Follow-up on Patients with Initial Negative mpMRI Target and Systematic Biopsy for PI-RADS ≥3 Lesions – An EAU-YAU Study Enhancing Prostate Cancer Detection.

Follow-up on Patients with Initial Negative mpMRI Target and Systematic Biopsy for PI-RADS ≥3 Lesions – An EAU-YAU Study Enhancing Prostate Cancer Detection. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Follow-up on Patients with Initial Negative mpMRI Target and Systematic Biopsy for PI-RADS ≥3 Lesions – An EAU-YAU Study Enhancing Prostate Cancer Detection. Fabio Zattoni, Giorgio Gandaglia, Roderick van den Bergh, Giancarlo Marra, and 27 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4263695/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2024 Read the published version in Prostate Cancer and Prostatic Diseases → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose To investigate the detection and predictors of prostate cancer (PCA) and clinically significant prostate cancer (csPCA) in patients with positive multiparametric MRI (mpMRI) followed by a negative MRI – guided target biopsy (TB) and systematic biopsy (SB). Materials and Methods This retrospective multicenter study included 694 patients from 10 tertiary referral centers with an initial positive mpMRI (PI-RADS ≥ 3) and negative results on both MRI-TB and SB. Patients were classified into three groups based on follow-up: Group 1 (prostate re-biopsy without new mpMRI), Group 2 (standardized second prostate mpMRI and subsequent re-biopsy), and Group 3 (follow-up with mpMRIs and biopsy based on clinical and radiological triggers). The primary outcomes were the detection of any PCA and csPCA during follow up. Study groups were compared according to their probability of PCA and csPCA assessed with the ERSPC-MRI risk calculator. Statistical analysis included Kaplan – Meier analysis, Cox regression, and multivariable analysis for the detection of (cs)PCa. Results The overall detection of PCA and csPCA was 26.8% and 19.3%, respectively, with varying rates in different PI-RADS groups. Group 3 had the highest 2 – year and 5 – year PCA – free survival (94% and 84%) and csPCA – free survival (96% and 86%). Multivariable analysis revealed a significantly higher risk of PCA and csPCA in Group 1 and 2 compared to Group 3. Clinical and radiological predictors for PCA and csPCA included higher age, prostate volume, PI-RADS score, the presence of atypical small acinar proliferation (ASAP), and a smaller number of TB and SB performed during the initial biopsy. Study limitations, include the retrospective design and reliance on clinical and radiological triggers for follow – up decisions. Conclusions Patients with positive mpMRI but negative TB and SB results exhibit varying rates of PCA and csPCA depending on the follow up scheme. Tailored follow-up strategies are essential for optimal management in this clinical scenario. Health sciences/Diseases/Cancer/Urological cancer/Prostate cancer Health sciences/Diseases Health sciences/Medical research/Outcomes research Prostate Cancer Diagnosis prostate MRI negative biopsy Target Prostate Biopsy targeted biopsy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The detection of prostate cancer (PCA) has significantly changed recently due to the introduction of multiparametric MRI (mpMRI). This advanced imaging technique has revolutionized the assessment of PCA risk before prostate biopsies across various clinical scenarios. MRI-guided target biopsy (TB) was associated with significantly higher diagnostic accuracy for clinically significant (cs) PCA (csPCA), reducing at the same time the diagnosis of clinically insignificant (CI) PCA [ 1 ] compared to systematic biopsy alone (SB). Further increases in diagnostic accuracy can be achieved by combining TB with SB, although this may come at the cost of increased infection and pain morbidity [ 2 ]. Moreover, in patients with negative MRI, its predictive value ranges from 91 to 96% for the detection of csPCA according to the different definitions of csPCA, allowing the possibility of avoiding biopsy in many patients with negative MRI [ 3 ]. One of the current unclear clinical scenarios is represented by the patients with positive mpMRI but negative TB and SB. This could either be explained by false positive MRI review or missed positive lesion at prostate biopsy. A recent mini-systematic review identified nine studies [ 4 ][ 5 ][ 6 ][ 7 ][ 8 ][ 9 ][ 10 ], including overall less than 500 patients in this clinical setting. On the whole, the systematic review demonstrated a highly variable detection rate of csPCA, ranging from 7.5–80% in PI-RADS 3 lesions, from 17–75% in PI-RADS 4 lesions, and over 80% in PI-RADS 5 lesions [ 11 ]. Based on those limited and heterogeneous data, the EAU guidelines suggested performing a rereview of the MRI after negative TBx, preferably from a high-volume expert radiologist in a tertiary referral center and, subsequently, clinical follow-up with PSA and repeated mpMRI at 6–12 mo for PI-RADS/Likert 3 lesions; clinical follow-up with PSA, repeated mpMRI, and repeated biopsy at 3–6 mo for PI-RADS/Likert 4 lesions; direct repeated biopsy for PI-RADS/Likert 5 lesions [ 11 ]. In the face of such a paucity of data supporting these recommendations, we elected to investigate further the detection of any PCA and csPCA and their clinical and radiological predictors in patients with positive mpMRI and negative MRI-TB and SB in a retrospective, multi-center series of patients with negative TB and SB following initial positive mpMRI, including patients receiving repeated biopsy only, repeated mpMRI and repeated biopsy, only clinical follow-up. Materials & methods The present study obtained Internal Review Board approval for retrospective data collection in accordance with the policies of each participating institution. A total of 694 patients from 10 tertiary referral centers were included. Inclusion criteria were patients with a first positive mpMRI (PI-RADS ≥ 3) along with negative results on both MRI TB and SB (initial biopsy). During the first 18 months, we classified patient management according to three different types of follow-ups, as decided by treating urologist: Group 1: Prostate re-biopsy without a new mpMRI. Group 2: Standardized second prostate mpMRI and subsequent re-biopsy, including either MRI-TBx and/or SB. Detailed information regarding this population is described elsewhere [ 12 ]. Group 3: Follow-up with mpMRIs and prostate biopsy based on clinical and radiological triggers. Depending on each institution's protocols, triggers included PSA increase, DRE changes and radiological progression observed in MRIs performed after the initial biopsy. The exclusion criteria included patients who underwent systematic biopsy before the initial MRI-TBx, as well as individuals who were previously diagnosed with PCa. Prostate Biopsy Techniques A mpMRI was performed before the first biopsy, following each institution's protocol. All centers utilized the PI-RADS v2 scoring system to assess MRI findings [ 13 ]. Expert genitourinary radiologists reviewed all MRIs in accordance with the ESUR/ESUI consensus for image acquisition, interpretation, and radiologists' training [ 14 ]. Transrectal or transperineal targeted biopsies were performed by experienced urologists using their preferred biopsy approach. TBs were performed using dedicated biopsy fusion software or cognitive methods, according to the expertise of each center. Transperineal TB was performed with a brachytherapy grid or freehand technique under general or local anesthesia. The number of SB after TB were performed according to each institution protocol. Assessing the probability of any PCA and csPCA at the first negative biopsy The probability of any PCA and csPCA has been calculated for each patient with the ERSPC-MRI risk groups (RC5, and RC6) at the first negative biopsy [ 15 ]. To ensure the optimal risk prediction in our cohort, the probability has been recalibrated according to the present cohort PCA and csPCA prevalence. Statistical Analysis Categorical variables were presented as frequencies, while continuous variables were reported as mean ± standard deviation (SD) for normally distributed variables and as median and interquartile range (IQR) for non-normally distributed variables. Differences in baseline characteristics between categorical and continuous variables were assessed using either chi-square, ANOVA or Mann-Whitney U test, as appropriate. Kruskal – Wallis test was used to assess any significant differences on a continuous dependent variable by the three groups. PCA and csPCA detection-free survival were evaluated using Kaplan-Meier analysis. The multiple log – rank test was used for comparison of the survival curves. Univariable (UVA) and multivariable (MVA) Cox regression analyses were performed to evaluate predictors for PCA and csPCA at the moment of repeat biopsy. CsPCA was defined as any ISUP ≥ 2 cancer. Covariates included in the model were selected based on univariable results with p – values ≤ 0.1. Variables with suspicious interaction terms (PSA and Prostate volume with PSAD as well as PI-RADS with cT stage at MRI) were adjusted accordingly. The analyses were performed in the whole population and in the subgroup with PI-RADS ≥ 4 lesions. A significance level of p < 0.05 was used for all tests. Statistical analyses were performed using SPSS version 28 (IBM, Armonk, NY, USA). Results Table 1 describes the characteristics of the initial biopsy and follow-up for the 694 patients in the whole cohort and stratified by study group. Each center contributed with all three follow-up strategies. Figure 1 illustrates the evolution of the diagnostic pathway from the first biopsy to the available follow – up of this selected patient population in a Sankey diagram. Table 1 characteristics of the 694 patients with positive mpMRI and initial negative biopsy. Full cohort and stratification by study group Whole cohort (n = 694) GRUP 1 (n = 58) GRUP 2 (n = 290) GRUP 3 (n = 346) P value Median age (IQR) 66.2 (61–71) 68 (61–72) 65 (61–71) 66 (61–72) 0.3 Therapy with 5 – ARI 150 (22%) 6 (15%) 103 (50%) 41 (15%) < 0.01 PSA at initial biopsy (ng/ml) 6.5 (4.7–8.9) 6.4 (4.9–9.7) 6.5 (4.7–9.0) 6.4 (4.8 − 8.9) 0.7 Median prostate volume (IQR) 53 (40–71.2) 48 (35–68) 53 (40–67) 55 (42–75) 0.1 PSAD (ng/ml/cc 3 ) 0.12 (0.08–0.18) 0.16 (0.09 − 0.25) + 0.12 (0.09–0.19) 0.12 (0.08–0.17) + < 0.01 Positive rectal examination 193 (28%) 16 (28%) 107 (37%) 70 (20%) < 0.01 Clinical stage \(\ge\) T3 at mpMRI 99 (14%) 7 (12%) 61 (21%) 31 (9%) < 0.01 Maximum diameter of the lesion (mm) 9 (7–12) 11.5 (8–16) 9 (6–12) 9 (7–11) 3 307 (44%) 387 (56%) 36 (62%) 22 (38%) 137 (47%) 153 (53%) 134 (39%) 212 (61%) < 0.01 Software – based registration for initial B 529 (76%) 26 (45%) 196 (68%) 307 (89%) < 0.01 Transperineal biopsy at initial biopsy 387 (56%) 22 (38%) 153 (53%) 212 (61%) 12 84 (12%) 221 (32%) 186 (27%) 10 (17%) 29 (50%) 19 (33%) 40 (14%) 170 (59%) 80 (28%) 34 (10%) 225 (65%) 87 (25%) 0.1 > 3 cores TB at initial biopsy 332 (48%) 20 (35%) 150 (52%) 162 (47%) 0.05 Presence of ASAP at initial biopsy 106 (15%) 5 (9%) 59 (20%) 42 (12%) < 0.01 ERSPC-MRI risk calculator