Improvement of the intermediate risk prostate cancer sub-classification by integrating MRI and fusion biopsy features

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Urologic Oncology: Seminars and Original Investigations 000 (2019) 1−7

Clinical-Prostate cancer

Improvement of the intermediate risk prostate cancer sub-classification by integrating MRI and fusion biopsy features Mathieu Roumiguie, M.D.c, Marine Lesourd, M.D.c, Joseph Zgheib, M.D.c, Christophe Tollon, M.D.a, Ambroise Salin, M.D.a, Christophe Almeras, M.D.a, Nicolas Doumerc, M.D.c, Mathieu Thoulouzan, M.D.c, Michel Soulie, M.D., Ph.D.c, Jean-Romain Gautier, M.D.a, Guillaume Loison, M.D.a, Jacques Assoun, M.D.d, Aurore Vacher, M.D.d, Richard Aziza, M.D.e, Bernard Malavaud, M.D., Ph.D.b, Jean-Baptiste Beauval, M.D.a, Guillaume Ploussard, M.D., Ph.D.a,* a Department of Urology, La Croix du Sud Hospital, Quint Fonsegrives, France Department of Urology, Institut Universitaire du Cancer Toulouse - Oncopole, Toulouse, France c Department of Urology, CHU Toulouse, Toulouse, France d Department of Radiology, La Croix du Sud Hospital, Quint Fonsegrives, France e Department of Radiology, Institut Universitaire du Cancer Toulouse - Oncopole, Toulouse, France b

Received 26 August 2019; received in revised form 24 November 2019; accepted 19 December 2019

Abstract Introduction: Treatment decision-making for intermediate-risk prostate cancer (CaP) is mainly based on grade and tumor involvement on systematic biopsy. We aimed to assess the added value of multi-parametric magnetic resonance imaging (mpMRI) and targeted biopsy (TB) features for predicting final pathology and for improving the well-established favourable/unfavourable systematic biopsy-based sub-classification. Materials and Methods: From a prospective database of 377 intermediate risk CaP cases, we evaluated the performance of the standard intermediate risk classification (IRC), and the predictive factors for unfavourable disease on final pathology aiming to build a new model. Overall unfavourable disease (OUD) was defined by any pT3-4 and/or pN1 and/or grade group (GG) ≥ 3. Results: The standard IRC was found to be predictive for unfavourable disease in this population. However, in multivariable analysis regression, ECE on mpMRI and GG ≥3 on TB remained the 2 independent predictive factors for OUD disease (HR = 2.7, P = 0.032, and HR = 2.41, P = 0.01, respectively). By using the new IRC in which unfavorable risk was defined by ECE on mpMRI and/or GG ≥3 on TB, the proportion of unfavorable cases decreased from 62.3% to 34.1% while better predicting unfavorable disease in RP speciments. The new model displayed a better accuracy than the standard IRC for predicting OUD (AUC: 0.66 vs. 0.55). Conclusions: The integration of imaging and TB features drastically improves the intermediate risk sub-classification performance and better discriminates the unfavourable risk group that could benefit from more aggressive therapy such as neo-adjuvant and/or adjuvant treatment, and the favourable group that could avoid over-treatment. External validation in other datasets is needed. Ó 2019 Elsevier Inc. All rights reserved.

Keywords: Prostate cancer; Radical prostatectomy; Intermediate risk; Biopsy; Targeted biopsies, Multiparametric MRI; Fusion biopsies

Financial disclosure: None. Author’s contributions: Drafting: M Roumiguie, M Lesourd, J Zgheib, D G Ploussard. Data collection: M Roumiguie, M Lesourd, J Zgheib, D Portalez, C Tollon, A Salin, C Almeras, N Doumerc, M Thoulouzan, M Soulie, JR Gautier, G Loison, J Assoun, A Vacher, R Aziza, B Malavaud, JB Beauval, G Ploussard. https://doi.org/10.1016/j.urolonc.2019.12.018 1078-1439/Ó 2019 Elsevier Inc. All rights reserved.

