Defining prostate cancer risk after radical prostatectomy

Defining prostate cancer risk after radical prostatectomy

Available online at ScienceDirect EJSO 40 (2014) 496e504 Review Defining prostate cancer risk after radical pro...

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ScienceDirect EJSO 40 (2014) 496e504


Defining prostate cancer risk after radical prostatectomy S. Adamis*, I.M. Varkarakis 2nd Department of Urology, University of Athens, Medical School, Sismanoglion Hospital, Athens, Greece Accepted 2 February 2014 Available online 20 February 2014

Abstract Prostate cancer encompasses a wide spectrum of tumor phenotypes with differing prognoses and a part of these patients are at risk of experiencing tumor recurrence after initial treatment. This review discusses the parameters that determine PCa risk for failure after radical prostatectomy and also focuses on the ability of currently available post-treatment nomograms to predict treatment outcomes, and probability of treatment failure. The use of predictive nomograms may be therefore helpful in the complex decision making process. Ó 2014 Elsevier Ltd. All rights reserved. Keywords: Prostate cancer; Radical prostatectomy; Adverse features; Predictive nomograms

Introduction Prostate cancer (PCA) is the most common solid neoplasm in Western countries, outnumbering lung and colorectal cancer.1 Each year 500.000 new patients will be diagnosed with this disease2 and PCa is currently still the second most common cause of cancer death in men,3 despite the fact that many articles in the news suggest that prostate cancer is an indolent disease not deserving treatment. During the last 2 decades screening with PSA has allowed detection of PCA at earlier stages, improving patient survival and the chances of cure with definitive local therapy. The rate of clinically advanced disease was reduced from 41% in the 80’s to <9% of all newly diagnosed cancers in the 90s.4,5 Despite this favorable stage migration which has led to significant improvement of cancer specific survival (CSS), 15% of patients still present with high-risk for failure tumors.6 Such tumors are either advanced locally or advanced distally and are usually of higher grade. The majority of such patients have a significantly worse 10year CSS; however many of them do not follow the rule

* Corresponding author. Dimitras Str. 5, 15236 P. Penteli, Athens, Greece. Tel.: þ30 6945330538; fax: þ30 2108044703. E-mail addresses: [email protected], [email protected] (S. Adamis). 0748-7983/$ - see front matter Ó 2014 Elsevier Ltd. All rights reserved.

and present with a better than expected prognosis.7 Knowing which patients with aggressive disease will fare better may spare them from the morbidity associated with adjuvant therapy. On the other hand anticipating local failure after treatment may allow for early institution of secondary treatment modalities leading to a potential survival benefit. This could also be true for patients with early stage tumors since it is known that 15e40% of them will still go on to develop progressive disease, despite adequate local treatment.8 Inaccurate risk characterization could result in an inappropriate management, such as indiscriminate application of hormonal or other adjuvant therapeutic modality as well as excluding certain patients from potentially curative local treatment.9 Therefore prognostic predictors after initial management are needed in order to appropriately guide patients to best outcomes with least morbidity. In this review factors predicting cancer at high risk for failure after radical prostatectomy (RP) will be presented. Defining risk for failure after radical prostatectomy Radical prostatectomy (RP) is an effective treatment for patients with organ-confined disease and has demonstrated to reduce the risk of death from the disease.10 Approximately 40% of the patients who choose definitive therapy will undergo RP. One of its advantages is the possibility

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of accurate local staging since the prostate is removed and analyzed. Advanced disease with potentially bad prognosis is detected at RP specimens in 38e52% of patients.11 Each stratum of extraprostatic disease is associated with a significantly increased risk of cancer recurrence and progression, measured at the earliest time with a detectable PSA, also known as biochemical recurrence (BCR).12 The natural history of PCA with BCR after RP can be variable; however approximately two thirds of these patients will develop metastatic disease if left untreated and most will die of PCA.13 The most comprehensive study of the natural history of PCA with BCR was conducted in a series of 1997 men who underwent RP.14 In this cohort BCR occurred in 15% of these men and time from RP to BCR averaged 3.5 years. The 5-year risk of clinical progression ranged from 27 to 60%. Median time from BCF to clinical progression was 8 years. 17% of men with BCR died from prostate cancer. In most cases BCF is used as a surrogate for more clinically meaningful endpoints such as metastatic progression or cancer-specific mortality. Pathologic high-risk features for failure after RP  Gleason Score of the RP specimen Tumor grading is a way of measuring histological differentiation, which frequently parallels tumor aggressiveness. It is widely accepted that Gleason score (GS) is one of the most powerful predictors of PCA progression and survival and one of the most influential factors used to determine treatment for PCA.15 Gleason scores 8e10 are invariably correlated with disease progression and are considered bad prognosticators. There is an increasing volume of evidence that small volumes of tertiary grade 5 patterns (and to a lesser extent tertiary grade 4) are associated with aggressive pathological features and a higher risk of biochemical recurrence.16,17 Recently Alenda et al.18 in a prospective trial demonstrated that the primary Gleason pattern remained statistically predictive for PSA failure (P ¼ 0.018) on multivariate analysis. The authors concluded that primary Gleason 4 pattern was an independent predictor for PSA failure. Furthermore, Epstein et al.19 demonstrated, in men with extraprostatic disease, and negative seminal vesicles and lymph nodes on RP, that high-grade tumors had a significantly higher risk of progression than lower grade tumors, indicating that extraprostatic disease alone in the absence of other adverse features (eg. serum PSA, margin status and Gleason score) does not infer high-risk for disease recurrence.  Positive surgical margins (PSM) A positive surgical margin (PSM) has been defined as cancer identified at the inked surgical resection margin of