estimated risk of PCA after the first negative biopsy. Median Probability any PCA (IQR) Median Probability of csPCA (IQR) 0.20 (0.1–0.3) 0.09 (0.03–0.18) 0.23 (0.11–0.36) 0.09 (0.04–0.19) 0.20 (0.11–0.35) 0.08 (0.04–0.19) 0.20 (0.10–0.30) 0.09 (0.03–0.16) 0.2 + 0.4 + Number of MRI scans during follow – up 1 (1–1) 0* + (0–1) 1 (1–2) + 1 (1–2) * 2 253 (36.4%) 407 (58.6%) 28 (4.0%) 6 (1%) 0 55 (95%) 3 (5%) 0 ( %) 0 271 (93.4%) 14 (4.8%) 5 (1.8%) 253 (73.1%) 81 (23.4%) 11 (3.1%) 1 (0.4%) < 0.01 Number of prostate biopsy after the initial negative biopsy 1 (0–1) 1 (1–2) 1 (1–2) 1 (0–1) 3 cores TB at repeated biopsy 202/441 (45%) 21/58 (36%) 141/290 (48%) 40/93 (43%) < 0.01 Total diagnosis of PCA – Any cancer – Clinically significant cancer 174 (27%) 134 (19%) 29 (50%) 21 (36%) 110 (38%) 86 (30%) 35 (10%) 27 (8%) < 0.01 Total diagnosis of PCA per "repeat biopsy analysis” Total diagnosis of PCA – Any cancer – Clinically significant cancer 174/441 (39%) 134/441 (30%) 29 (50%) 21 (36%) 110 (38%) 86 (30%) 35/93 (37%) 27/93 (29%) 0.06 Median time from fist MRI to PCA diagnosis or last follow up (mo) 28 (13–51) 24 (13.3–40.2) 28.4 (16.7–44.7) 20.0 (9–51) < 0.01 Group 1: Prostate re – biopsy without a new mpMRI; group 2: repeated prostate mpMRI and subsequent re – biopsy, including either MRI – TBx and/or SB; group 3: follow – up with mpMRIs and prostate biopsy based on clinical and radiological triggers *Missing data PSAD: PSA density; SB: systemic biopsy; TB: Targeted biopsy; ASAP: atypical small acinar proliferation; PCA: prostate cancer Overall, we identified 174 (27%) any grade PCA and 134 (19%) csPCA at a median follow-up duration of 28 mo (13–51). The median time from the first MRI to PCA diagnosis or last follow-up was 24 (13–40), 28 mo (17–45), and 20 mo (9–51) for groups 1 to 3, respectively (p < 0.01). The detection of any PCA and csPCA was 19% and 15% in initial PI-RADS 3 lesions; 27% and 21% in initial PI-RADS 4 lesions; and 60% and 37% in initial PI-RADS 5 lesions. The three groups differ in most of the clinical and radiological characteristics. However, no differences have been observed among the three groups in the detection of any PCA and csPCA after the first negative biopsy as estimated by the ERSPC-MRI PCA risk calculator. From the original biopsy, the median number of mpMRI was 0 (0 − 1) for group 1 and 1 (1 − 2) for groups 2 and 3 (p value < 0.01). The median number of prostate re-biopsy was 1 (1–2) in groups 1 and 2 and 1 (0–1) in group 3 (p value < 0.01). Overall, 75% of the patients had software-based registration for repeated TB, and 60% of the patients had a transperineal biopsy during repeated biopsies. In group 1, the re-biopsy was performed at a median of 13 (10–14) months after the original biopsy and detected PCA and csPCA in 29 (50%) and 21 patients (36%). Among the patients with negative repeated biopsy, 2 had further mpMRI, and 2 patients had further prostatic biopsy. At a follow-up of 24 (IQR: 13.3. 40.2), no further cancer was diagnosed. Data for group 2 were extensively reported previously [ 12 ]. The interval from the initial to repeated mpMRI and from the initial to repeated biopsy were 16 mo (IQR 12–20) and 18 mo (IQR 12–21), respectively. The PI-RADS score at the second MRI was classified as < 3 in 25 patients (9%), 3 in 91 patients (31%), 4 in 137 patients (47%), and 5 in 37 patients (13%). One hundred and eight patients (37%) were diagnosed with PCA and 74 (25.5%) with csPCA at re-biopsy. SB and MRI-TBx identified PCA in 28% and 31% of the cases (including 18% and 20% of csPCA). The median follow-up from second negative biopsy was 20 months (IQR: 5.7–34.7). Further MRI and subsequent biopsy were performed in 19 patients, of whom only 2 patients were diagnosed with PCA. In group 3, additional mpMRI during follow up was performed in 198 (69%) patients, identifying graded as PI-RADS 3 in 24%, 68%, and 8% of patients, respectively. On the whole, 93 patients (27%) received at least one repeated biopsy during follow-up. PCA and csPCA were detected in 35 (12%) and 27 patients (9.3%), however in a “repeat biopsy analysis” PCA and csPCA were detected in a percentage similar to group 1 and 2 (p = 0.06). Predictors of any grade and clinically significant PCA Figure 2 reports Kaplan-Meier curves for PCA and csPCA free survival in the whole cohort and stratified by study group and PI-RADS score. Overall, the 2- and 5-year PCA free survival estimates were 99% and 65% respectively; the 2- and 5-year csPCA free survival estimates were 99% and 71%, respectively. When stratified by study group, PCA-free and csPCA-free survival estimates were similar in groups 1 and 2 (log rank p value 0.4 for PCA and 0.7 for csPCA) but significantly higher in group 3 (log rank p value < 0.01) (Fig. 3 ). In particular the 2-year and 5 year Pca were respectively 63% and 50% for group 1, 72% and 51% for group 2 and 94% and 84% for group 3 (p < 0.01 between group 3 vs group 1 and group 2). The 2-year and 5-year csPca were respectively 77% and 60% for group 1, 94% and 61% for group 2 and 96% and 86% for group 3 (p < 0.01 between group 3 vs group 1 and group 2). When stratified by PI-RADS score, PCA-free and csPCA-free survival estimates were significantly the lowest in PI-RADS 5 lesions and the highest in PI-RADS 3 lesions (log rank p value < 0.01) (Fig. 4 ). Table 2 summarizes UVA and MVA Cox regression analyses assessing clinical and radiological predictors of PCA and csPCA after the initial negative biopsy. Table 2 Univariable and multivariable analysis assessing predictors of any prostate cancer and clinically-significant prostate cancer HR 95% CI of HR P value HR 95% CI of HR P value Any cancer Univariable analysis Multivariable analysis Study group < 0.01 < 0.001 Group 1 vs group 2 0.8 0.5–1.2 0.3 0.8 0.5–1.3 0.4 Group 1 vs group 3 0.2 0.1–0.4 < 0.01 0.2 0.1–0.4 < .001 Age (continuous) 1.0 1.0–1.05 0.03 1.0 1.0–1.1 < 0.01 PSA at initial biopsy (continuous) 0.9 0.9–1.03 0.8 – – – Prostate volume (ml) at initial biopsy (continuous) 0.9 1.0–1.0 0.01 1.0 0.9–1.0 < 0.01 PSAD (continuous) 1.0 0.5–3.4 0.6 – – – PI-RADS score < 0.01 < 0.01* 3 vs 4 1.7 1.2–2.3 < 0.01 1.8 1.3–2.4 < 0.01 3 vs 5 5.7 3.3–9.7 < 0.01 4.3 2.4–7.8 < 0.01 cT2 vs cT \(\ge\) 3 stage at MRI 1.6 1.0–2.4 0.03 * interaction terms Trasrectal vs trasperineal route 1.3 0.9–1.8 0.1 1.1 0.8–1.6 0.8 Cognitive vs software fusion biopsy 0.6 0.5–0.9 0.02 1.0 0.7–1.5 0.8 Number of TB cores (≤ 3 vs > = 3) 1.4 1.0–1.9 0.04 2.1 1.4–3.0 < 0.01 Number of SB cores < 0.01 12 SB cores 1.6 1.0–2.5 0.04 2.9 1.6–5.1 < 0.01 ASAP 2.4 1.7–3.4 < 0.01 2.1 1.4–3.0 < 0.01 Clinically significant prostate cancer Univariable analysis Multivariable analysis Study group < 0.01 < 0.01 Group 1 vs group 2 0.9 0.5–1.5 0.6 0.8 0.4 − 1.4 0.4 Group 1 vs group 3 0.3 0.1–0.5 < 0.01 0.2 0.1–0.4 < 0.01 Age (continuous) 1.0 1.0 − 1.1 0.06 1.0 1.0–1.1 < 0.01 PSA at initial biopsy (continuous) 0.9 0.9 – 1.0 0.4 – – – Prostate volume (ml) at initial biopsy (continuous) 0.9 0.9–0.9 < 0.01 0.9 0.9–0.9 < 0.01 PSAD (continuous) 0.8 0.2–3.0 0.8 – – – PI-RADS score < 0.01 < 0.01* 3 vs 4 1.6 1.1–2.3 0.01 1.1 1.2–2.5 < 0.01 3 vs 5 4.3 2.2–8.2 < 0.01 3.0 1.5–6.3 < 0.01 cT2 vs cT \(\ge\) 3 stage at MRI 1.9 1.2–3.0 = 3) 1.6 1.2–2.4 < 0.01 2.7 1.7–4.2 < 0.01 Number of SB cores < 0.01 12 SB cores 1.6 1.0 − 2.7 0.1 3.0 1.5–5.7 < 0.01 ASAP 2.6 1.8–4.0 < 0.01 2.0 1.3–3.0 < 0.01 HR: hazard ratio PSAD: PSA density; SB: Systematic biopsy; TB: Targeted biopsy; ASAP: atypical small acinar proliferation In the multivariable analysis, there was no statistically significant difference between group 1 and 2 (HR = 0.8, 95% CI = 0.5–1.3, p = 0.4), see Table 2 . However, the hazard on PCa and csPCa (HR = 0.2, 95% CI 0.1–0.4, p = p < 0.01) was lower for Group 3 vs Group 1. Furthermore, several clinical and radiological covariates (including patients age, prostate volume, PI-RADS score at the first MRI, presence of ASAP at the initial biopsy, number of TB and SB performed during the initial biopsy) were identified as predictors of PCA diagnosis during further follow-up. Table 3 summarizes UVA and MVA Cox regression analyses assessing clinical and radiological predictors of PCA and cs-PCA after the initial negative biopsy in cases with PI-RADS > 3 at the first MRI. Table 3 Univariable and multivariable analysis assessing predictors of any prostate cancer and clinically – significant prostate cancer during follow-up for PI-RADS > 3 lesions HR 95% CI of HR P value HR 95% CI of HR P value Any cancer Univariable analysis Multivariable analysis Study group < 0.01 < 0.01 Group 1 vs group 2 0.9 0.5–1.6 0.7 1.0 0.5–1.8 0.9 Group 1 vs group 3 0.3 0.1–0.5 < 0.01 0.2 0.1–0.5 < 0.01 Age (continuous) 1.0 1.0–1.1 0.04 1.0 1.0–1.1 < 0.01 PSA at initial biopsy (continuous) 1.0 1.0–1.1 0.4 – – – Prostate volume (ml) at initial biopsy (continuous) 0.9 0.9–0.9 0.01 *interaction term PSAD (continuous) 14.1 4.2–47.1 < 0.01 6.9 1.6–29.0 0.01 cT2 vs cT \(\ge\) 3 stage at MRI 1.9 1.2–3.0 = 3) 1.03 0.7–1.5 0.9 – – – Number of SB cores 0.2 – – – No SB vs ≤ 12 SB cores 0.8 0.5–1.4 0.5 – – – No SB vs > 12 SB cores 1.5 0.9–2.7 0.1 – – – ASAP 2.5 1.6–3.9 < 0.01 3.0 1.8–4.9 < 0.01 Clinically significant prostate cancer Univariable analysis Multivariable analysis Study group < 0.01 < 0.01 Group 1 vs group 2 0.9 0.5–1.9 0.8 1.0 0.5–2.2 1.0 Group 1 vs group 3 0.3 0.1–0.7 < 0.01 0.3 0.1–0.7 < 0.01 Age (continuous) 1.0 1–1.1 0.1 1.0 1.0–1.1 0.01 PSA at initial biopsy (continuous) 1.0 1.0–1.1 0.3 – – – Prostate volume (ml) at initial biopsy (continuous) 0.9 0.9–0.9 < 0.01 – – – PSAD (continuous) 9.2 2.0–42.2 < 0.01 1.8 0.3–9.2 0.5 cT2 vs cT \(\ge\) 3 stage at MRI 2.3 1.4–3.8 = 3) 1.2 0.8–2.0 0.4 – – – Number of SB cores 0.02 0.01 No SB vs ≤ 12 SB cores 0.7 0.4–1.4 0.3 0.5 0.2–0.9 0.044 No SB vs > 12 SB cores 1.6 0.9–2.9 0.1 1.1 0.5–2.4 0.9 ASAP 2.7 1.6–4.5 < 0.01 3.4 1.9–6.1 < 0.01 HR: hazard ratio PSAD: PSA density SB: Systematic biopsy TB: Targeted biopsy ASAP: atypical small acinar proliferation Once again, the study group variable, the number of systematic biopsies, and the presence of ASAP at the initial biopsy were identified as predictors of subsequent detection of PCA and csPCA. In addition, PSAD was an independent predictor of PCA (HR: 6.9, p < 0.01). Treatments Supplementary table 1 summarizes the treatment for the patients diagnosed with PCA and radical prostatectomy specimen data for the patients who received surgery. Among the selected treatment options, active surveillance/watchful waiting, surgery, radiation therapy, and focal therapy were chosen by 36 (21.4%), 109 (64.9%), 19 (11.3%), and 5 (2.4%) patients, respectively (supplementary table 1). Among the 109 radical prostatectomies, 53/109 had a pT stage \(\ge\) T3 and 63/109 had a ISUP score \(\ge\) 3. Discussion The presented study investigates the characteristics, follow-up outcomes, treatment decisions, and predictors of PCA and csPCA in a cohort of 694 patients with an initial positive mpMRI and negative prostate biopsies followed up according to different protocols. Overall, about 25% of the patients were diagnosed with PCA, more than 75% of which were identified as csPCA. This underscores the critical importance of establishing