature.com/pcan/)","snPcode":"41391","submissionUrl":"https://mts-pcan.nature.com/cgi-bin/main.plex","title":"Prostate Cancer and Prostatic Diseases","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"prostate MRI, risk calculator, surgical planning, extraprostatic extension, nerve sparing","lastPublishedDoi":"10.21203/rs.3.rs-4459729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4459729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTraditional nomograms can inform the presence of extraprostatic extension (EPE) but not laterality, which remains important for surgical planning, and have not fully incorporated multiparametric MRI data. We evaluated predictors of side-specific EPE on surgical pathology including MRI characteristics and developed side-specific EPE risk calculators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective cohort of patients evaluated with mpMRI prior to radical prostatectomy (RP) in our eleven hospital healthcare system from July 2018-November 2022. \u0026nbsp;The dominant side was defined pre-operatively using a tiered system based on laterality of highest biopsy Gleason Grade Group (GG), highest PIRADS lesion, number of lesions, and cancer volume. Univariable and multivariable logistic regression were performed for overall EPE, dominant side EPE, and non-dominant side EPE.\u0026nbsp;Internal validation with leave one out and calibration curves were completed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEPE was identified in 53% (317/601) of patients at RP. Side-specific factors (PIRADS, GG, abutment) were only associated with EPE on their respective side. Final variables in the model associated with EPE on the dominant and non-dominant sides included age, log PSA density (PSAD), side-specific PIRADS 5, side-specific GG3-5, and percentage positivity of systematic cores. AUCs for dominant and non-dominant side EPE were 0.77 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.84), respectively. MRI-identified abutment and prostate health index (PHI) did not improve model discrimination. Risk calculators available online at https://rossnm1.shinyapps.io/PredictionOfEPELaterality/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePSA, side-specific PIRADS, side-specific GG, and percentage positivity of systematic cores were associated with side-specific EPE at RP and incorporated into a risk calculator to assist in surgical planning and nerve-sparing decisions at time of RP.\u003c/p\u003e","manuscriptTitle":"Prostate MRI and Clinicopathologic Risk Calculator to Predict Laterality of Extraprostatic Extension at Radical Prostatectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 18:42:09","doi":"10.21203/rs.3.rs-4459729/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-06-18T14:30:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-06-16T17:42:50+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-05-28T15:00:16+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-05-28T14:49:31+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-05-27T16:53:28+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-05-27T14:59:34+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-05-26T13:17:23+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-05-24T12:18:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-24T12:14:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-23T10:02:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Prostate Cancer and Prostatic Diseases","date":"2024-05-22T09:15:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"prostate-cancer-and-prostatic-diseases","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"pcan","sideBox":"Learn more about [Prostate Cancer and Prostatic Diseases](http://www.nature.com/pcan/)","snPcode":"41391","submissionUrl":"https://mts-pcan.nature.com/cgi-bin/main.plex","title":"Prostate Cancer and Prostatic Diseases","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f20aca96-d8d8-4f27-a42b-9707551c8e5f","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32360985,"name":"Health sciences/Diseases/Cancer/Cancer therapy"},{"id":32360986,"name":"Health sciences/Medical research/Outcomes research"},{"id":32360987,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-01-07T08:08:26+00:00","versionOfRecord":{"articleIdentity":"rs-4459729","link":"https://doi.org/10.1038/s41391-024-00928-7","journal":{"identity":"prostate-cancer-and-prostatic-diseases","isVorOnly":false,"title":"Prostate Cancer and Prostatic Diseases"},"publishedOn":"2025-01-07 05:00:00","publishedOnDateReadable":"January 7th, 2025"},"versionCreatedAt":"2024-06-11 18:42:09","video":"","vorDoi":"10.1038/s41391-024-00928-7","vorDoiUrl":"https://doi.org/10.1038/s41391-024-00928-7","workflowStages":[]},"version":"v1","identity":"rs-4459729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4459729","identity":"rs-4459729","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}