Statistics: M Roumiguie, G Ploussard. Revision and critical analysis: B Malavaud, M Soulie. *Corresponding author. Tel.: + 33 5 32 02 72 02. E-mail addresses: [email protected], [email protected] (G. Ploussard).

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1. Introduction The management of patients with localized prostate cancer remains a challenge as it is composed of many groups with different prognosis disease. The real challenge is to detect the poor prognosis patients to plan adequate treatment and on the other hand, avoid overtreatment for indolent prostate cancer patients. Despite a classification proposed by D’Amico et al. to stratify men into low, intermediate and high-risk groups, we believe that it poses a significant clinical heterogeneity particularly for patients with intermediate risk-group disease [1,2]. Indeed, some patients harbor aggressive characteristics at final pathology such as extracapsular extension (ECE), seminal vesicle invasion (SVI), and high grade group tumor, increasing the risk of early recurrence after surgery. On the other hand, other patients are affected by indolent prostate cancer (CaP) despite their initial risk assessment [3,4]. Thus, more accurate stratification is needed to improve treatment and management. Zumsteg et al. suggested a sub-classification of intermediate risk-group (IRC) into favourable and unfavourable disease subsets in patients who underwent external beam radiotherapy. The favourable disease group was defined as a patient with NCCN intermediate-risk disease and all of the following: a single NCCN intermediate risk factor, Gleason ≤3 + 4 = 7 (GG: 2), and <50% of systematic biopsy cores containing cancer. All others were classified as unfavourable intermediate risk disease [5]. Then, this stratification of intermediate risk disease was validated in a cohort of patients who were treated by radical prostatectomy [6]. But the introduction of multiparametric MRI led to a change in the way of thinking in CaP diagnosis. The PRECISION trial demonstrated that the combination of mpMRI and targeted biopsies allowed to detect more significant cancer and to avoid unnecessary prostate biopsies/ subsequent treatment [7,8]. Moreover, the availability of both mpMRI and targeted biopsy increasing the preoperative information on disease characteristics, might improve accuracy in prediction of adverse pathological features on RP. Hence, these findings should be incorporated into the available models based on clinical criteria to improve stratification in the intermediate risk prostate cancer group. We aimed in this article to assess the added value of prebiopsy mpMRI and targeted biopsy (TB) features for predicting final pathology in intermediate risk CaP and improving the systematic biopsy (SB) based stratification. 2. Materials and methods 2.1. Study population After institutional review board approval, in a time frame between January 2014 and January 2019, we collected data from patients who underwent RP for pathologically biopsy-proven prostate cancer after a prebiopsy

positive mpMRI (PI-RADS ≥ 3) followed by concomitant SB and TB. Then, we only included patients with a NCCN intermediate risk of CaP(10 300 mpMRI reading and >50 fusion biopsies). All operators were experienced in fusion biopsy procedures (same device in both centres) and only operators beyond their learning curves were considered. In centre 1, mean number of fusion TB per year and per operator was 70 compared with 97 in centre 2. The 2-centre overall volume was as follows: (i) number of MRI performed per year: 960; (ii) number of MRI-targeted biopsy per year: 480. 2.3. Analyses The baseline clinical and biological features were studied. Imaging characteristics included the number of mpMRI lesions, lesion size, and the PI-RADS score. Biopsy features were detailed: number of positive cores, total tumor length, maximal tumor length in any core, GG on SB and on TB. Radical prostatectomy specimen analyses included the TNM stage, final grade group, margin status. Firstly, patients were divided according to the validated IRC criteria which defined favourable vs. unfavourable disease (defined by a primary Gleason pattern of