an accurate follow-up schedule for these patients, as the presence of csPCA cannot be definitively ruled out in a significant number of patients. Notably, about 16% of those patients with Pca underwent radical prostatectomy during the available follow-up. Among these patients, more than 50% exhibited a pT stage \(\ge\) T3 and about the same percentage had a ISUP score \(\ge\) 3. We conducted a comprehensive comparative analysis of three distinct follow-up schedules, revealing outcomes disparities. Patients who underwent a second biopsy without a new MRI exhibited similar results to those who received a new MRI before the second biopsy. Conversely, individuals who underwent a second biopsy triggered by specific clinical and radiological factors demonstrated a low detection of PCA and csPCA. This underscores the need for a risk-based strategy to mitigate overdiagnosis on one hand and, on the other, a more rigorous follow-up approach to prevent the omission of caPCA diagnosis. In particular, the potential limitations of the first TB emphasize the need for further investigations and follow-up in cases with inconsistent results between MRI images and MRI-guided biopsy findings. In addition to MRI, a possible role of PET/CT has been explored in recent studies [ 16 ][ 17 ]. The implementation of PSMA PET/CT with MRI results would help the selection of men who would benefit the most of screening or further biopsies [ 18 ]. The significance of second MRI within the initial 18 months after the first biopsy has been assessed in our prior publication [ 12 ]. In cases where lesions were initially described in the first MRI, downgrading occurred in only 19% of instances, while upgrading was observed in 39%, and stability was maintained in 42% of cases. The approach of combining SB and TB yielded elevated detection rates. For patients with lesions detected at the first MRI, the positivity rates were 16%, and for those with new lesions detected at the second MRI, the rate was 17.2% [ 12 ]. Conversely, the data of the present analysis suggested very similar diagnostic performance with group 1, i.e. the group of patients who repeated prostate biopsy without a new mpMRI. That was shown in the whole cohort and the subgroup of patients with initial PI-RADS lesions graded as 4 or 5. Unfortunately, even in a large multicenter series, the limited number of cases did not allow to further stratify the analyses. The literature on the topic is quite limited. In a recent mini-systematic review, Grivas et al. identified only nine studies, including less than 500 patients in this clinical setting. Overall, the systematic review demonstrated that the detection of csPCA was highly variable among the few available studies. Specifically, csPCA was detected in 7.5 to 80% of the patients with PI-RADS 3 lesions, 17 to 75% with PI-RADS 4 lesions, and over 80% with PI-RADS 5 lesions [ 11 ]. Different factors, including heterogeneity in mpMRI quality and accuracy, inaccuracy/errors in TB or SB, and discrepancies in the follow-up protocols can explain such large differences. In the present analysis, we identified csPCA in about 20% of the whole population and 36%, 30%, and 8% of groups 1 to 3, respectively. Notably, those patients who were followed up less strictly, with repeated biopsy triggered by PSA increase, DRE changes and radiological progression in repeated MRIs, had a significantly lower chance of being diagnosed with PCA and csPCA. Although several differences in the patients’ characteristics are evident among the different study groups, the detection of PCA and csPCA estimated by the ERSPC-MRI PCA risk calculator was similar. The data is constant in all our analyses and might allow us to hypothesize a certain level of underdectection of csPCA in this subgroup of patients. We identified several predictors of PCA and csPCA during the initial biopsy that warrant thorough evaluation when considering the decision for a second biopsy during patient consultation. Specifically, the presence of a high PI-RADS score, advanced age, smaller prostate volume, adequate prostate biopsy sampling with > 3 fusion cores and > 12 systematic biopsies, along with the presence of ASAP at the first biopsy, can serve as indicators for the likelihood of PCA and csPCA presence. ASAP is regarded as a precursor lesion, often indicating that the prostate tissue is undergoing changes that are more likely to progress to cancer over time. While not all instances of ASAP will inevitably develop into cancer, its presence prompts clinicians to engage in closer patient monitoring. However, there is an ongoing debate regarding the role of ASAP in the MRI era [ 19 ][ 20 ]. In our study, we observed a robust correlation between the presence of ASAP on the initial biopsy and the likelihood of PCA and csPCA occurrence. This supports the growing interest in investigating glandular – stromal alterations, along with acute or chronic inflammation and vascular changes. The present study is relevant for several reasons. It includes more patients than the only available systematic review on the topic. Consequently, the study provides more reliable data on the detection of PCA and, above all, csPCA in such an interesting patients population. Moreover, the series collected data from different tertiary referral centers, indicating a potentiality for good validity of the data in real life practice. Third, we provided data on PCA and csPCA predictors. Several limitations should be acknowledged. First of all, despite the present series being large, the number of patients with initial PI-RADS 4 or 5 lesions at MRI and subsequently negative TB/SB is limited. That might have made some of our analyses underpowered and limited our ability to perform more accurate subgroup analyses. Secondly, the study is retrospective, which introduces a significant risk of selection bias for follow-up, and patients were followed with different protocols. In other words, it is possible that patients with a higher risk of cancers, in the opinion of the attending urologists, might have been followed more strictly than those with a potentially lower risk. Although our statistical analyses tried to correct for the differences in covariate distributions, we cannot be sure that a selection bias might explain at least partially our findings. However, the finding that the detection of PCA and csPCA estimated by the ERSPC-MR PCA risk calculator was similar in the 3 groups suggest that such selection bias should not play a major role. The dataset includes both transperineal and transrectal prostate biopsies which may have different detection rate [ 21 ][ 22 ] and the results could have been different including one procedure only. The present study is preliminary, and randomized controlled trials evaluating various follow-up protocols in patients with positive MRI and negative TB/SB results would be valuable for standardizing the follow-up procedures for these patients. Finally, at the current follow- up, we are not able to understand the prognostic implications related to the diagnosis of such csPCA, which could arguably have lower volume and reduced clinical aggressively compared to those diagnosed in the first prostatic biopsy. Conclusions Prostate cancer diagnostics should be regarded as a longitudinal process, instead of a cross-sectional one-time approach. Overall, our findings contribute to a better understanding of patient characteristics, follow-up trajectories, treatment preferences, and predictive factors for PCA and csPCA, offering valuable insights for clinical decision-making and management strategies in these men with abnormal MRI but negative biopsy. Less aggressive re-imaging and re-biopsy may lead to more csPCa being missed. Improved knowledge on follow-up findings aids in primary biopsy decisions. Abbreviations PCA prostate cancer csPCA clinically significant prostate cancer mpMRI multiparametric MRI TB negative MRI – guided target biopsy ASAP atypical small acinar proliferation SD standard deviation UVA - MVA Univariable and multivariable Declarations COMPLIANCE WITH ETHICAL STANDARD Financial disclosure: None. Disclaimer: The authors declared that they have no conflict of interests. All included patients undergoing radical treatment provided written informed consent for surgery. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Institutional review board apply to each center due to observational and retrospective nature of the study. Author's Contribution Protocol/project development F Zattoni, G Gandaglia, G Novara, R C N van den Bergh, A Briganti Data collection or management LJ.Paulino Pereira, G Marra, M Valerio, J Olivier, I Puche – SanzI, M Maggi, R Campi, D Amparore, S De Cillis, Z Junlong, H Guo, G La Bombarda, A Fuschi . , A Veccia, F Ditonno, A Marquis, F Barletta, R Leni, S Serni, Data analysis F Zattoni, G Novara, F Dal Moro, Sebastiaan Remmers, Monique J. 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Radical Prostatectomy Without Prior Biopsy Following Multiparametric Magnetic Resonance Imaging and Prostate-specific Membrane Antigen Positron Emission Tomography. Eur Urol 2022;82:156–60. https://doi.org/10.1016/j.eururo.2021.11.019 . Tang W, Tang Y, Qi L, Zhang Y, Tang G, Gao X, et al. BPH-related False Positive of PSMA-PET in the Diagnosis of Prostate Cancer: the Achilles’ Heel of Biopsy-free Radical Prostatectomy? J Urol 2023:101097JU0000000000003680. https://doi.org/10.1097/JU.0000000000003680 . Gordetsky JB, Ullman D, Schultz L, Porter KK, Del Carmen Rodriguez Pena M, Calderone CE, et al. Histologic findings associated with false-positive multiparametric magnetic resonance imaging performed for prostate cancer detection. Human Pathology 2019;83:159–65. https://doi.org/10.1016/j.humpath.2018.08.021 . Hupe MC, Offermann A, Tharun L, Fürschke A, Frydrychowicz A, Garstka N, et al. Histomorphological analysis of false positive PI-RADS 4 and 5 lesions. Urologic Oncology: Seminars and Original Investigations 2020;38:636.e7- 636.e12 . https://doi.org/10.1016/j.urolonc.2020.01.017. Zattoni F, Marra G, Kasivisvanathan V, Grummet J, Nandurkar R, Ploussard G, et al. The Detection of Prostate Cancer with Magnetic Resonance Imaging-Targeted Prostate Biopsies is Superior with the Transperineal vs the Transrectal Approach. A European Association of Urology-Young Academic Urologists Prostate Cancer Working Group Multi-Institutional Study. Journal of Urology 2022;208:830–7. https://doi.org/10.1097/JU.0000000000002802 . Zattoni F, Marra G, Martini A, Kasivisvanathan V, Grummet J, Harkin T, et al. Is There an Impact of Transperineal Versus Transrectal Magnetic Resonance Imaging–targeted Biopsy on the Risk of Upgrading in Final Pathology in Prostate Cancer Patients Undergoing Radical Prostatectomy? An European Association of Urology-Young Academic Urologists Prostate Cancer Working Group Multi-institutional Study. European Urology Focus 2023;9:621–8. https://doi.org/10.1016/j.euf.2023.01.016 . Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Supplementarytable1.docx Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2024 Read the published version in Prostate Cancer and Prostatic Diseases → Version 1 posted Editorial decision: revise 07 Aug, 2024 Review # 1 received at journal 14 Jul, 2024 Review # 2 received at journal 29 Jun, 2024 Reviewer # 2 agreed at journal 29 Jun, 2024 Reviewer # 1 agreed at journal 28 Jun, 2024 Reviewers invited by journal 29 Apr, 2024 Editor assigned by journal 16 Apr, 2024 Submission checks completed at journal 16 Apr, 2024 First submitted to journal 14 Apr, 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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20:27:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":251852,"visible":true,"origin":"","legend":"\u003cp\u003eProstate cancer (2a) and clinically significant prostate cancer (2b) free survival estimates in the whole cohort\u003c/p\u003e","description":"","filename":"Fig2OK.png","url":"https://assets-eu.researchsquare.com/files/rs-4263695/v1/e701ff028902625fa394f0b2.png"},{"id":56043969,"identity":"f679afe3-3aac-498e-9c16-bafa91f5e1ca","added_by":"auto","created_at":"2024-05-07 20:27:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":321230,"visible":true,"origin":"","legend":"\u003cp\u003eProstate cancer (3a) and clinically significant prostate cancer (3b) free survival estimates stratified by study group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4263695/v1/eced7c2566823730c5d3ef82.png"},{"id":56043966,"identity":"bebbe7a1-7eff-42ed-9a71-2cf20f2e44c4","added_by":"auto","created_at":"2024-05-07 20:27:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eProstate cancer (4a) and clinically significant prostate cancer (4b) free survival estimates stratified by the initial PI-RADS score\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4263695/v1/bb453c9712e3ef18940cb880.png"},{"id":68335548,"identity":"417dd028-5902-42c8-b11f-149313e926a4","added_by":"auto","created_at":"2024-11-06 08:08:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1946282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4263695/v1/9573071e-e42d-4c00-9583-0abac85b57fd.pdf"},{"id":56044563,"identity":"b6ac3f45-4df5-4d61-820e-516af04179b1","added_by":"auto","created_at":"2024-05-07 20:35:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14540,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4263695/v1/97966084bf4b994a100bcb97.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"\u003cp\u003eFollow-up on Patients with Initial Negative mpMRI Target and Systematic Biopsy for PI-RADS ≥3 Lesions – An EAU-YAU Study Enhancing Prostate Cancer Detection.\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe detection of prostate cancer (PCA) has significantly changed recently due to the introduction of multiparametric MRI (mpMRI). This advanced imaging technique has revolutionized the assessment of PCA risk before prostate biopsies across various clinical scenarios. MRI-guided target biopsy (TB) was associated with significantly higher diagnostic accuracy for clinically significant (cs) PCA (csPCA), reducing at the same time the diagnosis of clinically insignificant (CI) PCA [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] compared to systematic biopsy alone (SB). Further increases in diagnostic accuracy can be achieved by combining TB with SB, although this may come at the cost of increased infection and pain morbidity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, in patients with negative MRI, its predictive value ranges from 91 to 96% for the detection of csPCA according to the different definitions of csPCA, allowing the possibility of avoiding biopsy in many patients with negative MRI [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the current unclear clinical scenarios is represented by the patients with positive mpMRI but negative TB and SB. This could either be explained by false positive MRI review or missed positive lesion at prostate biopsy. A recent mini-systematic review identified nine studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], including overall less than 500 patients in this clinical setting. On the whole, the systematic review demonstrated a highly variable detection rate of csPCA, ranging from 7.5\u0026ndash;80% in PI-RADS 3 lesions, from 17\u0026ndash;75% in PI-RADS 4 lesions, and over 80% in PI-RADS 5 lesions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Based on those limited and heterogeneous data, the EAU guidelines suggested performing a rereview of the MRI after negative TBx, preferably from a high-volume expert radiologist in a tertiary referral center and, subsequently, clinical follow-up with PSA and repeated mpMRI at 6\u0026ndash;12 mo for PI-RADS/Likert 3 lesions; clinical follow-up with PSA, repeated mpMRI, and repeated biopsy at 3\u0026ndash;6 mo for PI-RADS/Likert 4 lesions; direct repeated biopsy for PI-RADS/Likert 5 lesions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the face of such a paucity of data supporting these recommendations, we elected to investigate further the detection of any PCA and csPCA and their clinical and radiological predictors in patients with positive mpMRI and negative MRI-TB and SB in a retrospective, multi-center series of patients with negative TB and SB following initial positive mpMRI, including patients receiving repeated biopsy only, repeated mpMRI and repeated biopsy, only clinical follow-up.\u003c/p\u003e"},{"header":"Materials \u0026 methods","content":"\u003cp\u003e The present study obtained Internal Review Board approval for retrospective data collection in accordance with the policies of each participating institution. A total of 694 patients from 10 tertiary referral centers were included. Inclusion criteria were patients with a first positive mpMRI (PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;3) along with negative results on both MRI TB and SB (initial biopsy).\u003c/p\u003e \u003cp\u003eDuring the first 18 months, we classified patient management according to three different types of follow-ups, as decided by treating urologist:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGroup 1: Prostate re-biopsy without a new mpMRI.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGroup 2: Standardized second prostate mpMRI and subsequent re-biopsy, including either MRI-TBx and/or SB. Detailed information regarding this population is described elsewhere [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGroup 3: Follow-up with mpMRIs and prostate biopsy based on clinical and radiological triggers. Depending on each institution's protocols, triggers included PSA increase, DRE changes and radiological progression observed in MRIs performed after the initial biopsy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe exclusion criteria included patients who underwent systematic biopsy before the initial MRI-TBx, as well as individuals who were previously diagnosed with PCa.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProstate Biopsy Techniques\u003c/h2\u003e \u003cp\u003eA mpMRI was performed before the first biopsy, following each institution's protocol. All centers utilized the PI-RADS v2 scoring system to assess MRI findings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Expert genitourinary radiologists reviewed all MRIs in accordance with the ESUR/ESUI consensus for image acquisition, interpretation, and radiologists' training [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Transrectal or transperineal targeted biopsies were performed by experienced urologists using their preferred biopsy approach. TBs were performed using dedicated biopsy fusion software or cognitive methods, according to the expertise of each center. Transperineal TB was performed with a brachytherapy grid or freehand technique under general or local anesthesia. The number of SB after TB were performed according to each institution protocol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAssessing the probability of any PCA and csPCA at the first negative biopsy\u003c/h2\u003e \u003cp\u003eThe probability of any PCA and csPCA has been calculated for each patient with the ERSPC-MRI risk groups (RC5, and RC6) at the first negative biopsy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To ensure the optimal risk prediction in our cohort, the probability has been recalibrated according to the present cohort PCA and csPCA prevalence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were presented as frequencies, while continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for normally distributed variables and as median and interquartile range (IQR) for non-normally distributed variables. Differences in baseline characteristics between categorical and continuous variables were assessed using either chi-square, ANOVA or Mann-Whitney U test, as appropriate. Kruskal \u0026ndash; Wallis test was used to assess any significant differences on a continuous dependent variable by the three groups.\u003c/p\u003e \u003cp\u003ePCA and csPCA detection-free survival were evaluated using Kaplan-Meier analysis. The multiple log \u0026ndash; rank test was used for comparison of the survival curves. Univariable (UVA) and multivariable (MVA) Cox regression analyses were performed to evaluate predictors for PCA and csPCA at the moment of repeat biopsy. CsPCA was defined as any ISUP\u0026thinsp;\u0026ge;\u0026thinsp;2 cancer. Covariates included in the model were selected based on univariable results with p \u0026ndash; values\u0026thinsp;\u0026le;\u0026thinsp;0.1. Variables with suspicious interaction terms (PSA and Prostate volume with PSAD as well as PI-RADS with cT stage at MRI) were adjusted accordingly. The analyses were performed in the whole population and in the subgroup with PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions.\u003c/p\u003e \u003cp\u003eA significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used for all tests. Statistical analyses were performed using SPSS version 28 (IBM, Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the characteristics of the initial biopsy and follow-up for the 694 patients in the whole cohort and stratified by study group. Each center contributed with all three follow-up strategies. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the evolution of the diagnostic pathway from the first biopsy to the available follow \u0026ndash; up of this selected patient population in a Sankey diagram.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003echaracteristics of the 694 patients with positive mpMRI and initial negative biopsy. Full cohort and stratification by study group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eWhole cohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;694)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGRUP 1\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGRUP 2\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;290)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGRUP 3\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;346)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.2 (61\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (61\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (61\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (61\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapy with 5 \u0026ndash; ARI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA at initial biopsy (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (4.7\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4 (4.9\u0026ndash;9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5 (4.7\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4 (4.8 \u0026minus;\u0026thinsp;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian prostate volume (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (40\u0026ndash;71.