The management of active surveillance in prostate cancer: validation of the Canary Prostate Active Surveillance Study risk calculator with the Spanish Urological Association Registry

Ángel Borque-Fernando, José Rubio-Briones, Luis Mariano Esteban, Argimiro Collado-Serra, Yoni Pallás-Costa, Pedro Ángel López-González, Jorge Huguet-Pérez, José Ignacio Sanz-Vélez, Jesús Manuel Gil-Fabra, Enrique Gómez-Gómez, Cristina Quicios-Dorado, Lluis Fumadó, Sara Martínez-Breijo, Juan Soto-Villalba, Ángel Borque-Fernando, José Rubio-Briones, Luis Mariano Esteban, Argimiro Collado-Serra, Yoni Pallás-Costa, Pedro Ángel López-González, Jorge Huguet-Pérez, José Ignacio Sanz-Vélez, Jesús Manuel Gil-Fabra, Enrique Gómez-Gómez, Cristina Quicios-Dorado, Lluis Fumadó, Sara Martínez-Breijo, Juan Soto-Villalba

Abstract

The follow up of patients on active surveillance requires to repeat prostate biopsies. Predictive models that identify patients at low risk of progression or reclassification are essential to reduce the number of unnecessary biopsies. The aim of this study is to validate the Prostate Active Surveillance Study risk calculator (PASS-RC) in the multicentric Spanish Urological Association Registry of patients on active surveillance (AS), from common clinical practice.

Results: We find significant differences in age, PSA and clinical stage between our validation cohort and the PASS-RC generation cohort (p < .0001), with a reclassification rate of 10-22% on the follow-up Bx, no cancer was found in 43% of the first follow-up Bx. The calibration curve shows underestimation of real appearance of reclassification. The AUC is 0.65 (C.I.95%: 0.60-0.71). PDF and CUC do not suggest a specific cut-off point of clinical use.

Methods: We select 498 patients on AS with a minimum of one follow-up biopsy (Bx) from the 1,024 males registered by 36 Spanish centers recruiting patients on the Spanish Urological Association Registry on AS. PASS-RC external validation is carried by means of calibration curve and area under de ROC-curve (AUC), identifying cut-offs of clinical utility by probability density functions (PDF) and clinical utility curves (CUC).

Conclusions: In our first external validation of the PASS-RC we have obtained a moderate discrimination ability, although we cannot recommend cut-off points of clinical use. We suggest the exploration of new biomarkers and/or morpho-functional parameters from multiparametric magnetic resonance image, to improve those necessary tools on AS.

Keywords: active surveillance; external validation; prostate cancer; reclassification; risk calculator.

Conflict of interest statement

CONFLICTS OF INTEREST The authors declare that they have no conflict of interest.

Figures

Figure 1. Percentage of reclassification in follow-up…
Figure 1. Percentage of reclassification in follow-up biopsies
Figure 2. Calibration plot of PASS-RC validation…
Figure 2. Calibration plot of PASS-RC validation in PIEM cohort
Figure 3. ROC curve of PASS-RC validation…
Figure 3. ROC curve of PASS-RC validation in PIEM cohort
Figure 4. Probability density functions of probability…
Figure 4. Probability density functions of probability values obtained from PASS-RC in patients with/without reclassification in PIEM cohort
Figure 5. Clinical utility curve: For different…
Figure 5. Clinical utility curve: For different threshold probability points selected in X axe, it can be seen in the Y axe, on the one hand, in blue line, the percentage of biopsies not performed to patients (Saved biopsy) and, in the other hand, in red line, the percentage of patients whose progression have been not been adequately diagnosed (Undetected reclassification)
Figure 6. Decision curve analysis
Figure 6. Decision curve analysis

