Prostate Cancer Nomograms: An Update

1. Introduction 

The field of prostate cancer (PCa) prognostics has exploded in the last decade, and clinicians have been provided with numerous tools to assist with evidence-based medical decision-making [1]. Most of these “decision aids” consist of nomograms such as the Kattan nomogram of biochemical recurrence (BCR) after radical prostatectomy (RP) [2], [3], [4], [5], artificial neural networks (ANNs) that were pioneered by Snow et al. [6], probability tables such as the perhaps most widely known and applied Partin staging tables [7], and Classification and Regression Tree (CART) analyses, such as the Hamburg lymph node or side-specific extracapsular extension (SS-ECE) algorithms [8], [9]. These distinct models address various PCa outcomes that range from prediction of biopsy outcome in men considered at risk of PCa [10], [11], [12], [13], [14], [15], [16] through prediction of specific pathologic features [17], [18], [19], [20], [21], [22], [23], [24], such as the likelihood of Gleason upgrading between biopsy and RP pathology [18], to prediction of side-specific extracapsular extension [21] at RP and death from hormone-refractory PCa [25]. For some outcomes more than one model might be available, which makes model selection difficult.

Because of the overwhelming output in the field of PCa outcomes and prognostics as well as equally high predictive accuracy (PA) measures compared to ANNs and other machine learning methods and, most importantly, due to better comparability, in this update we decided to focus only on PCa probability nomograms that are based on traditional logistic regression and Cox regression analyses [26], [27], [28], [29].

2. Defining and reading nomograms 

Various distinct statistical methodologies have broadly been described as “nomograms.” However, the statistical definition of a nomogram applies to a specific functional representation that graphically displays prediction models using lines with numeric scales based on traditional statistical methods such as multivariable logistic regression analysis to predict a binary outcome or Cox regression analysis to predict a prognostic outcome [1], [29].

Fig. 1 displays an example of a nomogram predicting Gleason sum upgrading between biopsy and final pathology [18]. To obtain nomogram-predicted probability of biopsy upgrading, locate the patient values at each axis. Subsequently, draw a vertical line to the “Point” axis to determine how many points are attributed for each variable value. Then, sum the points for all variables. Locate the sum on the “Total Points” line to be able to assess the individual probability of biopsy Gleason sum upgrading.

Fig. 1. Nomogram predicting Gleason sum upgrading between biopsy and radical prostatectomy pathology. To obtain nomogram-predicted probability of biopsy upgrading, locate patient values at each axis. Draw a vertical line to the “Point” axis to determine how many points are attributed for each variable value. Sum the points for all variables. Locate the sum on the “Total Points” line to be able to assess the individual probability of biopsy Gleason sum upgrading on the “P(Upgrade)” line. PSA=prostate-specific antigen (ng/ml); BX Gleason Pri=primary biopsy Gleason score; BX Gleason sec=secondary biopsy Gleason score; P(Upgrade)=probability of biopsy Gleason sum upgrading.

The Loess calibration plot, which graphically explores the correspondence between nomogram-predicted probability and observed rate of Gleason sum upgrading between biopsy and final pathology is shown in Fig. 2 [18]. Its x-axis represents the nomogram-predicted probability, and its y-axis represents the observed rate of Gleason sum upgrading. Perfect predictions correspond to the 45° line.

Fig. 2. Calibration plot of a nomogram for prediction of biopsy Gleason sum upgrading, where the x-axis represents predicted probability and the y-axis represents observed fraction with evidence of upgrading between biopsy and final pathology. Perfect predictions correspond to the 45° line. Points estimated below the 45° line represent overprediction, whereas points situated above 45° line represent underprediction. A nonparametric, smoothed curve indicates the relationship between predicted probability and observed frequency of biopsy Gleason sum upgrading. Vertical lines indicate the frequency distribution of predicted probabilities.

3. Nomogram criteria 

It is important to note that the following criteria apply to nomograms and other prediction models and might be proposed, as follows:

Availability of several high-quality predictive models should encourage the clinician to adopt these tools into everyday clinical practice. Arguments favouring such behaviour include standardisation of care and of decision-making.

4. Nomogram limitations 

Despite their advantages, the limitations of nomograms must be acknowledged. Every nomogram depends on its development cohort. Therefore, it needs to be mentioned that most of the PCa nomograms are based on either single-centre series or data from tertiary care referral centres or both [2], [3], [4], [5], [18].