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (35\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (40\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55 (42\u0026ndash;75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSAD (ng/ml/cc\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12 (0.08\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.09 \u0026minus;\u0026thinsp;0.25) \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.09\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12 (0.08\u0026ndash;0.17) \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePositive rectal examination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eClinical stage\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eT3 at mpMRI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum diameter of the lesion (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5 (8\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (6\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI-RADS score\u003c/p\u003e \u003cp\u003e\u0026ndash; 3\u003c/p\u003e \u003cp\u003e\u0026ndash; \u0026gt;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (44%)\u003c/p\u003e \u003cp\u003e387 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (62%)\u003c/p\u003e \u003cp\u003e22 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e137 (47%)\u003c/p\u003e \u003cp\u003e153 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134 (39%)\u003c/p\u003e \u003cp\u003e212 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoftware \u0026ndash; based registration for initial B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e529 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e307 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransperineal biopsy at initial biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e212 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of SB at initial biopsy\u003c/p\u003e \u003cp\u003e\u0026ndash; Not Performed\u003c/p\u003e \u003cp\u003e\u0026ndash; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le\\)\u003c/span\u003e\u003c/span\u003e12\u003c/p\u003e \u003cp\u003e\u0026ndash; \u0026gt;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (12%)\u003c/p\u003e \u003cp\u003e221 (32%)\u003c/p\u003e \u003cp\u003e186 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (17%)\u003c/p\u003e \u003cp\u003e29 (50%)\u003c/p\u003e \u003cp\u003e19 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (14%)\u003c/p\u003e \u003cp\u003e170 (59%)\u003c/p\u003e \u003cp\u003e80 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (10%)\u003c/p\u003e \u003cp\u003e225 (65%)\u003c/p\u003e \u003cp\u003e87 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3 cores TB at initial biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of ASAP at initial biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERSPC-MRI risk calculator estimated risk of PCA after the first negative biopsy.\u003c/p\u003e \u003cp\u003eMedian Probability any PCA (IQR)\u003c/p\u003e \u003cp\u003eMedian Probability of csPCA (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20 (0.1\u0026ndash;0.3)\u003c/p\u003e \u003cp\u003e0.09 (0.03\u0026ndash;0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23 (0.11\u0026ndash;0.36)\u003c/p\u003e \u003cp\u003e0.09 (0.04\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.11\u0026ndash;0.35)\u003c/p\u003e \u003cp\u003e0.08 (0.04\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20 (0.10\u0026ndash;0.30)\u003c/p\u003e \u003cp\u003e0.09 (0.03\u0026ndash;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e0.4\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of MRI scans during follow \u0026ndash; up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0*\u003csup\u003e+\u003c/sup\u003e (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;2) \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;2) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of biopsies after first biopsy\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e253 (36.4%)\u003c/p\u003e \u003cp\u003e407 (58.6%)\u003c/p\u003e \u003cp\u003e28 (4.0%)\u003c/p\u003e \u003cp\u003e6 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e55 (95%)\u003c/p\u003e \u003cp\u003e3 (5%)\u003c/p\u003e \u003cp\u003e0 ( %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e271 (93.4%)\u003c/p\u003e \u003cp\u003e14 (4.8%)\u003c/p\u003e \u003cp\u003e5 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e253 (73.1%)\u003c/p\u003e \u003cp\u003e81 (23.4%)\u003c/p\u003e \u003cp\u003e11 (3.1%)\u003c/p\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of prostate biopsy after the initial negative biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3 cores TB at repeated biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202/441 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21/58 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141/290 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40/93 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal diagnosis of PCA\u003c/p\u003e \u003cp\u003e\u0026ndash; Any cancer\u003c/p\u003e \u003cp\u003e\u0026ndash; Clinically significant cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174 (27%)\u003c/p\u003e \u003cp\u003e134 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (50%)\u003c/p\u003e \u003cp\u003e21 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (38%)\u003c/p\u003e \u003cp\u003e86 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (10%)\u003c/p\u003e \u003cp\u003e27 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal diagnosis of PCA per \"repeat biopsy analysis\u0026rdquo;\u003c/p\u003e \u003cp\u003eTotal diagnosis of PCA\u003c/p\u003e \u003cp\u003e\u0026ndash; Any cancer\u003c/p\u003e \u003cp\u003e\u0026ndash; Clinically significant cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174/441 (39%)\u003c/p\u003e \u003cp\u003e134/441 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (50%)\u003c/p\u003e \u003cp\u003e21 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (38%)\u003c/p\u003e \u003cp\u003e86 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35/93 (37%)\u003c/p\u003e \u003cp\u003e27/93 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian time from fist MRI to PCA diagnosis or last follow up (mo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (13\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (13.3\u0026ndash;40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.4 (16.7\u0026ndash;44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.0 (9\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eGroup 1: Prostate re \u0026ndash; biopsy without a new mpMRI; group 2: repeated prostate mpMRI and subsequent re \u0026ndash; biopsy, including either MRI \u0026ndash; TBx and/or SB; group 3: follow \u0026ndash; up with mpMRIs and prostate biopsy based on clinical and radiological triggers\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Missing data\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePSAD: PSA density; SB: systemic biopsy; TB: Targeted biopsy; ASAP: atypical small acinar proliferation; PCA: prostate cancer\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, we identified 174 (27%) any grade PCA and 134 (19%) csPCA at a median follow-up duration of 28 mo (13\u0026ndash;51). The median time from the first MRI to PCA diagnosis or last follow-up was 24 (13\u0026ndash;40), 28 mo (17\u0026ndash;45), and 20 mo (9\u0026ndash;51) for groups 1 to 3, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The detection of any PCA and csPCA was 19% and 15% in initial PI-RADS 3 lesions; 27% and 21% in initial PI-RADS 4 lesions; and 60% and 37% in initial PI-RADS 5 lesions.\u003c/p\u003e \u003cp\u003eThe three groups differ in most of the clinical and radiological characteristics. However, no differences have been observed among the three groups in the detection of any PCA and csPCA after the first negative biopsy as estimated by the ERSPC-MRI PCA risk calculator.\u003c/p\u003e \u003cp\u003eFrom the original biopsy, the median number of mpMRI was 0 (0 \u0026minus;\u0026thinsp;1) for group 1 and 1 (1 \u0026minus;\u0026thinsp;2) for groups 2 and 3 (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The median number of prostate re-biopsy was 1 (1\u0026ndash;2) in groups 1 and 2 and 1 (0\u0026ndash;1) in group 3 (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Overall, 75% of the patients had software-based registration for repeated TB, and 60% of the patients had a transperineal biopsy during repeated biopsies.\u003c/p\u003e \u003cp\u003eIn group 1, the re-biopsy was performed at a median of 13 (10\u0026ndash;14) months after the original biopsy and detected PCA and csPCA in 29 (50%) and 21 patients (36%). Among the patients with negative repeated biopsy, 2 had further mpMRI, and 2 patients had further prostatic biopsy. At a follow-up of 24 (IQR: 13.3. 40.2), no further cancer was diagnosed. Data for group 2 were extensively reported previously [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The interval from the initial to repeated mpMRI and from the initial to repeated biopsy were 16 mo (IQR 12\u0026ndash;20) and 18 mo (IQR 12\u0026ndash;21), respectively. The PI-RADS score at the second MRI was classified as \u0026lt;\u0026thinsp;3 in 25 patients (9%), 3 in 91 patients (31%), 4 in 137 patients (47%), and 5 in 37 patients (13%). One hundred and eight patients (37%) were diagnosed with PCA and 74 (25.5%) with csPCA at re-biopsy. SB and MRI-TBx identified PCA in 28% and 31% of the cases (including 18% and 20% of csPCA).\u003c/p\u003e \u003cp\u003eThe median follow-up from second negative biopsy was 20 months (IQR: 5.7\u0026ndash;34.7). Further MRI and subsequent biopsy were performed in 19 patients, of whom only 2 patients were diagnosed with PCA.\u003c/p\u003e \u003cp\u003eIn group 3, additional mpMRI during follow up was performed in 198 (69%) patients, identifying graded as PI-RADS\u0026thinsp;\u0026lt;\u0026thinsp;3, 3 and \u0026gt;\u0026thinsp;3 in 24%, 68%, and 8% of patients, respectively. On the whole, 93 patients (27%) received at least one repeated biopsy during follow-up. PCA and csPCA were detected in 35 (12%) and 27 patients (9.3%), however in a \u0026ldquo;repeat biopsy analysis\u0026rdquo; PCA and csPCA were detected in a percentage similar to group 1 and 2 (p\u0026thinsp;=\u0026thinsp;0.06).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of any grade and clinically significant PCA\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports Kaplan-Meier curves for PCA and csPCA free survival in the whole cohort and stratified by study group and PI-RADS score.