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4 (GG 3), percentage of positive biopsy cores >50%, or multiple intermediate risk factors). Then, we analysed correlations between preRP features (clinical, biological, pathological, imaging, IRC) and study endpoints. The primary endpoints were the pathological upgrading and upstaging rates (pT3-4 and/or pN1; GG≥3; overall unfavourable disease). Overall unfavourable disease (OUD) was defined by a pT3-4 and/or pN1 disease and/or a ≥GG 3 cancer. 2.4. Statistics Categorical variables were analyzed using chi-square test or Fisher’s exact test as appropriate and continuous variables were analyzed using Student’s t-test. The Mann-Whitney’s test was used in case the distribution was not normal. The area under the curve (AUC) of the receiver operating characteristic curve of the models was calculated. Binary logistic regression models were used for multivariable analyses. We developed a multivariable model including the standard IRC (which took into account PSA, clinical stage, GG on SB, and the percentage of SB positive cores), ECE on mpMRI, GG on TB, and PIRADS score. The limit of statistical significance was defined as P < 0.05. The SPSS 22.0 (Chicago, Illinois) software was used for analysis. 3. Results 3.1. Overall patient characteristics Overall, 377 prebiopsy MRI-positive patients treated by radical prostatectomy for an intermediate prostate cancer risk disease and harbouring complete data regarding both preoperative and postoperative radical prostatectomy outcomes were included. Baseline characteristics of overall patients are listed in Table 1. 3.2. IRC validation in cohort of CaP diagnosed with pre biopsy mpMRI and targeted/systematic biopsies According to the IRC, the distribution between favourable and unfavourable IRC disease was 39.5% (149 patients) and 60.5% (228 patients) respectively. In univariate analysis, total PSA, PSA density, and proportion of T2 clinical stage were positively associated with unfavourable IRC disease (Table 2). Besides, concerning the mpMRI outcomes, patients with unfavourable IRC disease had higher PI-RADS scores, higher mpMRI target size and more mpMRI lesions than the favourable group. The proportion of extracapsular extension (ECE) on mpMRI increased from 7.4% to 15.8% when comparing favourable to unfavourable IRC disease respectively. Regarding prostate biopsy features, all parameters (number of positive cores, total tumour length, maximum cancer involvement per core) were significantly higher in unfavourable IRC group as compared with the favourable one. The proportion of GG ≥ 3 was also higher in the unfavourable IRC group. These results were confirmed in the final pathology on RP

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Table 1 Baseline clinical, biological, imaging, and biopsy characteristics of the overall population. Overall n = 377 Age (years): Mean Median (Range) PSA (ng/ml): Mean Median (Range) PSAD: Mean Median (Range) T2 clinical stage (%) Prostate volume (ml): Mean Median (Range) IRC by: GG only PSA only Clinical stage only GG and PSA Clinical stage and PSA Clinical stage, PSA and GG 1 factor 2 factors 3 factors PSA No of cores: Mean Median (Range) No of positive cores: Mean Median (Range) Total tumor length (mm): Mean Median (Range) MRI lesions: PIRADS 3 PIRADS 4 PIRADS 5 Number of MRI lesions: 1 2 3-4 MRI lesions: Mean Median (Range) MRI lesions size (mm): Mean Median (Range) GG on Biopsy: 1 2 3 Maximal tumor length in any core (mm): Mean Median (Range) Extraprostatic extension on MRI (%) Unfavourable IRC (%) Pathological T stage: pT2 pT3a

64.8 65.5 (46−78) 8.4 7.7 (0.7−19) 0.20 0.17 (0.04−0.82) 126 (33.4%) 47.5 42.0 (15−179) 265 13 0 61 3 35 278 64 35

14.5 14.0 (12−20) 5.8 5.0 (1−14) 28.3 21.0 (1−315) 72 198 107 275 79 23 1.35 1.0 (1−5) 12.1 10.0 (4−33) 16 (4.2%) 260 (69.0%) 101 (26.8%) 7.8 8.0 (1−20) 47 (12.5%) 228 (60.5%) 193 (51.2%) 135 (35.8%) (continued)

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4 Table 1 (Continued)

Overall n = 377 pT3b-4 Pathological N1 stage Pathological grade group: 1 2 3-5 Positive surgical margins

Table 2 Univariate association between baseline characteristics in 2 group of intermediate risk stratification.