5. Clinical value of nomograms 

Controversy surrounds the question of the clinical value of nomograms. Studies have shown that nomograms predict more accurately than clinicians [44], [45]. Thus, it appears that nomograms have a better ability to predict the outcome of interest than even expert clinicians. It is conceivable that the advantage related to the use of nomogram predictions may be even more important if clinical ratings were obtained from less expert clinicians. In breast cancer, nomogram prediction clearly outperformed clinical judgement (72% vs. 54%) where 50% equals a flip of a coin and 100% represents perfect prediction. [44]. In the field of PCa, Ross et al. [45] showed that urologist predictions of BCR after RP were inferior to the nomogram (concordance index decreased from 67% to 55%, −12%, p<0.05). Increases in accuracy related to the use of nomograms may not only be of statistical significance but more importantly, they may be clinically meaningful. For example, an increase in accuracy of 12% translates into 120 men of 1000 patients who are provided with accurate predictions when the nomogram is used [44]. This figure needs then to be extrapolated to the disease prevalence and subsequently to the number of diagnostic or therapeutic procedures. Thus, from a health economic, medical, and personal standpoint, a small increase in predictive accuracy (PA) translates into a clinically important number of patients who are being provided with accurate predictions. However, criteria for selecting a model need to be considered. For example, patients who received neoadjuvant hormonal therapy are excluded in most of the models. Consequently, those patients cannot be subjected to models where those patients were excluded [18].

6. Patient perspective 

Patients are becoming increasingly aware of the existence of predictive tools. This trend is likely to increase in the future. Patients are also increasingly demanding to actively participate in decision-making, which may, in part, be explained by the following observations:

Thus, it may be postulated that greater emphasis will be placed on standardised predictions, which will further promote the development of new tools or the improvement of existing predictive tools. These considerations may motivate clinicians to adopt the use of decision tools. Their motivation may also stem from the wealth of clinical data used for the development and validation of each model. Most decision tools are based on thousands of observations, and it is virtually impossible to achieve that level of clinical exposure and expertise on an individual basis. Moreover, most clinicians do not have the capacity to systematically record or remember the risk characteristics of thousands of patients. Additionally, unlike computers, clinicians are incapable of systematically and cumulatively processing the recorded risk characteristics and outcomes of historic cases and to derive an estimated probability of outcome for a new case at hand. Thus, it may be expected that the majority of physician-derived estimates are not as accurate as computer-derived decision models [44], [45]. Despite this advantage, decision tools are not meant to replace clinical judgement. The input from clinicians needs to be weighed against the pros and cons of several other considerations, such as comorbidity, cost, and social, religious, or emotional considerations.

The above criteria are meant to provide guidelines for the process of decision aid selection. However, a list of hypothetical criteria might not appeal to clinicians and insecurity may persist in choosing a reliable decision aid. To address this issue, we provide an organised update of PCa nomograms. Moreover, we recorded predictor variables, the outcome of interest, and the number of patients who were used to develop the nomogram, nomogram-specific features as well as their accuracy estimates, and whether some kind (either internal or external) of validation has been performed.

7. Prediction of biopsy outcome 

Prediction tools are needed to assist with the identification of those at highest risk of harbouring PCa on either initial, repeat, or saturation biopsy [10], [11], [12], [14], [15], [16], [46]. Table 1 displays these efforts within the initial biopsy setting. However, since the late 1990s, an extended biopsy scheme represents the standard of care in the early detection of PCa. This scheme consists of at least 10 biopsy cores and increases the detection rate by 30% relative to the sextant scheme [16]. This trend suggests that nomograms developed in the sextant biopsy era may not be able to predict the probability of PCa on needle biopsy in the extended biopsy era equally as accurately as they did in the sextant biopsy era. Based on this assumption, many clinicians are reluctant to use tools that were developed in the sextant biopsy era [10], [11], [12], [13].

Table 1. Initial biopsy nomograms predicting a positive outcome

	Reference	Type of study	n	Predictors	Number of cores	Accuracy	Validation
	Eastham et al. [10]	Nomogram development	700	Age, race, DRE, PSA (0–4ng/ml)	6	75%	Internal
	Garzotto et al. [11]	Nomogram development	1239	Age, race, family history, referral indications, prior vasectomy, DRE, PSA (≤10ng/ml), TRUS findings	≥6	73%	Not performed
	Karakiewicz et al. [12]	Nomogram development	1762	Age, DRE, PSA, % fPSA	6	78%	Internal and external

DRE=digital rectal examination; PSA=prostate-specific antigen; TRUS=transrectal ultrasound; fPSA=free prostate-specific antigen.