the RP specimen. There are two types of PSM: iatrogenic and non-iatrogenic. In other words PSM can result from incision into extraprostatic cancer in patients with extracapsular extension (pT3a) or capsular incision into organconfined tumors (pT2þ).20 From an oncological point of view the presence of PSM at the RP specimen indicates theoretically inadequate cancer clearance. Retrospective reports have suggested that PSM are a risk factor for future BCF in all patients with clinically localized disease.21 Thus PSM have been associated with increased adverse outcomes in several studies and most investigators consider PSM as an independent predictor of PCA recurrence after RP.20,22 However some other studies have contested these findings and have not found PSM to be an independent predictor of disease recurrence and progression.23 Furthermore data indicate that when adverse pathologic features such as %Gleason grade 4/5, seminal vesicle invasion (SVI) or lymph node involvement (LNI) are accounted for, margin status no longer plays a role in determining clinical outcomes.24 Furthermore there are studies showing a difference in mean age and mean preoperative PSA between patients with and patients without PSM, suggesting that differences in PSM rates may depend on variables other than pathological stage.25,26 Alkhateeb et al.26 found that pretreatment PSA and pathological Gleason score are related to PSM rates, and that when patients were stratified into 3 disease risk groups using the D0 Amico classification the PSM rates in patients with low risk disease were significantly lower than those with intermediate or high risk disease (12.3% vs 21.8% and 34.5%, respectively, p < 0.001). Thus the effect of margin status on disease recurrence in patients after RP remains controversial and the debate over whether PSM represent unfavorable tumor biology, technical error, or both is still pertinent. Several studies have investigated the prognostic significance of the site, number, and extend of PSM. In some of them the difference in the risk of recurrence between a focal or solitary PSM and an extensive or multifocal PSM has been noted,27 while in others it has been refuted.23,28 Sofer et al.27 showed that BCR was significantly more prevalent among men with multiple PSM compared to single PSM (HR: 2.19; 95% CI: 1.11e4.32) but not associated with the location of the PSM. Conversely Eastham et al.21 demonstrated that the effect on BCR was significantly influenced by the specific location of the PSM, with the posterolateral site conferring the greatest probability of relapse. Other studies reported that a solitary positive apical margin is associated with higher recurrence rates and a shorter interval to progression,29 whereas in others patients with PSM at the prostate base appeared to carry a higher risk for BCF than those with PSM elsewhere.30 Nevertheless it remains perplexing as to why a PSM at one location but not at another can predict disease recurrence. Conclusively, PSM after RP are uniformly considered an adverse oncologic outcome. However, their long-term


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impact on clinical progression and cancer-specific survival is highly variable and largely dependent on additional risk modifiers, as well as patient life expectancy and therefore their interpretation in risk assessment should be done with caution.  Extraprostatic extension Extraprostatic extension (EPE) is defined as the presence of neoplastic glands outside the prostate in the periprostatic tissue. EPE became accepted terminology at a 1996 Consensus Conference, and has replaced earlier terms such as extracapsular or extraglandular invasion, penetration, and perforation.31 Despite this, disagreement exists as to what features define extraprostatic extension. Moreover, this definition is, somewhat oversimplified as the prostate does not possess a true histological capsule and it can be challenging for pathologists to identify the boundary of the gland.32 EPE is a well-established adverse prognostic factor for prostate carcinoma, and accurate identification of this feature is required for optimal patient management after radical prostatectomy. Both extraprostatic extension (EPE) and margin status offer prognostic information. Various studies find that one may be more dominant than the other, but in most cases, it is really hard to separate the two.33 The correlation between surgical margins and EPE is largely unknown. Only a limited number of studies have evaluated the influence of EPE and surgical margins on cancer progression without the confounding factors of SVI and LNI. Nevertheless, Cheng et al.25 analyzed the correlation between surgical margin status and EPE in a large series of totally embedded, serially sectioned whole-mount prostatectomies from previously untreated men in the absence of SVI or LN metastasis. Their results indicated that there was a significant association between these two factors. Patients with both PSM and EPE appeared to have a higher progression rate than those with PSM or EPE alone. The independent predictive value of EPE is less certain than that of positive surgical margins. Nevertheless EPE has some prognostic power independent of margin status. For EPE positive disease, a 5-year PFS rate of 48%e68% has been reported.34,35  Seminal vesicle invasion (SVI) SVI is defined as invasion of the muscular wall of the seminal vesicles. SVI is associated with a poor prognosis, as these tumors usually have large volumes, they are poorly differentiated and they tend to have a greater incidence of extracapsular extension.36 Patients with SVI experience tumor recurrence almost universally after surgery.7 Nevertheless few studies investigating tumors with isolated SVI have attempted to stratify the prognosis based on the pathologic parameter of SVI alone.37 Epstein et al.38 investigated this

question in a series of 45 men with isolated SVI and longterm follow-up. They found that the prognosis of men with SVI was not predicted by differences in tumor volume, extent of SVI (focal, moderate, extensive) or bilaterality of SVI. Surgical margin status and Gleason score (Gleason score <7 vs 7) were predictive, although this did not reach statistical significance. However, Ohori et al.39 reported that surgical margin status did not affect progression in cases with positive seminal vesicles. Salomon et al.40 in a cohort of 137 patients with isolated SVI suggested that only preoperative PSA level and Gleason score of the RP specimen were independent predictors of progression and neither capsular invasion nor PSM predicted progression. The authors reported a 5-year progression-free survival rate of 33.8%, but progression-free survival rates from 5% to 60% have also been reported.41  Tumor volume (TV) The prognostic value of tumor volume (TV) in predicting BCR after RP has been debated and is still not completely defined. Larger TV has been associated with adverse pathologic features, including pathologic Gleason score, PSM, SVI, and LNI.42,43 However, the role of TV as an independent predictor of BCR remains controversial. Salomon et al.44 in a retrospective study demonstrated that in univariate analysis specimen Gleason score, pathological stage, PSM, and TV are all predictors of progression. Using multivariate analysis, however, only Gleason score and pathological stage are progression predictors after surgery, and when these parameters are known, TV does not provide novel additional prognostic information. In contrast, Rampersaud et al.45 in their series defined the association between percent tumor involvement and BCR across multiple pathological stages. They found that percent tumor involvement was a significant predictor of BCR and was able to stratify men who were already assigned to narrowly defined pathological groups. More recently, Thompson et al.46 pointed out that the controversy regarding TV is augmented by different methods of ascertainment (i.e., direct measurement of TV by planimetry vs visually estimated percent tumor involvement of the gland). The authors in their study demonstrated that TV was a significant predictor of BCR only when directly measured by planimetry and not with the percent tumor involvement estimations. These findings indicate that large TV may constitute a high-risk feature after RP, its exact role however has not been clarified yet.  Perineural invasion A pathologic finding that has been variable as a prognostic factor is that of perineural invasion (PNI). Ostensibly, it is a pathway that the cancer can follow to extend outside of the gland. In many studies that report on it, it is an observational finding.47 Other than that observation,