49 (13.0%) 22 (5.8%) 10 (2.7%) 220 (58.4%) 135 (35.8%) 67 (17.8%)

IRC = intermediate risk classification.

specimens with unfavourable IRC disease patients more likely harbouring aggressive and locally advanced disease according to pathological stage, involvement of lymph node and biochemical recurrence. 3.3. Added value of mpMRI and Targeted biopsy for the prediction of adverse RP features Then, we aimed to identify the criteria associated with the 3 different adverse outcomes: pT3-4 and/or pN1, GG ≥3 and OUD (Table 3). In univariate analysis, the total tumour lengths, the maximum tumour length per core, and ECE on mpMRI, were significantly correlated with the risk of pT3-4 and/or pN1 stage, pathological GG ≥3 and OUD. The size of the mpMRI lesion was a predictive factor of pT3-4 and/or pN1 stage and OUD. The PI-RADS score, GG on both SB and TB were associated with a final GG≥3 and OUD. Finally, T2 clinical stage was only associated with OUD disease. We developed a multivariable model including the standard IRC (which took into account PSA, clinical stage, GG on SB, and the percentage of SB positive cores), ECE on mpMRI, GG on TB and PIRADS score. In multivariable analysis regression, ECE on mpMRI was an independent predictive factor for a pT3-4 and/or pN1 stage ((HR = 2.95 95%IC [1.35−6.44] P = 0.007) and OUD disease (HR = 2.7; 95%IC [1.09−6.84] P = 0.032). The GG ≥ 3 on TB was predictive criteria of final GG ≥ 3 (HR = 5.44 95%IC [3.02−9.8]; P < 0.001) and OUD (HR = 2.41 95%IC [1.23−4.69]; P = 0.01) (Table 4). Furthermore, standard IRC failed to predict GG ≥ 3, pT3-4 and/or pN1 disease and OUD. Finally, we developed an easy-to-use model, only based on ECE on MRI and GG ≥ 3 on TB. The favourable group was defined as a patient with the absence of ECE on MRI and GG < 3 on TB. All others (any ECE on MRI and/or GG≥3 on TB) were classified as unfavourable disease. Fig. 1 shows that by this new MRI- and TB-based IRC, the proportion of unfavourable IRC cases decreased from 62.3% to 34.1% with an increasing proportion of detected pT3-4 and/or N1 (51.2% vs. 71.7%), final GG ≥ 3 (26.3% vs. 62.3%) and OUD (73.7% vs. 84.9%) in the unfavorable group. No increased risk of OUD, pT3-4 and/or N1, or final

Age (years) PSA (ng/ml) PSAD (ng/ml/gr) T2 clinical stage (%) Prostate volume (ml) No of cores (nb) No of positive cores (nb) Total tumor length (mm) MRI lesions (%): PIRAS 3 PIRADS 4 PIRADS 5 MRI lesions (nb) MRI lesions size (mm) GG on Biopsy (%): 1 2 3 Maximal tumor length in any core (mm) Extraprostatic extension on MRI (%) Pathological T stage (%): pT2 pT3a pT3b-4 Pathological N1 stage(%): Pathological GG (%): 1 2 3-5 Positive Surgical Margins (%): OUD (%): PSA failure (%):