Within the repeat biopsy setting, PCa detection rates are as high as 30% and continue to remain elevated at subsequent biopsy sessions, as evidenced by positive biopsy rates of 13–35% at saturation biopsy [14], [15]. However, despite elevated repeat biopsy rates, not all men are at an equally high risk of having PCa after one or several previously negative biopsy sessions. A repeat nomogram was 71% accurate (Table 2) in an external validation [14], [15].

Table 2. Repeat biopsy nomograms predicting a positive outcome

	Reference	Type of study	n	Predictors	Biopsy sessions	No. of cores	Accuracy	Validation
	Lopez-Corona et al. [14]	Nomogram development	343	Age, DRE, negative cores previously taken, history of HGPIN, history of ASAP, PSA, PSA slope, family history, months from initial negative biopsy	2.9 (2–12)	9.2 (6–22)	70%	Internal
	Yanke et al. [15]	Nomogram validation	230	Age, DRE, negative cores previously taken, history of HGPIN, history of ASAP, PSA, PSA slope, family history, months from initial negative biopsy	2.6 (2–7)	17.9 (12–54)	71%	–

DRE=digital rectal examination; HGPIN=high-grade intraepithelial neoplasia; ASAP=atypical small acinar proliferation of prostate; PSA=prostate-specific antigen.

Further, Walz et al. recently reported an internally validated nomogram that was 70% accurate (Table 3) [16]. However, one may argue that a nomogram to predict repeat or saturation biopsy outcome consisting of nine rather complex predictor variables may not be directly applicable to a busy clinical practice.

Table 3. Saturation biopsy nomograms predicting a positive outcome

	Reference	Type of study	n	Predictors	No. of cores	Accuracy (%)	Validation
	Walz et al. [16]	Nomogram development	161	Age, PSA, % fPSA, prostate and BPH volume, PSAD, TZD, number of previous biopsy sessions, number of cores at saturation biopsy	24.5 (20–32)	70%	Internal

PSA=prostate-specific antigen; fPSA=free prostate-specific antigen; BPH=benign prostatic hyperplasia; PSAD=prostate-specific antigen doubling; TZD=transition zone density.

8. Prediction of specific pathologic features of clinically localised prostate cancer 

As experienced by the wide use of the Partin probability tables, prediction of pathologic features has a significant impact on choosing an adequate treatment modality [7], [46]. Nomograms that predict specific pathologic features are shown in Table 4.

Table 4. Prediction of specific pathologic features of a clinically localised prostate cancer

	Reference	Type of study	Outcome	n	Predictors	Accuracy	Validation
	Kattan et al. [19]	Nomogram development	Clinically insignificant prostate cancer defined as pT2, <0.5 cc, no high-grade cancer	409	PSA, primary and secondary biopsy Gleason score	64%	Internal
					PSA, primary and secondary biopsy Gleason score, % positive cores, TRUS volume	74%
					PSA, clinical stage, primary and secondary biopsy Gleason score, TRUS volume, mm cancer, mm noncancer	79%
	Chun et al. [20]	Nomogram development	Gleason upgrading between biopsy and final pathology	2982	PSA, clinical stage, primary and secondary biopsy Gleason score	80%	Internal
	Chun et al. [21]	Nomogram development	Significant Gleason upgrading between biopsy and final pathology	4789	PSA, clinical stage, biopsy Gleason sum	76%	Internal
	Ohori et al. [22]	Nomogram development	SS-ECE	763	Side-specific predictor variables such as PSA, clinical stage, biopsy Gleason sum, percent positive cores, and percent of cancer in positive cores	81%	Internal
	Steuber et al. [23]	Nomogram development	SS-ECE	1118	Side-specific predictor variables such as PSA, clinical stage, biopsy Gleason sum, percent positive cores, and percent of cancer in positive cores	84%	Internal
	Koh et al. [24]	Nomogram development	SVI	763	PSA, clinical stage, primary and secondary Gleason score, and percent of cancer at the base	88%	Internal
	Cagiannos et al. [25]	Nomogram development	LNI assessed with limited PLND	5510	PSA, clinical stage, biopsy Gleason sum (+ institution)	76% (78%)	Internal
	Briganti et al. [26]	Nomogram development	LNI assessed with extended PLND	602	PSA, clinical stage, biopsy Gleason sum	76%	internal

PSA=prostate-specific antigen; TRUS=transrectal ultrasound; SS-ECE=side-specific extracapsular extension; SVI=seminal vesicle invasion; LNI=lymph node invasion; PLND=pelvic lymphadenectomy.