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no real correlation of PNI with failure was possible. When considered in conjunction with other factors such as extraprostatic extension and positive margins, it is not independently associated with recurrence. Thus, perineural invasion is not a strong independent predictor of failure.48  Lymphovascular invasion (LVI) Lymphovascular invasion (LVI) is a pathologic adverse feature after RP that is defined as the unequivocal presence of tumor cells within a vascular or lymphatic endotheliallined space. LVI has been reported in 5%e53% of specimens after RP.49 LVI has been associated with other unfavorable pathologic features, such as higher Gleason score of the specimen, increased pathologic T stage as well as PSM and SVI.50,51 Its independent role in disease recurrence is controversial. It has been reported, that LVI is associated significantly with higher rates of disease progression after RP.50,52 De Taille et al.53 reported of a 30% BCR-free survival rate of patients with microvascular invasion vs 92% of patients without microvascular invasion. Ouden et al.54 suggested that LVI was a significant prognostic factor for biochemical progression, clinical progression, local recurrence, distant metastasis and overall survival. There are studies suggesting an independent, significant association of LVI with disease progression on multivariate analysis.52e55 However other studies have found that LVI did not retain significance on multivariate analysis when adjusting for other variables, such as preoperative PSA, lymph node metastasis and Gleason score.50 Recently Yee et al.56 reported of an association between LVI and higher preoperative PSA levels and Gleason scores and a greater likelihood of extraprostatic extension, SVI, PSM, and lymph node metastasis in univariate analysis (all P < 0.001). With a median follow-up of 27 months, LVI was significantly associated with an increased risk of BCF after RP on univariate (P < 0.001) and multivariate analysis (HR: 1.77; 95% CI: 1.11e2.82; P ¼ 0.017). Nevertheless, the authors concluded that LVI added minimally to established predictors on short follow-up.  PSA kinetics as a high-risk feature for failure after RP It is well known, that PSA, in addition to its utility as a screening tool, is also a predictor of adverse pathological features after initial treatment.57 There are many controversies regarding the modern impact of PSA in PCa prognosis and outcome after radical prostatectomy, which have stimulated several investigations and comparative analyses of PSA-based parameters (PSA-doubling time, PSA velocity and PSA slope) as potential predictors of adverse pathology after surgery. In an effort to better confine tumor aggressiveness PSA kinetics has been evaluated for its ability to predict unfavorable cancer characteristics. The majority of such studies have utilized changes in PSA kinetics as their primary endpoint.


PSA doubling time (PSA-DT) seems to be the most studied PSA-based parameter among the others. Many authors agree that PSADT is the strongest predictor of metastasis. Since first recognized as an important prognostic factor 18 years ago, multiple studies have confirmed that PSADT is a predictor not only of clinical progression and metastasis58 but also of prostate-cancer-specific mortality (PCSM).14,59 Moreover, the prognostic importance of PSADT in patients with biochemical recurrence is independent of the type of local therapy (prostatectomy or radiotherapy). However, optimal PSADT cut-points for differential stratification of patients remain uncertain. Zhou et al.60 investigated predictors of PCSM in a series of 489 patients with BCR. Correlates of CP included PSADT, Gleason score, and time from RP to BCR. The authors found that a PSA-DT 3 months correlated strongly with PCSM. PCSM 5 years after BCR was 31% in patients with PSA-DT 3 months vs. 1% for patients with PSADT 3 months. Pound et al.13 found that PSA-DT <10 months predicted time to metastatic progression (MP). However, PSA-DT was dependent on Gleason score, and GS >7 better predicted MP. Predictors of time to MP, however, were the time to interval between RP and BCR and advanced pathologic stage. In another study, which investigated PSA kinetic patterns associated with metastatic disease, clinically significant BCR was characterized by postoperative PSA-DT 3 months. This kinetic pattern was associated with a preoperative PSA velocity of 2 ng/ml/year and specimen Gleason score of 7.61,62 Patel et al.63 demonstrated a strong correlation between PSADT 3 months and CP, however 43% of patients with CP in their series had PSA-DT 6 months. Moreover there is data supporting that the majority of patients who die from PCA have PSA-DT 3 months.64 Conclusively, patients with a short PSADT are at highrisk for prostate cancerespecific mortality and should be offered early aggressive salvage treatment and participation in clinical trials. Conversely, patients with a long PSADT (15 months) are at low risk for prostate cancer-specific mortality and can potentially be spared the effects of secondary treatment because it is unlikely to significantly prolong life in patients with such a low risk. However risk assessment should not be based on PSA-DT solely. Table 1 summarizes the most important characteristics of the adverse features after RP. Prognostic models and their ability to predict risk for failure after RP Men and their doctors may want to know the probability of recurrence following surgery. Traditionally, the judgment of which patients are at high risk for failure after RP has been based largely on final pathologic stage. However, the final pathologic stage alone is a problematic variable for judging high-risk disease, because patients with adverse pathologic features on RP specimens do not have a