P value

Favourable IRC n = 149

Unfavourable IRC n = 228

0.181 <0.001 <0.001 0.004 0.589 0.214 <0.001 <0.001 <0.001

64.2 7.2 0.17 24.8% 11.0 14.9 3.8 16.1

65.2 9.2 0.23 39.0% 12.8 14.5 7.1 36.3

30.2% 53.0% 16.8% 1.27 11.0

11.8% 52.2% 36.0% 1.41 12.8

<0.001

8.7% 91.3% 0 6.4

1.3% 54.4% 44.3% 8.7

0.016

7.4%

15.8%

63.1% 30.2% 6.7% 2.0%

43.4% 39.5% 17.1% 8.3%

4.0% 64.4% 29.6% 14.1% 66.4% 4.0%

1.8% 54.4% 41.7% 20.2% 73.7% 11.4%

0.039 0.003 <0.001

<0.001

<0.001 0.136

0.131 0.130 0.012

GG ≥3 was reported in the new expanded favorable IRC group. Thus, with the new model of IRC based on MRI and TB, the performance of discrimination of OUD, final GG ≥ 3, and pT3N1 increased from 0.55 to 0.66, 0.56 to 0.67 and 0.55 to 0.60 respectively. 4. Discussion Patients classified in the intermediate risk group of prostate cancer are considered harboring heterogenous cancers, and the prognosis of the disease can be stratified into favorable and unfavorable groups. Since the IRC description by Zumsteg et al, this stratification was based on total PSA, Gleason score, DRE and the percentage of positive biopsies [5]. Overall, 45% of patients in the Zumsteg’s article had a clinically palpable disease compared with only one third in our cohort [5]. But recent changes in the diagnostic pathway for clinically localized prostate cancer with the emerging mpMRI and TB might preclude this applicability to current patients. Indeed, the quantity and quality of

ARTICLE IN PRESS M. Roumiguie et al. / Urologic Oncology: Seminars and Original Investigations 00 (2019) 1−7 Table 3 Univariate association between baseline characteristics and pathological outcomes in radical prostatectomy specimens: final grade group (GG), risk of non-organ confined disease (pT3-4 and/or pN1), overall unfavourable disease (OUD). pT3-4 and/or pN1 Age PSA PSAD T2 clinical stage Prostate volume No of positive cores Percent of positive cores >50% Total tumor length MRI lesions MRI lesions size PIRADS GG on biopsy GG on TB Maximal tumor length in any core Extraprostatic extension on MRI

GG 3-5

OUD

0.332 0.078 0.054 0.131 0.584 0.130 0.110

0.169 0.115 0.106 0.078 0.302 0.279 0.734

0.911 0.102 0.080 0.010 0.457 0.727 0.800

0.002 0.145 0.025 0.097 0.192 0.141 0.016

0.010 0.237 0.055 0.005 <0.001 <0.001 0.021

0.029 0.220 0.046 0.037 0.015 0.003 0.012

0.001

0.033

0.008

information for preoperative risk stratification substantially differ between men diagnosed via mpMRI targeted biopsy and those undergoing systematic biopsy alone [10]. To our knowledge, this study was the largest including patients who were diagnosed with an intermediate risk of prostate cancer from prebiopsy mpMRI and targeted biopsy. Our results show that the available IRC prediction of adverse pathological outcomes is suboptimal for the classification of men who undergo RP after mpMRI-targeted biopsy.

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Taking into consideration ECE on mpMRI and GG on TB improved the prediction of pathologically confirmed unfavourable disease. Our simple model brings up new elements compared to the previous IRC. First, while previous nomograms included T stage determined via DRE, adding information on the presence of ECE or SVI as assessed via mpMRI improved the AUC of our new stratification. The role of mpMRI in CaP staging is still a debatable question. On one hand, a recent meta-analysis demonstrated that mpMRI has a poor sensitivity for ECE, SVI, and overall stage T3 with respective sensitivities of 57, 58, and 61% [11]. On the other hand, authors assessed the added value of mpMRI data to clinical parameters to predict adverse pathological outcomes. In a recent review Dell’oglio et al. summarized the studies that attempt to outperform the prediction of clinicals parameters after added mpMRI results [10]. For example, Feng et al. showed the added value of mpMRI result (ECE positive or negative) in prior existing clinical-based models (Partin Table and Memorial Sloan Kettering (MSK) nomogram) to predict pathological ECE [12]. In the same study design, a comparison between the accuracy of both CAPRA score and the Partin table with or without mpMRI results to predict adverse features on RP, confirmed that mpMRI was the strongest factor associated with ECE, SVI and lymph node involvement outcomes [13]. In addition, while previous studies showed that adding systematic core biopsies at the time of mpMRI- targeted biopsy leads to detect more significant CaP, we showed that the GG on targeted biopsy strongly outperformed SB-based features to predict adverse features on radical prostatectomy [14]. Indeed, several studies reported a higher concordance