References

    1. Cózar JM, Miñana B, Gómez-Veiga F, Rodríguez-Antolín A, Villavicencio H, Cantalapiedra A, Pedrosa E. National prostate cancer registry 2010 in Spain. [Article in Spanish] Actas Urol Esp. 2013;37:12–9.
    1. Rubio-Briones J, Borque A, Esteban LM, Iborra I, López PA, Gil JM, Pallás Y, Fumadó L, Martínez-Breijo S, Chantada V, Gómez E, Quicios C, Congregado CB, et al. Preliminary results of the Spanish Association of Urology National Registry in Active Surveillance for prostate cancer. Actas Urol Esp. 2016;40:3–10.
    1. Newcomb LF, Thompson IM, Jr, Boyer HD, Brooks JD, Carroll PR, Cooperberg MR, Dash A, Ellis WJ, Fazli L, Feng Z, Gleave ME, Kunju P, Lance RS, et al. Outcomes of Active Surveillance for Clinically Localized Prostate Cancer in the Prospective, Multi-Institutional Canary PASS Cohort. J Urol. 2016;195:313–20.
    1. Klotz L, Zhang L, Lam A, Nam R, Mamedov A, Loblaw A. Clinical results of long-term follow-up of a large, active surveillance cohort with localized prostate cancer. J Clin Oncol. 2010;28:126–31.
    1. DallʼEra MA, Konety BR, Cowan JE, Shinohara K, Stauf F, Cooperberg MR, Meng MV, Kane CJ, Perez N, Master VA, Carroll PR. Active surveillance for the management of prostate cancer in a contemporary cohort. Cancer. 2008;112:2664–70.
    1. van den Bergh RC, Roemeling S, Roobol MJ, Aus G, Hugosson J, Rannikko AS, Tammela TL, Bangma CH, Schröder FH. Outcomes of men with screen-detected prostate cancer eligible for active surveillance who were managed expectantly. Eur Urol. 2009;55:1–8.
    1. Soloway MS, Soloway CT, Eldefrawy A, Acosta K, Kava B, Manoharan M. Careful selection and close monitoring of low-risk prostate cancer patients on active surveillance minimizes the need for treatment. Eur Urol. 2010;58:831–5.
    1. Tosoian JJ, Trock BJ, Landis P, Feng Z, Epstein JI, Partin AW, Walsh PC, Carter HB. Active surveillance program for prostate cancer: an update of the Johns Hopkins experience. J Clin Oncol. 2011;29:2185–90.
    1. Adamy A, Yee DS, Matsushita K, Maschino A, Cronin A, Vickers A, Guillonneau B, Scardino PT, Eastham JA. Role of prostate specific antigen and immediate confirmatory biopsy in predicting progression during active surveillance for low risk prostate cancer. J Urol. 2011;185:477–82.
    1. van As NJ, Norman AR, Thomas K, Khoo VS, Thompson A, Huddart RA, Horwich A, Dearnaley DP, Parker CC. Predicting the probability of deferred radical treatment for localised prostate cancer managed by active surveillance. Eur Urol. 2008;54:1297–305.
    1. Cussans A, Somani BK, Basarab A, Dudderidge T. The role of targeted prophylactic antimicrobial therapy prior to transrectal ultrasound (TRUS) guided prostate biopsy in reducing infection rates: a systematic review. BJU Int. 2016;117:725–31.
    1. Miocinovic R, Jones JS, Pujara AC, Klein EA, Stephenson AJ. Acceptance and durability of surveillance as a management choice in men with screen-detected, low-risk prostate cancer: improved outcomes with stringent enrollment criteria. Urology. 2011;77:980–4.
    1. Ayres BE, Montgomery BS, Barber NJ, Pereira N, Langley SE, Denham P, Bott SR. The role of transperineal template prostate biopsies in restaging men with prostate cancer managed by active surveillance. BJU Int. 2012;109:1170–6.
    1. Available in: .
    1. Ankerst DP, Xia J, Thompson IM, Jr, Hoefler J, Newcomb LF, Brooks JD, Carroll PR, Ellis WJ, Gleave ME, Lance RS, Nelson PS, Wagner AA, Wei JT, et al. Precision Medicine in Active Surveillance for Prostate Cancer: Development of the Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator. Eur Urol. 2015;68:1083–8.