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Table 1 Characteristics and prognostic importance of adverse features after RP. Adverse feature Gleason Score (GS) Positive Surgical Margins (PSM Extraprostatic Extension (EPE) Seminal Vesicle Invasion (SVI) Tumor Volume (TV)

Perineural Invasion (PNI) Lymphovascular Invasion (LNI)


Prognostic importance Well established prognostic factor. GS 8-10: bad prognostic factor. Independent predictor of PSA recurrence. Its prognostic power remains controversial in multivariate analyses. Well-established adverse prognostic factor. Significant association between PSM and EPE. Its independent predictive value is not certain. Associated with a poor prognosis. When SVI is present, other adverse features such as PSM play no significant role in prognosis. Its prognostic value has been debated and is still not completely defined. Its role as independent prognostic factor is controversial. In univariate analyses is considered as progression predictor. In multivariate analyses has no prognostic value. It is not considered as a strong independent predictor of failure It has been associated with other unfavorable pathologic features. Its independent role in disease recurrence is controversial. Not significant predictive power on multivariate analysis. It is the strongest predictor of metastasis It correlates strongly with CP and PCSM. Risk assessment should not be based on PSA-DT solely.

uniformly poor prognosis after RP.65 Thus the use of individual pathologic features is insufficient to estimate risk of recurrence. Prognostic models, using known available preoperative and postoperative parameters, have been constructed for this reason. Predictive tools can be subdivided into risk groupings, look-up tables, classification and regression tree analyses, artificial neural networks, and prediction models, with the latter often presented graphically in the form of a nomogram. In 1995 Partin et al.66 developed a biostatistical model using the postoperative Gleason score, the specimen confinement status, and a sigmoidally transformed PSA value to predict which patients with palpable T2b or T2c PCA were most likely to develop early BCR and therefore deemed most suitable to participate in adjuvant clinical trials of new therapies. This validated model successfully stratified patients into high, intermediate, and low-risk groups for early disease recurrence. Han et al.67 retrospectively reviewed the clinical follow-up of a large series of men with clinically localized PCA who underwent RP to identify clinical and/or pathological indicators of BCR. The authors then used those indicators to develop multivariate models for determination of recurrence probability following RP. The so called “Han tables” were designed

to predict the probability of the first evidence of recurrence (detectable PSA level) up to 10 years following surgery, correlating prostatectomy Gleason score, PSA and pathological organ confinement status. Roberts et al.68 constructed a multivariate proportional hazards model based on obtainable clinical and pathologic information, namely lymph node (LN) status, SV status, SM status and Gleason score. EPE, according to the authors, did demonstrate a statistically significant hazard ratio when incorporated into the multivariate analysis. However, the addition of EPE as a fifth variable into the equation did not alter the stratification achieved using only four variables. For this reason and to avoid variability in the classification of EPE, the authors did not include this variable into their model. Kattan et al.69 developed one of the most accurate postoperative tools for prediction of 5-year biochemical recurrence using data from 996 men treated with RP for clinically localized PCA by a single surgeon (73% discrimination). This postoperative nomogram allows the prediction of the probability of cancer recurrence after RP, from the serum PSA level, degree of EPE, specimen Gleason sum, SM status, SVI and LN status. External validation yielded accuracies of 80% (range: 77%e82%). Stephenson et al.12 updated this post-operative tool by including contemporary patients and extending predictions up to 10 years after RP while accounting for disease-free interval. Preoperative PSA, primary and secondary Gleason grades, EPE, PSM, SVI, LNI, treatment year and absence of adjuvant radiotherapy were the variables, on which this nomogram was based. The authors enhanced the updated version of the postoperative nomogram, as predictions are adjusted for patients’ treatment year and for the use of adjuvant radiotherapy, which was considered as a treatment failure in the Kattan’s nomogram. External validation yielded a discrimination of 78%e81%. Suardi et al.70 developed the furthest-reaching prediction tool, which provides the probability of biochemical recurrence up to 20 years after RP. Pathologic stage, SM status, pathologic Gleason sum, type of RP (perineal or retropubic), and adjuvant radiotherapy represented independent predictors of BCR in both Cox and competing-risks regression models and constituted the nomogram predictor variables. Interestingly, preoperative PSA was omitted from consideration within the nomogram. The reason for that was that most of the men in this cohort were treated before the PSA era, so preoperative serum PSA values were unavailable for a large proportion of these patients. In internal validation, the nomogram accuracy was 79.3%, 77.2%, 79.7%, and 80.6% at 5 years, 10 years, 15 years, and 20 years, respectively, after RP. In external validation, the nomogram was 77.4% accurate at 5 years in the first cohort and 77.9%, 79.4%, and 86.3% accurate at 5 years, 10 years, and 15 years, respectively, in the second cohort. Their prediction tool also accounts for disease-free interval. The prediction tool’s discrimination (77e83%) was confirmed in two external validation cohorts. More recently Kattan et al.71