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, the 2- and 5-year PCA free survival estimates were 99% and 65% respectively; the 2- and 5-year csPCA free survival estimates were 99% and 71%, respectively.\u003c/p\u003e \u003cp\u003eWhen stratified by study group, PCA-free and csPCA-free survival estimates were similar in groups 1 and 2 (log rank p value 0.4 for PCA and 0.7 for csPCA) but significantly higher in group 3 (log rank p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In particular the 2-year and 5 year Pca were respectively 63% and 50% for group 1, 72% and 51% for group 2 and 94% and 84% for group 3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 between group 3 vs group 1 and group 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 2-year and 5-year csPca were respectively 77% and 60% for group 1, 94% and 61% for group 2 and 96% and 86% for group 3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 between group 3 vs group 1 and group 2).\u003c/p\u003e \u003cp\u003eWhen stratified by PI-RADS score, PCA-free and csPCA-free survival estimates were significantly the lowest in PI-RADS 5 lesions and the highest in PI-RADS 3 lesions (log rank p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes UVA and MVA Cox regression analyses assessing clinical and radiological predictors of PCA and csPCA after the initial negative biopsy.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable analysis assessing predictors of any prostate cancer and clinically-significant prostate cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI of HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI of HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAny cancer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 vs group 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u0026ndash;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 vs group 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA at initial biopsy (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate volume (ml) at initial biopsy (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u0026ndash;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSAD (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI-RADS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 vs 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026ndash;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 vs 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026ndash;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4\u0026ndash;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT2 vs cT\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3 stage at MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e* interaction terms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrasrectal vs trasperineal route\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u0026ndash;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive vs software fusion biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of TB cores (\u0026le;\u0026thinsp;3 vs\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SB vs\u0026thinsp;\u0026le;\u0026thinsp;12 SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u0026ndash;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u0026ndash;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SB vs\u0026thinsp;\u0026gt;\u0026thinsp;12 SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u0026ndash;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026ndash;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinically significant prostate cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnivariable analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMultivariable analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 vs group 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4 \u0026minus;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 vs group 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026ndash;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 \u0026minus;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA at initial biopsy (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 \u0026ndash; 1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate volume (ml) at initial biopsy (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSAD (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI-RADS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 vs 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026ndash;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u0026ndash;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 vs 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u0026ndash;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u0026ndash;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT2 vs cT\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3 stage at MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e*interaction term\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrasrectal vs trasperineal route\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026ndash;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u0026ndash;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive vs software fusion biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 \u0026minus;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of TB cores (\u0026le;\u0026thinsp;3 vs\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u0026ndash;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SB vs\u0026thinsp;\u0026le;\u0026thinsp;12 SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u0026ndash;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SB vs\u0026thinsp;\u0026gt;\u0026thinsp;12 SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 \u0026minus;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u0026ndash;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026ndash;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eHR: hazard ratio PSAD: PSA density; SB: Systematic biopsy; TB: Targeted biopsy; ASAP: atypical small acinar proliferation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multivariable analysis, there was no statistically significant difference between group 1 and 2 (HR\u0026thinsp;=\u0026thinsp;0.8, 95% CI\u0026thinsp;=\u0026thinsp;0.5\u0026ndash;1.3, p\u0026thinsp;=\u0026thinsp;0.4), see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. However, the hazard on PCa and csPCa (HR\u0026thinsp;=\u0026thinsp;0.2, 95% CI 0.1\u0026ndash;0.4, p\u0026thinsp;=\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was lower for Group 3 vs Group 1.\u003c/p\u003e \u003cp\u003eFurthermore, several clinical and radiological covariates (including patients age, prostate volume, PI-RADS score at the first MRI, presence of ASAP at the initial biopsy, number of TB and SB performed during the initial biopsy) were identified as predictors of PCA diagnosis during further follow-up.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes UVA and MVA Cox regression analyses assessing clinical and radiological predictors of PCA and cs-PCA after the initial negative biopsy in cases with PI-RADS\u0026thinsp;\u0026gt;\u0026thinsp;3 at the first MRI.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable and multivariable analysis assessing predictors of any prostate cancer and clinically \u0026ndash; significant prostate cancer during follow-up for PI-RADS\u0026thinsp;\u0026gt;\u0026thinsp;3 lesions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI of HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI of HR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAny cancer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 vs group 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u0026ndash;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 vs group 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026ndash;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u0026ndash;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA at initial biopsy (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate volume (ml) at initial biopsy (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e*interaction term\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSAD (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026ndash;47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u0026ndash;29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT2 vs cT\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3 stage at MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u0026ndash;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrasrectal vs trasperineal route\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive vs software fusion biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u0026ndash;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of TB cores (\u0026le;\u0026thinsp;3 vs\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SB vs\u0026thinsp;\u0026le;\u0026thinsp;12 SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SB vs\u0026thinsp;\u0026gt;\u0026thinsp;12 SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u0026ndash;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u0026ndash;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinically significant prostate cancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnivariable analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMultivariable analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 vs group 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u0026ndash;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup 1 vs group 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026ndash;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u0026ndash;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSA at initial biopsy (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate volume (ml) at initial biopsy (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSAD (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u0026ndash;42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u0026ndash;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT2 vs cT\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3 stage at MRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u0026ndash;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0\u0026ndash;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrasrectal vs trasperineal route\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026ndash;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u0026ndash;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive vs software fusion biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u0026ndash;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of TB cores (\u0026le;\u0026thinsp;3 vs\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026ndash;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SB vs\u0026thinsp;\u0026le;\u0026thinsp;12 SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026ndash;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u0026ndash;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo SB vs\u0026thinsp;\u0026gt;\u0026thinsp;12 SB cores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026ndash;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u0026ndash;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u0026ndash;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eHR: hazard ratio PSAD: PSA density SB: Systematic biopsy TB: Targeted biopsy ASAP: atypical small acinar proliferation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOnce again, the study group variable, the number of systematic biopsies, and the presence of ASAP at the initial biopsy were identified as predictors of subsequent detection of PCA and csPCA. In addition, PSAD was an independent predictor of PCA (HR: 6.