    1. Goltz D, Holmes EE, Gevensleben H, Sailer V, Dietrich J, Jung M, Röhler M, Meller S, Ellinger J, Kristiansen G, Dietrich D. CXCL12 promoter methylation and PD-L1 expression as prognostic biomarkers in prostate cancer patients. Oncotarget. 2016;7:53309–53320. .
    1. Hansen AF, Sandsmark E, Rye MB, Wright AJ, Bertilsson H, Richardsen E, Viset T, Bofin AM, Angelsen A, Selnaes KM, Bathen TF, Tessem MB. Presence of TMPRSS2-ERG is associated with alterations of the metabolic profile in human prostate cancer. Oncotarget. 2016;7:42071–42085. .
    1. Ferro M, Lucarelli G, Bruzzese D, Di Lorenzo G, Perdonà S, Autorino R, Cantiello F, La Rocca R, Busetto GM, Cimmino A, Buonerba C, Battaglia M, Damiano R, et al. Low serum total testosterone level as a predictor of upstaging and upgrading in low-risk prostate cancer patients meeting the inclusion criteria for active surveillance. Oncotarget. 2017;8:18424–18434. .
    1. Ferro M, Ungaro P, Cimmino A, Lucarelli G, Busetto GM, Cantiello F, Damiano R, Terracciano D. Epigenetic Signature: A New Player as Predictor of Clinically Significant Prostate Cancer (PCa) in Patients on Active Surveillance (AS) Int J Mol Sci. 2017;18:1146.
    1. Cantiello F, Russo GI, Cicione A, Ferro M, Cimino S, Favilla V, Perdonà S, De Cobelli O, Magno C, Morgia G, Damiano R. PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance. World J Urol. 2016;34:485–493.
    1. de Cobelli O, Terracciano D, Tagliabue E, Raimondi S, Bottero D, Cioffi A, Jereczek-Fossa B, Petralia G, Cordima G, Almeida GL, Lucarelli G, Buonerba C, Matei DV, et al. Predicting pathological features at radical prostatectomy in patients with prostate cancer eligible for active surveillance by multiparametric magnetic resonance imaging. PLoS One. 2015;10:e0139696.
    1. Klotz L, Vesprini D, Sethukavalan P, Jethava V, Zhang L, Jain S, Yamamoto T, Mamedov A, Loblaw A. Long-term follow-up of a large active surveillance cohort of patients with prostate cancer. J Clin Oncol. 2015;33:272–7.
    1. Rubio-Briones J, Borque Fernando Á. Responses and advisability of active surveillance in prostate cancer (in response to editorial comments by Dr. Sánchez Badajoz) Actas Urol Esp. 2016;40:72–74.
    1. Sánchez-de-Badajoz E. The inconveniences of active surveillance in prostate cancer. Actas Urol Esp. 2016;40:69–71.
    1. Tosoian JJ, Mamawala M, Epstein JI, Landis P, Wolf S, Trock BJ, Carter HB. Intermediate and Longer-Term Outcomes From a Prospective Active-Surveillance Program for Favorable-Risk Prostate Cancer. J Clin Oncol. 2015;33:3379–85.
    1. Lane JA, Donovan JL, Davis M, Walsh E, Dedman D, Down L, Turner EL, Mason MD, Metcalfe C, Peters TJ, Martin RM, Neal DE, Hamdy FC, ProtecT study group Active monitoring, radical prostatectomy, or radiotherapy for localised prostate cancer: study design and diagnostic and baseline results of the ProtecT randomised phase 3 trial. Lancet Oncol. 2014;15:1109–18.
    1. Andriole GL, Crawford ED, Grubb RL, 3rd, Buys SS, Chia D, Church TR, Fouad MN, Isaacs C, Kvale PA, Reding DJ, Weissfeld JL, Yokochi LA, OʼBrien B, et al. PLCO Project Team Prostate cancer screening in the randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: mortality results after 13 years of follow-up. J Natl Cancer Inst. 2012;104:125–32.
