Numerous predictive and prognostic tools have recently been developed for risk stratification of prostate cancer (PCa) patients who are candidates for or have been treated with radical prostatectomy (RP).
To critically review the currently available predictive and prognostic tools for RP patients and to describe the criteria that should be applied in selecting the most accurate and appropriate tool for a given clinical scenario.
A review of the literature was performed using the Medline, Scopus, and Web of Science databases. Relevant reports published between 1996 and January 2010 identified using the keywords prostate cancer, radical prostatectomy, predictive tools, predictive models, and nomograms were critically reviewed and summarised.
We identified 16 predictive and 22 prognostic validated tools that address a variety of end points related to RP. The majority of tools are prediction models, while a few consist of risk-stratification schemes. Regardless of their format, the tools can be distinguished as preoperative or postoperative. Preoperative tools focus on either predicting pathologic tumour characteristics or assessing the probability of biochemical recurrence (BCR) after RP. Postoperative tools focus on cancer control outcomes (BCR, metastatic progression, PCa-specific mortality [PCSM], overall mortality). Finally, a novel category of tools focuses on functional outcomes. Prediction tools have shown better performance in outcome prediction than the opinions of expert clinicians. The use of these tools in clinical decision-making provides more accurate and highly reproducible estimates of the outcome of interest. Efforts are still needed to improve the available tools’ accuracy and to provide more evidence to further justify their routine use in clinical practice. In addition, prediction tools should be externally validated in independent cohorts before they are applied to different patient populations.
Predictive and prognostic tools represent valuable aids that are meant to consistently and accurately provide most evidence-based estimates of the end points of interest. More accurate, flexible, and easily accessible tools are needed to simplify the practical task of prediction.
Keywords: Prostate cancer, Radical prostatectomy, Prediction tools, Nomograms.
Prostate cancer (PCa) is the most common solid malignancy in men, with 192 280 new cases having been diagnosed in 2009 in the United States . Different treatment options are currently available for men with localised PCa, such as external-beam radiation therapy (EBRT), brachytherapy, and active surveillance. Radical prostatectomy (RP) represents one of the most commonly used therapies for clinically localised PCa. The selection of a candidate for RP remains complex because of the variety of strategies that can be followed in planning the surgical technique, postoperative treatment, and follow-up. Tools that could facilitate the decision-making process in these phases would be very valuable.
In this manuscript, we provide an overview of the currently available predictive (ie, predicting the probability of an outcome without considering the effect of time) and prognostic (ie, predicting the probability of an outcome over time) tools for candidates to RP and for patients who have already undergone the procedure. In addition, we provide objective characteristics that support the use of some models over others. Finally, because several patients with localised PCa may elect other treatment options, we provide a brief summary of prediction tools in patients treated with EBRT or brachytherapy.
2. Evidence acquisition
A review of the literature using the Medline, Scopus, and Web of Science databases was conducted to identify original articles, review articles, and editorials regarding predictive and prognostic tools in patients treated with RP and in candidates for RP. Searches were limited to the English language. The literature search was performed by combining the following terms: prostate cancer, radical prostatectomy, predictive tools, predictive models, and nomograms. A total of 415 records were retrieved from Medline, 748 from Scopus, and 712 from Web of Science. Subsequently, the search results were pooled, and articles published between 1996 and January 2010 were identified. The articles with the highest level of evidence were selected, reviewed, and summarised, with the consensus of all of the authors of this paper.
3. Evidence synthesis
3.1. Different methods used for clinical decision making
Clinical decision-making may be based on physician experience; randomised, controlled trials (RCTs); or on the information received from decision aids. Experience alone results in poorly reproducible predictions and in excessive interrater variability , , and . Moreover, RCTs are too infrequently performed in the context of RP; even when RCT data exist, they are not universally implemented for a number of reasons  and . Therefore, most clinical questions that pertain to PCa cannot be solved using RCT data. As a consequence, several predictive and prognostic tools have been developed to assist with uncertainties related to clinical decision-making. These tools are intended to provide more accurate and highly reproducible estimates of the outcome of interest  and . To date, these tools have shown better performance in outcome prediction than the opinions of expert clinicians in both prostate and breast cancer , , and .