9. Prediction of biochemical recurrence following radical prostatectomy 

Like the Partin tables [7], the Kattan nomograms [2], [3], [4], [5] are the most widely known and applied nomograms (Table 5). These nomograms represent robust and highly generalisable models, which have been externally validated in multiple data sets of RP patients across the world and excelled in equally high PA measures. Kattan et al. constructed two distinct nomograms using either preoperative [2] or postoperative [4] variables to predict BCR after RP. They used a PSA threshold of 0.4ng/ml and rising or initiation of adjuvant therapy to define BCR.

Table 5. Prediction of biochemical recurrence after radical prostatectomy

	Reference	Type of study	Predictors	No. of patients	Length of predicted PSA-free survival	Accuracy	Validation
	Kattan et al. [2]	Nomogram development and external validation	PSA, clinical stage, biopsy Gleason sum	983	60 mo	74%	Internal and external
				168		79%
	Stephenson et al. [3]	Nomogram development and external validation	PSA, clinical stage, biopsy Gleason sum, year of surgery, number of positive and negative cores	1978	120 mo	76%	Internal and external
				1545		79%
	Graefen et al. [33]	Nomogram validation	PSA, clinical stage, biopsy Gleason sum	6232	60 mo	75%	–
	Greene et al. [34]	Nomogram validation	PSA, clinical stage, biopsy Gleason sum	1701 (BCR defined as ≥0.2ng/ml)	60 mo	68%	–
				1701 (BCR defined as ≥0.4ng/ml)		71%
	Kattan et al. [4]	Nomogram development and external validation	PSA, Gleason sum, ECE, +SM, SVI, LNI	996	84 mo	88%	Internal and external
				332		89%
	Stephenson et al. [5]	Nomogram development and 2 external validations	PSA, Gleason sum, ECE, +SM, SVI, LNI	1881	120 mo	86%	Internal and external
				1782		81%
				1357		78%
	Graefen et al. [35]	Nomogram validation	PSA, Gleason sum, ECE, +SM, SVI, LNI	2465	84 mo	80%	–
	Bianco et al. [36]	Nomogram validation stratified by race: black American males (AAM) vs. white American males (CAM)	PSA, clinical stage, biopsy Gleason sum	1039		AAM: 74%	–
						CAM: 78%
			PSA, Gleason sum, ECE, +SM, SVI, LNI		84 mo	AAM:83%
						CAM:85%

PSA=prostate-specific antigen; BCR=biochemical recurrence; ECE=extracapsular extension; +SM=positive surgical margins; SVI=seminal vesicle invasion; LNI=lymph node invasion.

The preoperative nomogram was originally 74% accurate after internal validation [2]. Graefen et al. [33] and Greene et al. [34] externally validated this preoperative nomogram in internationally assembled external cohorts consisting, respectively, of 6232 and 1701 patients. Additionally, in these validation studies, the authors tested different PSA recurrence definitions, institutional variability, and delivery of neoadjuvant therapy. PA did not seem to be substantially affected [33], [34].

Moreover, Stephenson et al. most recently published a new preoperative BCR nomogram that is adjusted for the year of surgery (after 2003) and includes more detailed biopsy information such as the number of positive and negative cores [3]. This new nomogram is internally and externally validated with a concordance index of 0.76 and 0.79, respectively, and extends BCR predictions up to 10 yr. It allows the clinician to estimate the probability of recurrence at any point in time from 1 to 10 yr after RP. However, its predictions are likely to be valid in regions where PSA screening is widespread. Thus, for European patients, the original preoperative nomogram is better suited than this new nomogram [3].

The postoperative Kattan nomogram [4] was originally developed and internally validated in 996 patients (88% accurate). In the original report, Kattan et al. also reported an external validation in 322 men (89% accurate). This postoperative nomogram predicted a 7-yr BCR-free probability. Again, this nomogram was externally validated in a large international cohort. Graefen et al. [35] tested its PA in 2465 men from four institutions and three continents. The PA ranged from 77% to 82%, substantiating its reliability and accuracy.

It has been argued that the Kattan nomograms were developed in a patient population in which the majority of men were white. Bianco et al. [36] tested both nomograms in an African American patient population. The concordance indices for both nomograms were comparable to a cohort of white American men. Consequently, the Kattan nomograms can be accurately applied to an individual regardless of race.

In the field of PCa prognostics, follow-up is pivotal. Therefore, Stephenson et al. [5] recently updated the postoperative nomogram by extending the length of BCR predictions to 10 yr. This nomogram was developed in 1881 men and was 86% accurate. External validation was reported and was 81% and 79% accurate, respectively, in two distinct external validation cohorts.