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have constructed new nomograms to directly incorporate surgeon experience as a variable into predictions of biochemical recurrence. The authors found that adjustment for surgeon experience in the predictions resulted in quite minor improvements in discrimination of the preoperative and postoperative nomograms. Predictive tools of the probability of metastatic progression after post-prostatectomy BCR have also been developed. Pound et al.13 in a series of 1997 patients who underwent RP found that PSA-DT <10 months, time to BCR 2 years, and pathologic Gleason score 8e10 were the strongest predictors of bone metastasis. The authors estimated the 3-, 5-, and 7-year probability of bone metastasis by using these three variables. Porter et al.72 developed a nomogram for predicting progression to metastatic disease after RP that can be adjusted according to the disease-free interval. Other investigators combined PSA-kinetics, along with other variables (pretreatment PSA, PSM, SVI, Gleason sum, etc) to devise nomograms predicting the probability of radiographically detectable bone metastasis.73,74 However none of these nomograms that predict the probability of metastatic progression have been externally validated. Table 2 shows an overview of characteristics of selected predictive models. To date, many similar predictive tools have been presented; however before applying such nomograms in clinical practice they need to be well calibrated and extensively validated. Despite their methologic differences, these models can be compared using several common parameters, such as discrimination, calibration, generalizability, level of complexity, and clinical net benefit.75 Although some of these predictive models exhibit good


discrimination and calibration, many have not been externally validated, and few have been directly compared to other models that predict the same endpoint. Future considerations In a review paper published in 2008 it was found that >100 different prediction tools for PCa alone have been presented.76 Although the existing predictive models provide an evidence-based approach to various clinical scenarios in the management of PCA patients, several limitations persist. The first major problem with most prediction models is the lack of independent validation. Predictive and prognostic models remain imperfect also in their discrimination properties and in their calibration characteristics. To date discrimination has not been significantly improved by the incorporation of clinical and pathologic features after initial treatment.77 Furthermore lack of external validation for several predictive models mentioned above, limit their applicability to a population of patients different from that used for their development.75 In other words, a model developed based on one set of patients might be inappropriate for another set of patients, not just for statistical reasons, but due to substantive differences in the data.78 Addition of novel biomarkers and/or imaging data that is associated with the biologic behavior of PCA may reduce the error margin of existing predicting tools. Several biomarkers have demonstrated a significant ability to improve prediction power of these models.79 Thus markers, such as protein expression profiling, transforming growth factor b1, human glandular kallicrein-2 and interleukin-6, may serve as prognosticators and/or therapeutic targets that facilitate

Table 2 Overview of the main characteristics of selected predictive postoperative nomograms. Study

No of patients

Variables used

Median follow-up


External validation


Han et al.67


Specimen Gleason score, PSA and pathological organ confinement status.

5.9 years (1e17)



Roberts et al.68




Not reported

Kattan et al.69


LN status, SV status, SM status and Gleason score. PSA, degree of EPE, specimen Gleason sum, surgical margin status, SVI and LN status. Preoperative PSA, primary and secondary Gleason grades, ECE, PSM, SVI, LNI, treatment year and absence of adjuvant radiotherapy Pathologic stage, SM status, pathologic Gleason sum, type of RP (perineal or retropubic), and adjuvant radiotherapy PSA, ECE, SM status, SVI, LN status, specimen Gleason score, surgeon experience Age, PSA, pathological Gleason sum, SM status, ECE, SMI and LNI

3, 5, 7 and 10 year of BCR free survival probability 3, 5 and 10 year of BCR free survival



Stephenson et al.12


Suardi et al.70


Kattan et al.71


Walz et al.82


37 months (1e168) 25 months (1e193)

10 year progression free probability



11.4 years (0.1e40.5)



3.9 years (1e156 months)

BCR probability at 5, 10, 15 and 20 years after RP 10 year freedom from BCR



2.6 years (0.1e10.8)

2 years BCR-free survival




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the emergence of new therapeutic approaches. Kattan et al.80 incorporated preoperative plasma levels of transforming growth factor b1 and interleukin-6 soluble receptor in the standard “Kattan“ preoperative nomogram. Addition of these biomarkers improved the predictive accuracy from 75% to 83%. This updated prediction tool, which was externally validated, showed an improved discrimination of 87.9% vs. 71.1% for the standard nomogramm that included clinical variables only. Shariat et al.81 also determined the added value of a panel of blood based biomarkers relative to established predictors of BCR. The authors demonstrated an increased predictive accuracy by 15% (86.6% vs. 71.6%) by including transforming growth factor 1, interleukin-6 and its soluble receptor in predictive tools. Furthermore they also found, that early postoperative transforming growth factor b1 is a strong predictor of PCA progression and an excellent candidate marker for inclusion in other predictive models for progression after primary therapy. Nevertheless, more extensive proof continues to be required, as the role of these tools has yet to be studied sufficiently. There are no prospective randomized trials that clearly demonstrate their efficacy in improving patient care and decision making on optimal treatment strategies. Therefore clinical trials would be valuable for establishing the effects of prediction tools and decision analytic methods.

evaluation of RT outcomes is complicated as final pathology does not exist. Current predictive tools rely therefore on a variety of prediction tools using a combination of pretreatment parameters. However, the applicability of existing tools needs to be improved and their accessibility to be simplified. Additionally, their clinical utility should be evaluated in prospective clinical trials. Moreover, as novel treatment modalities, such as laparoscopic or robotic prostatectomy or focal ablative therapy are gaining acceptance, predictive tools to these procedures are also needed. Currently at least partial evidence supports the benefits of predictive tools relative to experience-based predictions, and the use of such models is recommended by many authors. However not every prognostic model is suitable for every patient. Therefore doctors have to choose carefully a predictive tool for every patient individually, based on pre- as well as post-treatment features of his disease. Nevertheless, the interpretation of the results has to be done with caution and, generally, treatment should be tailored to each individual patient.



Prostate cancer encompasses a heterogenous group of patients. The complex natural history of PCA, as well as lack of specific and accurate risk definitions impedes treatment decision-making. The aim of initial treatment is to prevent death and disability from PCA while minimizing intervention-related complications. In other words the ideal end point on which to make therapeutic decisions is survival. Inaccurate risk characterization could result in an inappropriate management, such as indiscriminate application of hormonal or other adjuvant therapeutic modality as well as excluding certain patients from potentially curative local treatment. Therefore, PCA patients need to be involved in decisions regarding management of their disease. In order to make an informed decision, many patients want to know about their likely outcomes and clinicians need to accurately estimate these outcomes. Based on the result of the probability of recurrence, men and their doctors can decide the best course of management after initial treatment. The use of predictive nomograms may be therefore helpful in the complex decision making process. The risk definition after RP relies on several clinical and pathological features, and current tools have significant predictive power for the probability of recurrence and progression. Radiation therapy, either in form of EBRT or PPB, offers the same long-term survival with at least similar quality of life with that of surgery. Nevertheless, the post-treatment risk definition and consequently

Conflict of interest There is no conflict of interest.