3.2. How should a model be evaluated?
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. Despite their methodologic differences, these models can be compared using several common parameters: discrimination, calibration, generalisability, level of complexity, and clinical net benefit , , , and .
Discrimination and calibration represent the key properties that should invariably be reported when any model is validated. Discrimination represents the probability that the tool will correctly predict a poorer outcome for the patient who initially develops the outcome of interest out of a randomly selected pair of patients. Discrimination is usually quantified using the area under curve for binary outcomes and the concordance index for censored data. Both metrics range from 50% (no discrimination) to 100% (perfect discrimination) , , , and . Overall discrimination needs to be complemented with calibration, which is an evaluation of the relationship between predicted probabilities and observed rates of the outcome of interest, throughout the entire range of predictions , , , and . Inclusion of calibration properties is necessary, because a model may predict perfectly well in low-risk patients, but its predictions may be of dismal quality in high-risk individuals.
In addition, discrimination and calibration findings need to be generalisable and applicable to the population of interest. For example, data confirming the validity of a tool in Caucasian patients may not be generalisable to African American patients, even if all other variables are held constant . Therefore, external validation of a predictive or prognostic tool should be mandatory to establish whether the tool works satisfactorily in different patient populations , , and . Moreover, predictive and prognostic tools need to be simple enough to ensure that physicians are capable of using them in a busy clinical practice.
Finally, a recommended tool should be better than competing tools. Recently, Vickers et al described decision curve analysis, which is used to evaluate the effect of different models on medical decision-making . Decision curve analysis provides a common denominator for comparing predictive tools, as it factors the harms and benefits of a given tool within a single metric, and should therefore complement discrimination and calibration results when different predictive or prognostic tools are compared . Moreover, decision curve analysis is easier to implement than classic decision analysis .
3.3. End points of model application in the context of radical prostatectomy
3.3.1. Surgical planning
188.8.131.52. Preoperative prediction of Gleason score upgrading
Gleason score upgrading (GSU) between prostatic biopsy and RP pathology may affect treatment planning (Table 1). Therefore, knowing the probability of GSU may help select the best treatment option according to preoperative characteristics. This is especially important in patients with low-risk PCa who seek less invasive therapy, such as active surveillance, because the presence of potentially life-threatening disease may be underestimated. Five tools can predict the probability of GSU. D’Amico et al (n
|Reference||Year||Prediction form||Outcome||No. of patients||Variables||Discrimination||Validation|
|Prediction of Gleason score upgrading|
|1999||Look-up table||Gleason score upgrading (defined as 50% or higher probability of Gleason ≥4)||693||PSA, clinical stage, prostate volume||Internal: Not reported||–||X|
|2006||Nomogram||Gleason score upgrading (defined as any upgrade from prostatic biopsy to RP pathology)||2982||PSA, clinical stage, primary biopsy Gleason, secondary biopsy Gleason||Internal: 80.4%||X||X|
|2007||Nomogram||Gleason score upgrading (defined as upgrade from Gleason sum 6 to Gleason sum ≥7)||175||Age, PSA, clinical stage, primary biopsy Gleason, secondary biopsy Gleason||Internal: 71.0%||X||–|
|2009||Nomogram||Gleason score upgrading (defined as upgrade from Gleason sum ≤6 to Gleason sum ≥7)||301||Biopsy Gleason sum, number of cores, number of positive cores||Internal: 66.1%||X||–|
|2009||Nomogram||Gleason score upgrading (defined as upgrade from Gleason sum 6 to Gleason sum ≥7 and from primary Gleason 3 to primary Gleason 4)||175||Age, race, abnormal DRE, prostate volume, clinical stage, number of previous biopsies, PSA, number of cores, number of positive cores, percent of cancer in positive cores, secondary biopsy Gleason, perineural invasion, inflammation, HGPIN, atypia||Internal: 68.0%||X||–|
|Prediction of pathologic stage|
|1997||Look-up table||Pathologic stage (ECE, SVI, LNI)||4133||Biopsy Gleason sum, clinical stage, PSA||Internal: 72.4%||X||X|