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTreatments\u003c/h2\u003e \u003cp\u003eSupplementary table 1 summarizes the treatment for the patients diagnosed with PCA and radical prostatectomy specimen data for the patients who received surgery.\u003c/p\u003e \u003cp\u003eAmong the selected treatment options, active surveillance/watchful waiting, surgery, radiation therapy, and focal therapy were chosen by 36 (21.4%), 109 (64.9%), 19 (11.3%), and 5 (2.4%) patients, respectively (supplementary table 1).\u003c/p\u003e \u003cp\u003eAmong the 109 radical prostatectomies, 53/109 had a pT stage \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003eT3 and 63/109 had a ISUP score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe presented study investigates the characteristics, follow-up outcomes, treatment decisions, and predictors of PCA and csPCA in a cohort of 694 patients with an initial positive mpMRI and negative prostate biopsies followed up according to different protocols. Overall, about 25% of the patients were diagnosed with PCA, more than 75% of which were identified as csPCA. This underscores the critical importance of establishing an accurate follow-up schedule for these patients, as the presence of csPCA cannot be definitively ruled out in a significant number of patients. Notably, about 16% of those patients with Pca underwent radical prostatectomy during the available follow-up. Among these patients, more than 50% exhibited a pT stage \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003eT3 and about the same percentage had a ISUP score \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3.\u003c/p\u003e \u003cp\u003eWe conducted a comprehensive comparative analysis of three distinct follow-up schedules, revealing outcomes disparities. Patients who underwent a second biopsy without a new MRI exhibited similar results to those who received a new MRI before the second biopsy. Conversely, individuals who underwent a second biopsy triggered by specific clinical and radiological factors demonstrated a low detection of PCA and csPCA. This underscores the need for a risk-based strategy to mitigate overdiagnosis on one hand and, on the other, a more rigorous follow-up approach to prevent the omission of caPCA diagnosis. In particular, the potential limitations of the first TB emphasize the need for further investigations and follow-up in cases with inconsistent results between MRI images and MRI-guided biopsy findings. In addition to MRI, a possible role of PET/CT has been explored in recent studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e][\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The implementation of PSMA PET/CT with MRI results would help the selection of men who would benefit the most of screening or further biopsies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe significance of second MRI within the initial 18 months after the first biopsy has been assessed in our prior publication [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In cases where lesions were initially described in the first MRI, downgrading occurred in only 19% of instances, while upgrading was observed in 39%, and stability was maintained in 42% of cases. The approach of combining SB and TB yielded elevated detection rates. For patients with lesions detected at the first MRI, the positivity rates were 16%, and for those with new lesions detected at the second MRI, the rate was 17.2% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, the data of the present analysis suggested very similar diagnostic performance with group 1, i.e. the group of patients who repeated prostate biopsy without a new mpMRI. That was shown in the whole cohort and the subgroup of patients with initial PI-RADS lesions graded as 4 or 5. Unfortunately, even in a large multicenter series, the limited number of cases did not allow to further stratify the analyses.\u003c/p\u003e \u003cp\u003eThe literature on the topic is quite limited. In a recent mini-systematic review, Grivas et al. identified only nine studies, including less than 500 patients in this clinical setting. Overall, the systematic review demonstrated that the detection of csPCA was highly variable among the few available studies. Specifically, csPCA was detected in 7.5 to 80% of the patients with PI-RADS 3 lesions, 17 to 75% with PI-RADS 4 lesions, and over 80% with PI-RADS 5 lesions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Different factors, including heterogeneity in mpMRI quality and accuracy, inaccuracy/errors in TB or SB, and discrepancies in the follow-up protocols can explain such large differences. In the present analysis, we identified csPCA in about 20% of the whole population and 36%, 30%, and 8% of groups 1 to 3, respectively. Notably, those patients who were followed up less strictly, with repeated biopsy triggered by PSA increase, DRE changes and radiological progression in repeated MRIs, had a significantly lower chance of being diagnosed with PCA and csPCA. Although several differences in the patients\u0026rsquo; characteristics are evident among the different study groups, the detection of PCA and csPCA estimated by the ERSPC-MRI PCA risk calculator was similar. The data is constant in all our analyses and might allow us to hypothesize a certain level of underdectection of csPCA in this subgroup of patients.\u003c/p\u003e \u003cp\u003eWe identified several predictors of PCA and csPCA during the initial biopsy that warrant thorough evaluation when considering the decision for a second biopsy during patient consultation. Specifically, the presence of a high PI-RADS score, advanced age, smaller prostate volume, adequate prostate biopsy sampling with \u0026gt;\u0026thinsp;3 fusion cores and \u0026gt;\u0026thinsp;12 systematic biopsies, along with the presence of ASAP at the first biopsy, can serve as indicators for the likelihood of PCA and csPCA presence.\u003c/p\u003e \u003cp\u003eASAP is regarded as a precursor lesion, often indicating that the prostate tissue is undergoing changes that are more likely to progress to cancer over time. While not all instances of ASAP will inevitably develop into cancer, its presence prompts clinicians to engage in closer patient monitoring. However, there is an ongoing debate regarding the role of ASAP in the MRI era [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e][\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In our study, we observed a robust correlation between the presence of ASAP on the initial biopsy and the likelihood of PCA and csPCA occurrence. This supports the growing interest in investigating glandular \u0026ndash; stromal alterations, along with acute or chronic inflammation and vascular changes.\u003c/p\u003e \u003cp\u003eThe present study is relevant for several reasons. It includes more patients than the only available systematic review on the topic. Consequently, the study provides more reliable data on the detection of PCA and, above all, csPCA in such an interesting patients population. Moreover, the series collected data from different tertiary referral centers, indicating a potentiality for good validity of the data in real life practice. Third, we provided data on PCA and csPCA predictors. Several limitations should be acknowledged. First of all, despite the present series being large, the number of patients with initial PI-RADS 4 or 5 lesions at MRI and subsequently negative TB/SB is limited. That might have made some of our analyses underpowered and limited our ability to perform more accurate subgroup analyses. Secondly, the study is retrospective, which introduces a significant risk of selection bias for follow-up, and patients were followed with different protocols. In other words, it is possible that patients with a higher risk of cancers, in the opinion of the attending urologists, might have been followed more strictly than those with a potentially lower risk. Although our statistical analyses tried to correct for the differences in covariate distributions, we cannot be sure that a selection bias might explain at least partially our findings. However, the finding that the detection of PCA and csPCA estimated by the ERSPC-MR PCA risk calculator was similar in the 3 groups suggest that such selection bias should not play a major role. The dataset includes both transperineal and transrectal prostate biopsies which may have different detection rate [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and the results could have been different including one procedure only. The present study is preliminary, and randomized controlled trials evaluating various follow-up protocols in patients with positive MRI and negative TB/SB results would be valuable for standardizing the follow-up procedures for these patients. Finally, at the current follow- up, we are not able to understand the prognostic implications related to the diagnosis of such csPCA, which could arguably have lower volume and reduced clinical aggressively compared to those diagnosed in the first prostatic biopsy.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eProstate cancer diagnostics should be regarded as a longitudinal process, instead of a cross-sectional one-time approach. Overall, our findings contribute to a better understanding of patient characteristics, follow-up trajectories, treatment preferences, and predictive factors for PCA and csPCA, offering valuable insights for clinical decision-making and management strategies in these men with abnormal MRI but negative biopsy. Less aggressive re-imaging and re-biopsy may lead to more csPCa being missed. Improved knowledge on follow-up findings aids in primary biopsy decisions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003eprostate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003ecsPCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003eclinically significant prostate cancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003empMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003emultiparametric MRI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003enegative MRI \u0026ndash; guided target biopsy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eASAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003eatypical small acinar proliferation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003estandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eUVA - MVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003eUnivariable and multivariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCOMPLIANCE WITH ETHICAL STANDARD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial disclosure:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer:\u003c/strong\u003e The authors declared that they have no conflict of interests.