1. Jemal A, Siegel R, Xu J, Ward E. Cancer statistics 2010. CA Cancer J Clin 2010;60:277–300. 2. Quinn M, Babb P. Patterns and trends in prostate cancer incidence, survival, prevalence and mortality. Part I international comparisons. BJU Int 2002;90:162–73. 3. Boyle P, Ferlay J. Cancer incidence and mortality in Europe 2004. Ann Oncol 2005;16(3):481–8. 4. Mettlin C, Murphy GP, Babaian RJ, et al. Observation on the early detection of prostate cancer from the American Cancer Society e National Prostate Cancer Detection Project. Cancer 1997;80:1814–7. 5. Farkas A, Schneider D, Perrotti M, Cummings CB, Wars WS. National trends in the epidemiology of prostate cancer, 1973 to 1984: evidence for the effectiveness of prostate specific antigen screening. Urology 1986;52:444–9. 6. Cooperberg MR, Lubeck DP, Mehla SS, Carroll PR. Time trends in clinical risk stratification for prostate cancer: implications for outcomes (data from CaPSURE). J Urol 2009;170:S21–5. 7. Khan MA, Partin AW. Management of high-risk populations with locally advanced prostate cancer. Oncologist 2003;8:259–69. 8. Babaian RJ, Troncoso P, Bhadkamkar VA, Johnston DA. Analysis of clinicopathologic factors predicting outcome after radical prostatectomy. Cancer 2001;81:1414–22. 9. Nguyen CT, Reuther AM, Stephenson AJ, Klein EA, Jones JS. The specific definition of high risk prostate cancer has minimal impact on biochemical relapse-free survival. J Urol 2009;181:75–80. 10. Loeb S, Schaeffer EM, Trock BJ, Epstein JL, Humphreys EB, Walsh PC. What are the outcomes of radical prostatectomy for high-risk prostate cancer? Urology 2010;76(3):710–4. 11. Ward JF, Zincke H, Bergstrahl EJ, Slezak JM, Myers RP, Blute ML. The impact of surgical approach (nerve bundle preservation versus wide local excision) on surgical margins and biochemical recurrence following radical prostatectomy. J Urol 2004;172:1328–32.

S. Adamis, I.M. Varkarakis / EJSO 40 (2014) 496e504 12. Stephenson AJ, Scardino PT, Eastham JA, et al. Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Clin Oncol 2005;23:7005–12. 13. Pound CR, Partin AW, Eisenberger MA, Chan DW, Pearson JD, Walsh PC. Natural history of progression after PSA elevation following radical prostatectomy. JAMA 1999;281:1591–7. 14. Freedland SJ, Humphreys EB, Mangold LA, et al. Risk of prostate cancer-specific mortality following biochemical recurrence after radical prostatectomy. JAMA 2005;294:433–9. 15. Gleason DF, Mellinger GT. Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging. J Urol 2002;167:953–8. 16. Harnden P, Shelley MD, Coles B, Staffurth J, Mason MD. Should the Gleason grading system for prostate cancer be modified to account for high-grade tertiary components? A systematic review and metaanalysis. Lancet Oncol 2007;8:411–9. 17. Sim HG, Telesca D, Culp SH, et al. Tertiary Gleason pattern 5 in Gleason7 prostate cancer predicts pathological stage and biochemical recurrence. J Urol 2008;179:1775–9. 18. Alenda O, Ploussard G, Mouracade P, et al. Impact of the primary Gleason pattern on biochemical recurrence-free survival after radical prostatectomy: a single-center cohort of 1,248 patients with Gleason 7 tumors. World J Urol 2011 Oct;29(5):671–6. 19. Epstein JI, Amin M, Boccon-Gibod L, et al. Prognostic factors and reporting of prostate carcinoma in radical prostatectomy and pelvic lymphadenectomy specimens. Scand J Urol Nephrol Suppl 2005; 216:34–63. 20. Swindle P, Eastham JA, Ohori M, et al. Do margins matter? The prognostic significance of positive surgical margins in radical prostatectomy specimens. J Urol 2005;174:903–7. 21. Eastham JA, Kuroiwa K, Ohori M, et al. Prognostic significance of location of positive surgical margins in radical prostatectomy specimens. Urology 2007;70:965–9. 22. Pfitzenmaier J, Pahernik S, Tremmel T, Haferkamp A, Buse S, Hohenfellner M. Positive surgical margins after radical prostatectomy: do they have an impact on biochemical or clinical progression? BJU Int 2008;102:1413–8. 23. Vis AN, Schroeder FH, van der Kwast TH. The actual value of the surgical margin status as a predictor of disease progression in men with early prostate cancer. Eur Urol 2006;50:258–65. 24. Simon MA, Kim S, Soloway MS. Prostate specific antigen recurrence rates are low after radical retropubic prostatectomy and positive margins. J Urol 2006;175:140–4. 25. Cheng L, Darson MF, Bergstrahl EJ, Slezak J, Myers RP, Bostwick DG. Correlation of margin status and extraprostatic extension with progression of prostate carcinoma. Cancer 1999;86:1775–82. 26. Alkhateeb S, Alibhai S, Fleshner N, et al. Impact of positive surgical margins after radical prostatectomy differs by disease risk group. J Urol 2010;183:145–50. 27. Sofer M, Hamilton-Nelson KL, Civantos F, Soloway MS. Positive surgical margins after radical retropubic prostatectomy: the influence of site and number on progression. J Urol 2002;167:2453–6. 28. Marks RA, Koch MO, Lopez-Beltran A, Montironi R, Juliar BE, Cheng L. The relationship between the extend of surgical margin positivity and prostate specific antigen recurrence in radical prostatectomy specimens. Hum Pathol 2007;38:1207–11. 29. Pettus JA, Weight CJ, Thompson CJ, Middleton RG, Stephenson RA. Biochemical failure in men following radical retropubic prostatectomy: impact of surgical margin status and location. J Urol 2004; 172:129–32. 30. Aydin H, Tsuzuki T, Hernandez D, Walsh PC, Parin AW, Epstein JI. Positive proximal (bladder neck) margin at radical prostatectomy confers greater risk of biochemical progression. Urology 2004;64: 551–5. 31. Sakr WA, Wheeler TM, Blute M, et al. Staging and reporting of prostate cancerdsampling of the radical prostatectomy specimen. Cancer 1996;78:366–8.