|2004||Nomogram||Side-specific ECE||763||PSA, clinical stage, side-specific biopsy Gleason sum, side-specific percent of positive cores, side-specific percent of cancer in positive cores||Internal: 80.6%||X||–|
|2006||Nomogram||Side-specific ECE||1118||PSA, clinical stage, biopsy Gleason sum, percent of positive cores, percent of cancer in positive cores||Internal: 84.0%||X||–|
|2003||Nomogram||SVI||763||PSA, clinical stage, primary and secondary Gleason sum, percent of cancer at the base||Internal: 88.3%||X||–|
|2007||Nomogram||SVI||6740||Age, PSA, biopsy Gleason sum, clinical stage||Internal: 80.0%||X||–|
|2007||Nomogram||SVI||666||PSA, clinical stage, biopsy Gleason sum, percent positive biopsy cores||Internal: 79.2%||X||X|
|2003||Nomogram||LNI assessed with limited pelvic lymphadenectomy||5510||PSA, clinical stage, biopsy Gleason sum||Internal: 76.0%||X||–|
|2006||Nomogram||LNI assessed with extended pelvic lymphadenectomy (≥10 nodes removed)||602||PSA, clinical stage, biopsy Gleason sum||Internal: 76.0%||X||X|
|2007||Nomogram||LNI (probability of exclusive non-obturator lymph node metastases)||565||PSA, clinical stage, biopsy Gleason sum||Internal: 80.2%||X||–|
|Prediction of functional outcomes|
|2008||Nomogram||Trifecta probability (BCR, continence, potency)||1577||PSA, clinical stage, biopsy Gleason core, pretreatment erectile function, months from RP, age at RP||Internal: 77.3%||X||–|
|2010||Probability graph||Erectile function recovery||435||Age, CCI score, baseline IIEF||Internal: 69.1%||X||X|
|External: Not reported|
Only one tool for GSU prediction has been externally validated. However, the currently available GSU predictive tools were devised on a sextant prostate biopsy population. Therefore, these tools need to be validated and/or updated in a contemporary extended prostate biopsy population.
184.108.40.206. Preoperative prediction of life expectancy
Ten years is usually considered the minimum life expectancy prerequisite for RP candidates (Table 2). Unfortunately, clinicians are poor raters of life expectancy  and . Therefore, tools that can accurately identify those individuals who do not have sufficient life expectancy to warrant definitive PCa treatment are needed to help optimise treatment decision-making.
|Reference||Yr||Prediction form||Outcome||No. of patients||Variables||Discrimination||Validation|
|Preoperative prediction of perioperative mortality|
|2007||Nomogram||30-d mortality after RP||9208||Age, patient comorbidities, and surgical volume||Internal: 67.1%||X||–|
|Preoperative prediction of PCSM|
|2003||Probability graph||PCSM||4946||Biopsy Gleason sum, clinical stage, PSA||Not reported||–||–|
|2009||Probability graph||PCSM (5 and 10 yr after RP)||10627 (5378 RP patients)||Age, PSA, biopsy Gleason sum, clinical stage, percent positive biopsy||Internal: 80.0%||X||–|
|2009||Nomogram||PCSM (10 and 15 yr after BCR)||6398||Primary and secondary Gleason grade, PSA, clinical stage||External: 82.0%||–||X|
|Postoperative prediction of PCSM|
|2005||Probability graph||PCSM (5 yr after BCR)||498||PDT, biopsy Gleason sum||Not reported||–||–|
|2005||Look-up table||PCSM (5, 10, and 15 yr after BCR)||379||PDT, Gleason sum, time from surgery to BCR||Internal: 84.0%||X||–|
|Preoperative prediction of life expectancy|
|1996||Probability formula||OS (10 yr)||451||Age, Gleason sum, and index of coexistent disease category||Internal: Not reported||–||X|
|2004||Probability graph||OS (10 yr)||1611||Age, race, comorbidity, PSA, Gleason sum, treatment type||Internal: Not reported||–||X|
|External: 70.0% and 81.0%|
|2006||Nomogram||Life expectancy (5, 10, and 15 yr)||506||Age, CCI, presence of angina, systolic blood pressure, BMI, smoking, marital status, PSA< Gleason sum, clinical stage, treatment type||Internal: 73.0%||X||–|
|2007||Nomogram||Life expectancy (10 yr)||9131 (5955 RP patients)||Age, CCI, treatment type||Internal: 84.3%||X||X|
Two models with proven external validity can provide life expectancy estimates. The Tewari et al model for prediction of life expectancy in RP candidates (n
220.127.116.11. Preoperative prediction of pathologic stage
Prediction of pathologic stage is obviously important in surgical treatment planning (Table 1). Patients with a low probability of having PCa with extracapsular extension (ECE) of their disease may benefit from a more conservative (eg, nerve-sparing) surgery relative to individuals with a high risk of ECE. Similarly, individuals at high risk of having seminal vesical invasion (SVI) or lymph node invasion (LNI) may benefit from more extensive surgical approaches as well as from the prompted use of adjuvant therapies.