\u003c/p\u003e\n\u003cp\u003eAll included patients undergoing radical treatment provided written informed consent for surgery. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Institutional review board apply to each center due to observational and retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026apos;s Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eProtocol/project development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003eF Zattoni, G Gandaglia, G Novara, R C N van den Bergh, A Briganti\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eData collection or management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003eLJ.Paulino Pereira, G Marra, M Valerio, J Olivier, I Puche \u0026ndash; SanzI, M Maggi, R Campi, D Amparore, S De Cillis, Z Junlong, H Guo, G La Bombarda, A Fuschi\u003csup\u003e.\u003c/sup\u003e, A Veccia, F Ditonno, A Marquis, F Barletta, R Leni, S Serni,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eData analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003eF Zattoni, G Novara, F Dal Moro, Sebastiaan Remmers, Monique J. Roobol,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.8006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eManuscript writing/editing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.19937694704049%\" valign=\"top\"\u003e\n \u003cp\u003eF Zattoni, G Novara, R C N van den Bergh, A Antonelli, K Veeru, P Rajwa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKasivisvanathan V, Stabile A, Neves JB, Giganti F, Valerio M, Shanmugabavan Y, et al. Magnetic Resonance Imaging-targeted Biopsy Versus Systematic Biopsy in the Detection of Prostate Cancer: A Systematic Review and Meta-analysis. 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European Urology 2016;69:16\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eururo.2015.08.052\u003c/span\u003e\u003cspan address=\"10.1016/j.eururo.2015.08.052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Rooij M, Isra\u0026euml;l B, Tummers M, Ahmed HU, Barrett T, Giganti F, et al. ESUR/ESUI consensus statements on multi-parametric MRI for the detection of clinically significant prostate cancer: quality requirements for image acquisition, interpretation and radiologists\u0026rsquo; training. Eur Radiol 2020;30:5404\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00330-020-06929-z\u003c/span\u003e\u003cspan address=\"10.1007/s00330-020-06929-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberts AR, Roobol MJ, Verbeek JFM, Schoots IG, Chiu PK, Osses DF, Tijsterman JD, Beerlage HP, Mannaerts CK, Schimm\u0026ouml;ller L, Albers P, Arsov C. Prediction of High-grade Prostate Cancer Following Multiparametric Magnetic Resonance Imaging: Improving the Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculators. Eur Urol. 2019;75(2):310\u0026ndash;318. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eururo.2018.07.031\u003c/span\u003e\u003cspan address=\"10.1016/j.eururo.2018.07.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmmett L, Buteau J, Papa N, Moon D, Thompson J, Roberts MJ, et al. The Additive Diagnostic Value of Prostate-specific Membrane Antigen Positron Emission Tomography Computed Tomography to Multiparametric Magnetic Resonance Imaging Triage in the Diagnosis of Prostate Cancer (PRIMARY): A Prospective Multicentre Study. Eur Urol 2021;80:682\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eururo.2021.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.eururo.2021.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeissner VH, Rauscher I, Schwamborn K, Neumann J, Miller G, Weber W, et al. Radical Prostatectomy Without Prior Biopsy Following Multiparametric Magnetic Resonance Imaging and Prostate-specific Membrane Antigen Positron Emission Tomography. Eur Urol 2022;82:156\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eururo.2021.11.019\u003c/span\u003e\u003cspan address=\"10.1016/j.eururo.2021.11.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang W, Tang Y, Qi L, Zhang Y, Tang G, Gao X, et al. BPH-related False Positive of PSMA-PET in the Diagnosis of Prostate Cancer: the Achilles\u0026rsquo; Heel of Biopsy-free Radical Prostatectomy? J Urol 2023:101097JU0000000000003680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/JU.0000000000003680\u003c/span\u003e\u003cspan address=\"10.1097/JU.0000000000003680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordetsky JB, Ullman D, Schultz L, Porter KK, Del Carmen Rodriguez Pena M, Calderone CE, et al. Histologic findings associated with false-positive multiparametric magnetic resonance imaging performed for prostate cancer detection. Human Pathology 2019;83:159\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.humpath.2018.08.021\u003c/span\u003e\u003cspan address=\"10.1016/j.humpath.2018.08.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHupe MC, Offermann A, Tharun L, F\u0026uuml;rschke A, Frydrychowicz A, Garstka N, et al. Histomorphological analysis of false positive PI-RADS 4 and 5 lesions. Urologic Oncology: Seminars and Original Investigations 2020;38:636.e7-\u003cdiv class=\"ExternalRefDOI\"\u003e636.e12\u003c/div\u003e. https://doi.org/10.1016/j.urolonc.2020.01.017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZattoni F, Marra G, Kasivisvanathan V, Grummet J, Nandurkar R, Ploussard G, et al. The Detection of Prostate Cancer with Magnetic Resonance Imaging-Targeted Prostate Biopsies is Superior with the Transperineal vs the Transrectal Approach. A European Association of Urology-Young Academic Urologists Prostate Cancer Working Group Multi-Institutional Study. Journal of Urology 2022;208:830\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/JU.0000000000002802\u003c/span\u003e\u003cspan address=\"10.1097/JU.0000000000002802\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZattoni F, Marra G, Martini A, Kasivisvanathan V, Grummet J, Harkin T, et al. Is There an Impact of Transperineal Versus Transrectal Magnetic Resonance Imaging\u0026ndash;targeted Biopsy on the Risk of Upgrading in Final Pathology in Prostate Cancer Patients Undergoing Radical Prostatectomy? An European Association of Urology-Young Academic Urologists Prostate Cancer Working Group Multi-institutional Study. European Urology Focus 2023;9:621\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.euf.2023.01.016\u003c/span\u003e\u003cspan address=\"10.1016/j.euf.2023.01.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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 Cancer, Diagnosis prostate MRI, negative biopsy, Target Prostate Biopsy, targeted biopsy","lastPublishedDoi":"10.21203/rs.3.rs-4263695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4263695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo investigate the detection and predictors of prostate cancer (PCA) and clinically significant prostate cancer (csPCA) in patients with positive multiparametric MRI (mpMRI) followed by a negative MRI \u0026ndash; guided target biopsy (TB) and systematic biopsy (SB).\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThis retrospective multicenter study included 694 patients from 10 tertiary referral centers with an initial positive mpMRI (PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;3) and negative results on both MRI-TB and SB. Patients were classified into three groups based on follow-up: Group 1 (prostate re-biopsy without new mpMRI), Group 2 (standardized second prostate mpMRI and subsequent re-biopsy), and Group 3 (follow-up with mpMRIs and biopsy based on clinical and radiological triggers). The primary outcomes were the detection of any PCA and csPCA during follow up. Study groups were compared according to their probability of PCA and csPCA assessed with the ERSPC-MRI risk calculator. Statistical analysis included Kaplan \u0026ndash; Meier analysis, Cox regression, and multivariable analysis for the detection of (cs)PCa.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe overall detection of PCA and csPCA was 26.8% and 19.3%, respectively, with varying rates in different PI-RADS groups. Group 3 had the highest 2 \u0026ndash; year and 5 \u0026ndash; year PCA \u0026ndash; free survival (94% and 84%) and csPCA \u0026ndash; free survival (96% and 86%). Multivariable analysis revealed a significantly higher risk of PCA and csPCA in Group 1 and 2 compared to Group 3. Clinical and radiological predictors for PCA and csPCA included higher age, prostate volume, PI-RADS score, the presence of atypical small acinar proliferation (ASAP), and a smaller number of TB and SB performed during the initial biopsy. Study limitations, include the retrospective design and reliance on clinical and radiological triggers for follow \u0026ndash; up decisions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePatients with positive mpMRI but negative TB and SB results exhibit varying rates of PCA and csPCA depending on the follow up scheme. Tailored follow-up strategies are essential for optimal management in this clinical scenario.\u003c/p\u003e","manuscriptTitle":"Follow-up on Patients with Initial Negative mpMRI Target and Systematic Biopsy for PI-RADS ≥3 Lesions – An EAU-YAU Study Enhancing Prostate Cancer Detection.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 20:27:20","doi":"10.21203/rs.3.rs-4263695/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-08-07T15:26:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-07-14T14:52:06+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-06-29T10:27:33+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-29T10:03:58+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-06-28T11:05:44+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-04-29T19:07:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T18:15:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-16T07:42:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Prostate Cancer and Prostatic Diseases","date":"2024-04-14T05:32:56+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":"4262d902-75b2-48ea-948e-eb9a9174de08","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31561601,"name":"Health sciences/Diseases/Cancer/Urological cancer/Prostate cancer"},{"id":31561602,"name":"Health sciences/Diseases"},{"id":31561603,"name":"Health sciences/Medical research/Outcomes research"}],"tags":[],"updatedAt":"2024-11-06T08:08:03+00:00","versionOfRecord":{"articleIdentity":"rs-4263695","link":"https://doi.org/10.1038/s41391-024-00904-1","journal":{"identity":"prostate-cancer-and-prostatic-diseases","isVorOnly":false,"title":"Prostate Cancer and Prostatic Diseases"},"publishedOn":"2024-11-05 05:00:00","publishedOnDateReadable":"November 5th, 2024"},"versionCreatedAt":"2024-05-07 20:27:20","video":"","vorDoi":"10.1038/s41391-024-00904-1","vorDoiUrl":"https://doi.org/10.1038/s41391-024-00904-1","workflowStages":[]},"version":"v1","identity":"rs-4263695","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4263695","identity":"rs-4263695","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}