32. Ayala AG, Ro JY, Babaian R, Troncoso P, Grignon DJ. The prostatic capsule: does it exist? its importance in the staging and treatment of prostatic carcinoma. Am J Surg Pathol 1989;13:21–7. 33. Swanson GP, Basler JW. Prognostic factors for failure after prostatectomy. J Cancer 2011;2:1–19. 34. Roehl KA, Han M, Ramos CG, Antenor JA, Catalona WJ. Cancer progression and survival rates following anatomical radical retropubic prostatectomy in 3,478 consecutive patients: long-term results. J Urol 2004;172:910–4. 35. Karakiewicz PI, Eastham JA, Graefen M, et al. Prognostic impact of positive surgical margins in surgically treated prostate cancer: multiinstitutional assessment of 5831 patients. Urology 2005;66:1245–50. 36. Ravery V, Boccon-Gibod LA, Meulemans A, et al. Predictive value of pathological features for progression after radical prostatectomy. Eur Urol 1994;26:197–201. 37. Potter SR, Epstein JI, Partin AW. Seminal vesicle invasion: significance and therapeutic implications. Rev Urol 2000;2(3):190–5. 38. Epstein JL, Carmichael M, Walsh PC. Adenocarcinoma of the prostate invading the seminal vesicle: definition and relation of tumor volume, grade and margins of resection to prognosis. J Urol 1993; 149:1040–5. 39. Ohori M, Wheeler TM, Kattan MW, Goto Y, Scardino PT. Prognostic significance of positive surgical margins in radical prostatectomy specimens. J Urol 1995;154:1818–24. 40. Salomon L, Anastasiadis AG, Johnson CW, et al. Seminal vesicle involvement after radical prostatectomy: predicting risk factors for progression. Urology 2003;62:304–9. 41. Epstein JL, Partin AW, Potter SW, Walsh PC. Adenocarcinoma of the prostate invading the seminal vesicle: prognostic stratification based on pathologic parameters. Urology 2000;56:283–8. 42. Guzzo TJ, Vira MA, Neway W, et al. Minimal tumor volume may provide additional prognostic information in good performance patients after radical prostatectomy. Urology 2007;69(6):1147–51. 43. Chun FK, Briganti A, Jedres C, et al. Tumor volume and high grade tumor volume are the best predictors of pathologic stage and biochemical recurrence after radical prostatectomy. Eur J Cancer 2007;43(3):536–43. 44. Salomon L, Levrel O, Anastasiadis AG, et al. Prognostic significance of tumor volume after radical prostatectomy: a multivariate analysis of pathological prognostic factors. Eur Urol 2003;43:39–44. 45. Rampersaud EN, Sun L, Moul JW, Madden J, Freedland SJ. Percent tumor involvement and risk of biochemical progression after radical prostatectomy. J Urol 2008;180:571–6. 46. Thompson IM, Salem S, Chang SS, et al. Tumor volume as a predictor of adverse pathologic features and biochemical recurrence (BCR) in radical prostatectomy specimens: a tale of two methods. World J Urol 2011;29:15–20. 47. Endrizzi J, Seay T. The relationship between early biochemical failure and perineural invasion in pathological T2 prostate cancer. Br J Urol 2000;85:696–9. 48. Epstein JI. The role of perineural invasion and other biocharacteristics as prognostic markers for localized prostate cancer. Sem Urol Oncol 1998;16:124–8. 49. Brooks JP, Albert PS, O’Connell J, McLeod DG, Poggi MM. Lymphovascular invasion in prostate cancer: prognostic significance in patients treated with radiotherapy after radical prostatectomy. Cancer 2006;106:1521–6. 50. Shariat SF, Khoddami SM, Saboorian H, et al. Lymphovascular invasion is a pathological feature of biologically aggressive disease in patients treated with radical prostatectomy. J Urol 2004;171: 1122–7. 51. May M, Kaufmann O, Hammermann F, Loy V, Siegsmund M. Prognostic impact of lymphovascular invasion in radical prostatectomy specimens. BJU Int 2006;99:539–44. 52. Herman CM, Wilcox GE, Kattan MW, Scardino PT, Wheeler TM. Lymphovascular invasion as a predictor of disease progression in prostate cancer. Am J Surg Pathol 2000;24:859–63.