Partin et al (n
Lack of ability to provide side-specific ECE predictions represents a limitation of the Partin tables. This information is particularly important during surgical planning, because the decision to preserve one or both neurovascular bundles affects functional outcomes and, subsequently, the patient's quality of life. The issue of side-specific ECE predictions was addressed by two nomograms devised by Ohori et al (n
Koh et al (n
Cagiannos et al (n
In sum, several tools are available for the prediction of pathologic stage (ECE, SVI, and LNI) at RP. Of those, some have been externally validated and should be given higher priority when making pathologic stage predictions. However, it is worth noting that most external validations were performed in a single cohort , , and .
18.104.22.168. Preoperative prediction of perioperative mortality
Perioperative mortality remains an important issue in any type of surgery (Table 2). Mortality rates after RP can vary from 0.1% to 0.5% according to the type of population and type of institution  and . Recently, Walz et al (n
22.214.171.124. Preoperative prediction of biochemical recurrence
Biochemical recurrence (BCR) is widely used as an end point to assess RP efficacy (Table 3). BCR also represents a marker of metastatic progression and PCa-specific mortality  and . Despite the widespread use of BCR, uniform criteria to define BCR after RP are lacking, and numerous definitions are used in the medical literature, leading to different results in outcome reporting . Of note, estimates of risk ratio and predictive accuracy are generally robust regarding the definition of BCR . Stratifying patients according to their risk of BCR may be useful in selecting those who may benefit from an adjuvant therapy on the basis of their likelihood of treatment failure.
|Reference||Yr||Prediction form||BCR, yr||No. of patients||Variables||Discrimination||Validation|
|Preoperative prediction of BCR|
|1998||Probability graph||3 and 5||888||Biopsy Gleason sum, clinical stage, PSA||Internal: Not reported||–||X|
|2005||Probability graph||3 and 5||1439||Age, PSA, biopsy Gleason sum, clinical stage, percent positive biopsy||Internal: 66.0%||X||X|
|2006||Nomogram||1–10||1978||PSA, clinical stage, biopsy Gleason sum, yr of surgery, number of positive and negative cores||Internal: 76.0%||X||X|
|Postoperative prediction of BCR|
|1998||Look-up table||2||862||PSA, pathologic stage, Gleason sum, surgical margin status||Not reported||–||–|
|2009||Nomogram||2||2911||PSA, Gleason sum, surgical margin status, ECE, SVI, LNI||Internal: Not reported||–||X|
|2005||Nomogram||10||1881||PSA, Gleason sum, ECE, SVI, LNI, surgical margin status||Internal: 86.0%||X||X|
|2008||Nomogram||5, 10, 15, and 20||601||Gleason sum, pathologic stage, surgical margin status, type of surgery, adjuvant RT||Internal: 77.2–80.6%||X||X|
Preoperative prediction of BCR can be accomplished with the D’Amico et al risk stratification scheme (n
126.96.36.199. Preoperative prediction of prostate cancer–specific mortality
PCa-specific mortality (PCSM) represents the most important cancer-control end point in RP candidates (Table 2). D’Amico et al (n
More recently, Stephenson et al devised a competing-risks regression-based nomogram for predicting PCSM in the PSA era (n
188.8.131.52. Preoperative prediction of functional outcomes
Similarly to cancer-control outcomes, functional outcomes can be predicted prior to RP, thus helping clinicians choose the best treatment options according to preoperative patient and tumour characteristics (Table 1). Eastham et al (n
3.3.2. Immediate postoperative counselling
184.108.40.206. Postoperative prediction of biochemical recurrence
Prediction of BCR after RP represented the focus of several previously reported prognostic tools (Table 3) , , , and . Early BCR prediction was first addressed by D’Amico et al (n
BCR up to 10 yr after RP can be predicted using the postoperative Stephenson nomogram (n
BCR can also be predicted for even more distant end points (up to 20 yr after RP) using the nomogram developed by Suardi et al . This nomogram relies on pathologic Gleason sum, pathologic stage, surgical margin status, type of surgery, and adjuvant radiation therapy (RT), and it has been externally validated in a European cohort (discrimination: 77.9–86.3%). Moreover, its predictions can be adjusted according to the disease-free interval .