Table 4 Multivariable logistic regression between baseline characteristics and pathological outcomes in radical prostatectomy specimens: final grade group, risk of non-organ confined disease (pT3-4 and/or pN1), overall unfavourable disease (OUD). OUDa

Grade group 3 P IRC: Favorable Unfavorable PIRADS: 3–4 5 T3 MRI: No Yes GG3on TB: No Yes

OR (95% CI)

0.255

P

0.743 Ref 0.75 (0.43–1.24)

0.163

<0.001 Ref 5.44 (3.02–9.80)

OR (95% CI)

0.617

0.165

Ref 1.74 (0.87–3.46)

P

Ref 0.92 (0.55–1.55)

Ref 1.44 (0.86–2.42) 0.115

OR (95% CI)

pT3 and or pN1 disease

Ref 1.13 (0.70–1.83) 0.317

Ref 1.51 (0.85–2.66)

Ref 1.30 (0.78–1.05)

0.032

Ref 2.73 (1.09–6.84)

0.007

0.010

Ref 2.41 (1.23–4.69)

0.430

Ref 2.95 (1.35–6.44)

Ref 1.26 (0.71–2.21)

Variables included within the model: standard IRC (which included PSA, clinical stage, GG on SB, and the percentage of SB positive cores), ECE on mpMRI, GG on TB and PIRADS score. a Overall unfavourable disease, defined by Grade Group 3 and/or pT3-4 and/or pN1 disease.IRC = intermediate risk classification.

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Fig. 1. Stratification of intermediate risk of disease comparing the previous model (based in systematic biopsies) and the new one integrating ECE-mpMRI and Gleason Grade on targeted biopsy. TB = targeted biopsy; GG = grade group; RP = radical prostatectomy; OUD = overall unfavourable disease.

on GG between the targeted core and RP specimen [15−17]. Borkowetz et al. compared the prostate and histological findings of both targeted mpMRI- prostate biopsy and systematic biopsy with final histology of the radical prostatectomy specimen, and their results showed that fusion biopsy on a suspicious mpMRI lesion was associated with a better prediction of final Gleason Grade and pathology stage [18]. More recently, Raskolnikov et al. reported the association of GG between both targeted biopsy and final pathology in men who have a negative mpMRI for ECE. This finding emphasized the independent role of targeted biopsy to predict adverse RP outcome not directly visible by mpMRI [19]. In a recent publication, Gandaglia et al. reported that available models predicting LNI are characterized by suboptimal accuracy and there exists a clinical net benefit for patients diagnosed via mpMRI-targeted biopsies. They built a novel predictive tool specifically focused on men undergoing mpMRI targeted biopsies and concomitant systematic biopsies to identify patients harbouring high risk of LNI who should be considered for Eplnd [8]. To summarise, the introduction of mpMRI and TB in prostate cancer diagnosis undoubtedly improves prognosis assessment. Until this study, to our knowledge, no preoperative risk tool combining both imaging results and TB findings has been published aiming to improve the stratification in intermediate risk CaP. Our easy-to-use model, based on 2 variables (ECE on MRI and GG on TB), outperformed the standard IRC in the subgroup of MRI-positive patients. Interestingly, this new model better discriminated patients harbouring unfavorable disease and reclassified half of them in the favorable group. This could lead to a clinically relevant decrease in terms of over-treatment and to an improvement in neo-adjuvant and/or adjuvant strategies decision-making.