S. Adamis, I.M. Varkarakis / EJSO 40 (2014) 496e504

53. De la Taille A, Rubin MA, Buttyan R, et al. Is microvascular invasion on radical prostatectomy specimen a useful predictor of PSA recurrence for prostate cancer patients? Eur Urol 2000;38:79–84. 54. Van den Ouden D, Kranse R, Hop WC, van der Kwast TH, Schroder FH. Microvascular invasion in prostate cancer: prognostic significance in patients treated by radical prostatectomy for clinically localized carcinoma. Urol Int 1998;60:17–24. 55. Cheng L, Jones TD, Lin H, et al. Lymphovascular invasion is an independent prognostic factor in prostatic adenocarcinoma. J Urol 2005;174:2181–5. 56. Yee DS, Shariat SF, Lowrance WT, et al. Prognostic significance of lymphovascular invasion in radical prostatectomy specimens. BJU Int 2011;108(4):502–7. 57. Partin AW, Mangold LA, Lamm DM, et al. Contemporary update of prostate cancer staging nomograms (Partin tables) for the new millennium. Urology 2001;58:843–8. 58. Okotie OT, Aronson WJ, Wieder JA, et al. Predictors of metastatic disease in men with biochemical failure following radical prostatectomy. J Urol 2004;171:2260–4. 59. Trock BJ, Han M, Freedland SJ, et al. Prostate cancer-specific survival following salvage radiotherapy vs observation in men with biochemical recurrence after radical prostatectomy. JAMA 2008; 299:2760–9. 60. Zhou P, Chen MH, McLeod D, Caroll PR, Moul JW, D’Amico AV. Predictors of prostate cancer specific mortality after radical prostatectomy or radiation therapy. J Clin Oncol 2005;23:6992–8. 61. D’ Amico AV, Chen MH, Roehl KA, Catalona WJ. Identifying patients at risk for significant versus clinically insignificant postoperative prostate-specific antigen failure. J Clin Oncol 2005;23:4975–9. 62. D’Amico AV, Moul JW, Carroll PR, Sun L, Lubeck D, Chen MH. Surrogate end point for prostate cancer specific mortality after radical prostatectomy or radiation therapy. J Natl Cancer Inst 2003;95:1376–83. 63. Patel A, Dorey F, Franklin J, de Kernion JB. Recurrence patterns after radical retropubic prostatectomy: clinical usefulness of prostate specific antigen doubling times and log slope prostate specific antigen. J Urol 1997;158:1441–5. 64. Simmons MN, Stephenson AJ, Klein EA. Natural history of biochemical recurrence after radical prostatectomy: risk assessment for secondary therapy. Eur Urol 2007;51:1175–84. 65. Yossepowitch O, Eggener SE, Bianco FJ, et al. Radical prostatectomy for clinically localized, high-risk prostate cancer: critical analysis of risk assessment methods. J Urol 2007;178:493–9. 66. Partin AW, Piantadosi S, Santa MG, et al. Selection of men at high risk for disease recurrence for experimental adjuvant therapy following radical prostatectomy. Urology 1995;45:831–8. 67. Han M, Partin AW, Zahurak M, Piantadosi S, Epstein JI, Walsh CW. Biochemical (prostate specific antigen) recurrence probability following radical prostatectomy for clinically localized prostate cancer. J Urol 2003;169:517–23.

68. Roberts WW, Bergstrahl EJ, Blute ML, et al. Contemporary identification of patients at high risk of early prostate cancer recurrence after radical retropubic prostatectomy. Urology 2001;57:1033–7. 69. Kattan MW, Wheeler TM, Scardino PT. Postoperative nomogram for disease recurrence after radical prostatectomy for prostate cancer. J Clin Oncol 1999;17:1499–507. 70. Suardi N, Porter CR, Reuther AM, et al. A nomogram predicting long-term biochemical recurrence after radical prostatectomy. Cancer 2008;112:1254–63. 71. Kattan MW, Vickers AJ, Yu C, et al. Preoperative and postoperative nomograms incorporating surgeon experience for clinically localized prostate cancer. Cancer 2009;115:1005–10. 72. Porter CR, Suardi N, Kodama K, et al. A nomogram predicting metastatic progression after radical prostatectomy. Int J Urol 2008;15: 889–94. 73. Slovin SF, Wilton AS, Heller G, Scher HI. Time to detectable metastatic disease in patients with rising prostate-specific antigen values following surgery or radiation therapy. Clin Cancer Res 2005;11: 8669–73. 74. Dotan ZA, Bianco Jr FJ, Rabbani F, et al. Pattern of prostate-specific antigen (PSA) failure dictates the probability of a positive bone scan in patients with an increasing PSA after radical prostatectomy. J Clin Oncol 2005;23:1962–8. 75. Lughezzani G, Briganti A, Karakiewitcz PI, et al. Predictive and prognostic models in radical prostatectomy candidates: a critical analysis of the literature. Eur Urol 2010;58:687–700. 76. Shariat SF, Karakiewitcz PI, Roehrborn CG, Kattan MW. An updated catalog of prostate cancer predictive tools. Cancer 2008;113: 3075–99. 77. Shariat SF, Kattan MW, Vickers AJ, Karakiewitcz AJ, Scardino PT. Critical review of prostate cancer predictive tools. Future Oncol 2009;5(10):1555–84. 78. Vickers A. Prediction models in urology: are they any good, and how would we know anyway? Eur Urol 2010;57:571–3. 79. Kattan MW. Evaluating a new marker’s predicting contribution. Clin Cancer Res 2004;10:822–4. 80. Kattan MW, Shariat SF, Andrews B, et al. The addition of interleukin-6 soluble receptor and transforming growth factor beta improves a preoperative nomogram for predicting biochemical progression in patients with clinically localized prostate cancer. J Clin Oncol 2003;21:3573–9. 81. Shariat SF, Kattan MW, Traxel E, et al. Association of pre- and postoperative plasma levels of transforming growth factor b1 and interleukin-6 and its soluble receptor with prostate cancer progression. Clin Cancer Res 2004;10:1992–9. 82. Walz J, Chun FK, Klein EA, et al. Nomogram predicting the probability of early recurrence after radical prostatectomy for prostate cancer. J Urol 2009;181:601–7.