In sum, three externally validated nomograms are available for BCR predictions. Of these, the Stephenson et al nomogram has the greatest flexibility in providing BCR predictions at different time points (1–10 yr after RP) . The Walz et al nomogram is most specific for early BCR predictions . Finally, the Suardi et al nomogram is best suited to predictions beyond 10 yr and illustrates the persistent risk of BCR despite ≥10 yr of follow-up .
3.3.3. Long-term end points
220.127.116.11. Prediction of progression to metastatic disease
After RP, patients may develop BCR, and a portion of those patients may eventually develop bone metastases (Table 4). Three investigators devised tools capable of predicting the probability of metastatic progression after BCR. Pound et al examined 1997 RP patients (original discrimination not reported) and found that PSA doubling time (PDT)
|Reference||Yr||Prediction form||Patient population||Outcome||No. of Patients||Variables||Discrimination||Validation|
|Progression to metastatic disease|
|1999||Regression tree||BCR after RP||Metastasis (metastatic disease-free probability at 3, 5, and 7 yr)||315||PDT, Gleason sum, time to BCR||Not reported||–||–|
|2005||Nomogram||BCR after RP||Positive bone scan||239||Pre-treatment PSA, surgical margin status, SVI, Gleason sum, trigger PSA, ECE, PSA slope, PSA velocity||Internal: 93.0%||X||–|
|2005||Nomogram||BCR after RP||Metastasis (1 and 2 yr after treatment)||74||Baseline PSA, PDT, pathologic stage, Gleason sum||Internal: 69.0%||X||–|
|2008||Nomogram||RP||Metastasis (5, 10, 15, and 20 yr after RP)||752||Pathologic stage, Gleason sum, comorbidity, adjuvant RT||Internal: 76.0–82.0%||X||–|
|2009||Probability graph||RP||Metastasis (5 and 10 yr after RP)||10 627 (5378 RP patients)||Age, PSA, biopsy Gleason sum, clinical stage, percent positive biopsy||Internal: 78.0%||X||–|
In sum, no available tools with externally confirmed discrimination can assist the clinician with the prediction of bone metastasis after RP. As a consequence, the existing models need to be used with caution when determining the timing or type of staging investigations in patients treated with RP.
18.104.22.168. Postoperative prediction of prostate cancer–specific mortality
D’Amico et al (n
3.4. Models in the context of external-beam radiation therapy or focal therapy
Although RP remains one of the most commonly used therapies, an increasingly higher number of patients with localised PCa are currently managed with different treatment strategies (Table 5). Because no randomised trials directly comparing the efficacy and morbidity of these various treatment approaches have been undertaken, management decisions are often based on physician judgment and patient preferences. Therefore, prediction tools can be used to provide the most unbiased estimates of clinically relevant end points and facilitate patient counselling.