    1. Ross AE, Loeb S, Landis P, Partin AW, Epstein JI, Kettermann A, Feng Z, Carter HB, Walsh PC. Prostate-specific antigen kinetics during follow-up are an unreliable trigger for intervention in a prostate cancer surveillance program. J Clin Oncol. 2010;28:2810–6.
    1. Bul M, Zhu X, Valdagni R, Pickles T, Kakehi Y, Rannikko A, Bjartell A, van der Schoot DK, Cornel EB, Conti GN, Boevé ER, Staerman F, Vis-Maters JJ, et al. Active surveillance for low-risk prostate cancer worldwide: the PRIAS study. Eur Urol. 2013;63:597–603.
    1. Borque-Fernando Á, Esteban-Escaño LM, Rubio-Briones J, Lou-Mercadé AC, García-Ruiz R, Tejero-Sánchez A, Muñoz-Rivero MV, Cabañuz-Plo T, Alfaro-Torres J, Marquina-Ibáñez IM, Hakim-Alonso S, Mejía-Urbáez E, Gil-Fabra J, et al. A Preliminary Study of the Ability of the 4Kscore test, the Prostate Cancer Prevention Trial-Risk Calculator and the European Research Screening Prostate-Risk Calculator for Predicting High-Grade Prostate Cancer. Actas Urol Esp. 2016;40:155–163.
    1. Borque A, Rubio-Briones J, Esteban LM, Sanz G, Domínguez-Escrig J, Ramírez-Backhaus M, Calatrava A, Solsona E. Implementing the use of nomograms by choosing threshold points in predictive models: 2012 updated Partin Tables vs a European predictive nomogram for organ-confined disease in prostate cancer. BJU Int. 2014;113:878–86.
    1. Prostate Cancer Prevention Trial-Risk Calculator 2.0 [visited August 6, 2017] Available in: .
    1. European Research Screening Prostate Cancer [visited August 6, 2017] Available in:
    1. 4Kscore Test - HUMS pilot study [visited August 6, 2017] Available in:
    1. Hansen J, Auprich M, Ahyai SA, de la Taille A, van Poppel H, Marberger M, Stenzl A, Mulders PF, Huland H, Fisch M, Abbou CC, Schalken JA, Fradet Y, et al. Initial prostate biopsy: development and internal validation of a biopsy-specific nomogram based on the prostate cancer antigen 3 assay. Eur Urol. 2013;63:201–9.
    1. Rubio-Briones J, Borque A, Esteban LM, Casanova J, Fernandez-Serra A, Rubio L, Casanova-Salas I, Sanz G, Domínguez-Escrig J, Collado A, Gómez-Ferrer A, Iborra I, Ramírez-Backhaus M, et al. Optimizing the clinical utility of PCA3 to diagnose prostate cancer in initial prostate biopsy. BMC Cancer. 2015;15:633.
    1. Schoots IG, Petrides N, Giganti F, Bokhorst LP, Rannikko A, Klotz L, Villers A, Hugosson J, Moore CM. Magnetic resonance imaging in active surveillance of prostate cancer: a systematic review. Eur Urol. 2015;67:627–36.
    1. Silverman B. Density Estimation for Statistics and data Analysis. Monogr Stat Appl Probab. 1986;26:1–22.
    1. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.
    1. de Cobelli O, Terracciano D, Tagliabue E, Raimondi S, Galasso G, Cioffi A, Cordima G, Musi G, Damiano R, Cantiello F, Detti S, Matei DV, Bottero D, et al. Body mass index was associated with upstaging and upgrading in patients with low-risk prostate cancer who met the inclusion criteria for active surveillance. Urologic Oncology: Seminars and Original Investigations. 2015;33:201.e1–201.e8.
    1. Russo GI, Cimino S, Castelli T, Favilla V, Urzì D, Veroux M, Madonia M, Morgia G. Percentage of cancer involvement in positive cores can predict unfavorable disease in men with low-risk prostate cancer but eligible for the prostate cancer international: active surveillance criteria. Urologic Oncology: Seminars and Original Investigations. 2014;32:291–296.
    1. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–845.
    1. R Core Team . R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2016.

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