Several limitations have to be emphasized. Our study cohort does not investigate the clinical data of long-term follow-up that shows if the new stratification of intermediate risk disease was associated with strong oncological outcomes in terms of biochemical recurrence, overall, and specific survival. However, the criteria of adverse pathologic features chosen in our method (pT3/N1 and GG ≥3 and OUD) were defined in several publications as the strongest predictive factors of long-term oncological outcomes. Then, our cohort does not include all intermediate risk CaP patients at initial diagnosis, as some of them were preferentially treated by radiotherapy, brachytherapy, or active surveillance. We also did not include MRI-negative patients who could represent up to one fourth of newly diagnosed CaP cases [7]. The impact of mpMRI and mpMRI-TB on risk assessment depends on the precision of targeting. Several factors associated with the improvement of this imaging-based strategy have been highlighted: experience for both mpMRI reading and lesion targeting, along with decreasing software errors. We believe that these sources of imagingguided strategy failure are moderate in our study. Indeed, all radiologists and biopsy operators involved in the study were experienced beyond their learning curves since the beginning of the study period. In the 2 institutions, we used the same fusion computer-assisted software which reduced interpretation bias and centre effects. Recently, we have demonstrated that this elastic registration system improved the precision of targeting by 3 mm and increased the clinically significant CaP detection rate, as compared with cognitive fusion [20]. Moreover, the analyses stratified by the centre did not lead to significantly different results. In conclusion, we found that prebiopsy multiparametric MRI with PIRADS 2.0 and TB provides useful additional

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information to standard intermediate risk stratification based only on clinico-biological and SB-based parameters. The added value of mpMRI staging and grade group on targeted biopsy can increase the accuracy of the prior model in prediction of pathologically confirmed adverse features. This in turn, drastically improves the performance of the risk classification and re-classifies half of cancers initially defined as unfavorable with the standard IRC in the newly defined favorable group. This study could lead to a clinically relevant decrease in terms of over-treatment and to a better discrimination of the unfavorable risk group which could benefit the most from aggressive therapy such as neo-adjuvant and/or adjuvant treatment. In the future, these findings have to be validated in an external cohort and extended to cover long term oncological outcomes. Conflict of interest None. References [1] D’Amico AV, Whittington R, Malkowicz SB, Schultz D, Blank K, Broderick GA, et al. Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. JAMA 1998;280(11): 969–74. [2] Mottet N, Bellmunt J, Bolla M, Briers E, Cumberbatch MG, De Santis M, et al. EAU-ESTRO-SIOG Guidelines on prostate cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 2017;71(4):618–29. [3] Ploussard G, Isbarn H, Briganti A, Sooriakumaran P, Surcel CI, Salomon L, et al. Can we expand active surveillance criteria to include biopsy Gleason 3+4 prostate cancer? A multi-institutional study of 2,323 patients. Urol Oncol 2015;33(2):71 e1-9. [4] Abern MR, Aronson WJ, Terris MK, Kane CJ, Presti JC Jr., Amling CL, et al. Delayed radical prostatectomy for intermediate-risk prostate cancer is associated with biochemical recurrence: Possible implications for active surveillance from the SEARCH database. Prostate 2013;73(4):409–17. [5] Zumsteg ZS, Spratt DE, Pei I, Zhang Z, Yamada Y, Kollmeier M, et al. A new risk classification system for therapeutic decision making with intermediate-risk prostate cancer patients undergoing doseescalated external-beam radiation therapy. Eur Urol 2013;64(6): 895–902. [6] Beauval JB, Ploussard G, Cabarrou B, Roumiguie M, Ouzzane A, Gas J, et al. Improved decision making in intermediate-risk prostate cancer: a multicenter study on pathologic and oncologic outcomes after radical prostatectomy. World J Urol 2017;35(8):1191–7.

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