|Reference||Yr||Prediction form||Patient population||BCR, yr||No. of patients||Variables||Discrimination||Validation|
|Prediction of BCR|
|1997||Probability graph||EBRT||5||500||Biopsy Gleason sum, clinical stage, PSA||Not available||–||–|
|1998||Probability graph||EBRT and brachytherapy||3, 5||766 and 218||Biopsy Gleason sum, clinical stage, PSA||Not available||–||–|
|2000||Nomogram||EBRT||5||1042||PSA, biopsy Gleason sum, clinical stage, neoadjuvant ADT, radiation dose delivered||Internal: 73%||X||X|
|2001||Nomogram||Brachytherapy||5||920||Biopsy Gleason sum, clinical stage, PSA, co-administration of EBRT||Internal: Not reported||–||X|
|2007||Nomogram||EBRT||5, 10||2253||PSA, biopsy Gleason sum, clinical stage, neoadjuvant ADT, radiation dose delivered||Internal: 72%||X||–|
|2010||Nomogram||Brachytherapy||9||5931||Clinical stage, biopsy Gleason sum, isotope, EBRT, minimum dose to 90% of the prostate, PSA||Internal: 71%||X||–|
|Reference||Yr||Prediction form||Patient population||Outcome||No. of patients||Variables||Discrimination||Validation|
|Prediction of survival/progression to distant metastasis|
|2002||Probability graph||EBRT||PCSM (10 yr)||381||Biopsy Gleason sum, clinical stage, PSA||Not available||–||–|
|2003||Probability graph||EBRT||PCSM (8 yr)||2370||Biopsy Gleason sum, clinical stage, PSA||Not available||–||–|
|2005||Probability graph||EBRT||PCSM (5 yr)||661||PDT, biopsy Gleason sum||Not available||–||–|
|2003||Nomogram||EBRT||Progression to metastasis (5 yr)||1677||PSA, clinical stage, biopsy Gleason sum||Internal: Not reported||X||X|
|2005||Nomogram||EBRT||Progression to metastasis (1–2 yr)||71||Baseline PSA, PDT, pathologic T stage, Gleason sum||Internal: 69%||X||–|
Many of the prediction tools that we previously discussed (eg, tools predicting pathologic stage, Gleason score upgrading, life expectancy) may help patients and clinicians in deciding the most appropriate treatment option. In addition, several other tools have been developed to predict BCR, survival, or progression to distant metastasis in patients treated with EBRT or brachytherapy , , , , , , , , , , and . The currently available tools for these patient categories are summarised in Table 5.
3.5. Limitations of existing models
Although the existing models provide an evidence-based approach to various clinical scenarios in the management of RP patients, several limitations persist. Lack of external validation for several of the existing tools represents the most important limitation, because it may limit the model's applicability to a population of patients different from that used for its development  and . Moreover, lack of periodic updates that reflect changes in patient and disease characteristics may undermine the applicability of some models to contemporary patient populations. For example, some of the preoperative nomograms addressing GSU rely on a sextant prostatic biopsy population, which is now considered obsolete .
Predictive and prognostic models remain imperfect in their discrimination properties and in their calibration characteristics. Therefore, the use of novel, promising predictive or prognostic variables is required to reduce the error margin of existing models  and . Several biomarkers have demonstrated a powerful ability to improve models’ predictions. For example, transforming growth factor-β1, interleukine-6–soluble receptor, plasminogen activator inhibitor type 1, human glandular kallicrein-2, or gene expression signatures significantly improved the ability of different BCR predictive models to discriminate , , , , and . Shariat et al demonstrated that a panel of blood-based biomarkers improved the preoperative prediction of BCR . Similarly, plasma endoglin improved predictions of LNI at RP .
Lack of consideration of competing risks represents another problem with some models, especially those that predict distant end points, such as long-term BCR, metastatic progression, or PCSM. Lack of consideration of competing risks may confound the association between clinical and pathologic predictors and the outcome of interest. Similarly, only a few tools that predict BCR, metastatic progression, or PCSM adjust for disease-free interval since RP , , and . Adjustment for disease-free interval should be made, and models that allow it may provide more realistic predictions of the outcome of interest.
Besides methodologic issues, clinicians may be unaware of how and when to apply specific predictive or prognostic tools . Recently, Nguyen and Kattan addressed this issue and systematically organised the currently available PCa prediction tools within a metagram, which represents a framework on which existing PCa prediction tools are organised and stratified by their accuracy, quality, and usefulness in a clinical setting . The metagram showed that there is a great need for additional models as well as improvement in the accuracy of existing tools. Moreover, the existing nomograms may not provide adequate flexibility when it comes to predictor variables. For example, clinicians may not dispose of all the variables that were used to devise a specific model. Therefore, improvements in the accessibility and flexibility of predictive and prognostic tools are still needed to further promote their use in everyday clinical practice.
Finally, it deserves mention that predictive models reduce the amount of averaging. Nevertheless, they are based on averages of outcomes in patients with specific baseline risk factors. In addition, to the best of our knowledge, no tools are capable of quantifying the benefit of RP relative to other treatment modalities, such as active surveillance, EBRT, or focal therapy. Although prediction tools may help clinicians in choosing the best treatment option by stratifying the risk of harbouring a specific outcome (eg, Gleason score upgrading or PCa stage), no tools are capable of indicating the optimal therapy. Therefore, clinicians still have to carefully interpret the prediction tool results and discuss the risk–benefit ratio of any treatment type with the patient.
A recent international consensus conference on predictive modelling in PCa concluded that “nomograms are here to stay”. Moreover, despite the large number of available tools, additional models are still needed . Recently, Nguyen and Kattan observed that only 31 out of 160 possible treatment–outcome combinations are covered by existing models . However, the applicability of existing tools needs to be improved. Vickers et al suggested that predictive and prognostic models should be grouped into a unique platform to provide for the possibility of handling missing data and changing predictions with changing data or changing status of the patient and to provide better flexibility (ie, the possibility of generating predictions for more than one outcome) .
Similarly, the accessibility of the existing tools needs to be simplified. To date, only three Web sites (www.nomogram.org, www.nomograms.org, and www.clinicriskcalculators.org) offer a structured overview and easy access to several nomograms for prediction of numerous outcomes in PCa patients. In the future, personal digital assistant–based nomograms will likely complement Web-based tools and will further increase their accessibility and improve ease of use. Clearly, the optimal solution is for predictive models to be built into electronic medical records so that predictions are automatically generated from a patient's medical data.
Finally, a partially unanswered question remains as to whether nomograms improve patient outcomes or other indicators of quality of care. To date, no formal trials have addressed this question. However, data indicate that specific questions are answered more accurately if model predictions are used instead of even expert clinicians’ ratings , , and . Using decision curve analyses, Vickers et al recently demonstrated that the use of predictive tools resulted in better clinical outcomes both in PCa and bladder cancer  and .
Therefore, at least partial evidence supports the benefits of predictive or prognostic models relative to experience-based predictions. Based on this evidence, the last version of the European Association of Urology guidelines for PCa recommends the use of nomograms for predicting Gleason score, pathologic T and N stages, PCSM . Nonetheless, these data are scant, and more extensive proof continues to be required.
Study concept and design: Lughezzani, Karakiewicz.
Acquisition of data: Lughezzani.
Analysis and interpretation of data: Lughezzani, Karakiewicz.
Drafting of the manuscript: Lughezzani, Karakiewicz.
Critical revision of the manuscript for important intellectual content: Briganti, Karakiewicz, Kattan, Montorsi, Shariat, Vickers.
Statistical analysis: Lughezzani.
Obtaining funding: None.
Administrative, technical, or material support: None.
Other (specify): None.
Financial disclosures: I certify that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None.
Funding/Support and role of the sponsor: None.
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a Department of Urology, Vita-Salute San Raffaele University, Milano, Italy
b Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Centre, Montreal, Quebec, Canada
c Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA
d Department of Urology, Weill Medical College of Cornell University, New York, New York, USA
e Department of Statistics, Memorial Sloan-Kettering Cancer Centre, New York, New York, USA
Please visit www.eu-acme.org/europeanurology to read and answer questions on-line. The EU-ACME credits will then be attributed automatically.
© 2010 European Association of Urology, Published by Elsevier B.V.