10. Prediction of biochemical recurrence following radiation therapy 

Similarly, pretreatment BCR nomograms for patients treated with permanent brachytherapy or three-dimensional conformal radiation therapy [47], [48] were developed (Table 6). Both nomograms were developed in rich data sets consisting of approximately 1000 patients. The length of predicted PSA-free survival was 60 mo and both nomograms were externally validated. However, the PA of the brachytherapy nomogram was relatively low (61% and 64%). Several explanations may be advanced, including the retrospective nature of the data assessment and limited variability of the predictors in part due to the use of the 1997 TNM classification. Moreover, PSA measurements were not performed in a central laboratory and multiple pathologists contributed to the Gleason grading [47]. Taken together, data quality represents a limitation of any nomogram, possibly affecting its performance. However, brachytherapy nomogram predictions were better than pure chance agreement and currently represent the most accurate predictions available. Conversely, predictions of PSA recurrence in the three-dimensional conformal radiation nomogram are highly accurate (73%) and reliable as substantiated by an external validation (76%) [48].

Table 6. Prediction of biochemical recurrence after radiation therapy

	Reference	Type of study	Treatment	n	Predictors	Length of predicted PSA-free survival	Accuracy	Validation
	Kattan et al. [47]	Nomogram development	Permanent brachytherapy; (BCR defined according to ASTRO consensus definition)	920	PSA, biopsy Gleason sum, clinical stage, delivery of XRT (yes vs. no)	60 mo	–	External
		and		1827			61%
		2 external validations		765			64%
	Kattan et al. [48]	Nomogram development	3-dimensional conformal radiation therapy (BCR defined according to ASTRO consensus definition)	1042	PSA, clinical stage, biopsy Gleason sum, radiation dose, delivery of hormonal therapy (yes vs. no)	60 mo	73%	Internal and external
		and		912			76%
		External validation

BCR=biochemical recurrence; ASTRO=; PSA=prostate-specific antigen; XRT=external radiation therapy.

11. Prediction of metastasis following external-beam radiation 

Kattan et al. provided a highly accurate (81%), externally validated nomogram to predict metastases following three-dimensional conformal radiation therapy [49]. This nomogram addresses a clinically useful end point and may prove highly useful for counselling patients and for selecting patients for prospective clinical trials (Table 7).

Table 7. Prediction of distant metastasis after three-dimensional conformal radiation therapy

	Reference	Type of study	n	Predictors	Outcome	Length of predicted metastasis-free survival	Accuracy	Validation
	Kattan et al. [49]	Nomogram development and	1677	PSA, clinical stage, biopsy Gleason sum	Metastasis-free survival	60 mo
		External validation	1626				81%	External

PSA=prostate-specific antigen.

12. Prediction of metastasis following radical prostatectomy 

RP represents the mainstay for the treatment of localised PCa [46]. However, approximately 30% of men treated with RP for clinically localised PCa will experience BCR within 10 yr of surgery. Therefore, prediction of BCR represents a more important end point than prediction of specific pathologic features of the RP specimen. Conversely, local or distant metastases as well as PCa-specific survival with accounting for competing risks after initial treatment represent more important end points than BCR (Table 8).

Table 8. Prediction of distant metastasis

	Reference	Type of study	n	Predictors	Outcome	Accuracy	Validation
	Dotan et al. [38]	Nomogram development	239	Pretreatment PSA, surgical margin status, seminal vesicle invasion, RP Gleason sum, trigger PSA, extracapsular extension, PSA slope, and PSA velocity	Positive bone scan (distant metastasis)	93%	Internal
	Slovin et al. [39]	Nomogram development	148	Baseline PSA, PSA DT, pathologic T stage, Gleason sum	12-24 mo for metastasis-free survival	69%	Not performed

In the Slovin study, the outcome was the length of predicted distant metastasis-free survival. PSA=prostate-specific antigen; RP=radical prostatectomy; PSA DT=prostate-specific antigen doubling time.

In 239 patients with evidence of BCR following RP and no adjuvant treatment, Dotan et al. addressed the prediction of bone metastases as a binary outcome. They developed an internally validated nomogram that was 93% accurate [38].

Similarly, Slovin et al. [39] attempted to predict the time to radiographically detectable metastases in 148 patients who were initially treated with surgery or radiation therapy, with evidence of BCR and a PSA doubling time (PSA DT) of <12 mo. Based on a Cox regression model, the nomogram achieved a PA of 69%. This nomogram predicted a 1- to 2-yr probability of metastases-free survival. However, validation was not performed to confirm its performance.

13. Prediction of life expectancy 

Patients diagnosed with a clinically localised PCa may be offered an array of different treatment procedures. Life expectancy plays a crucial role in choosing the adequate treatment option [46]. The group of Kattan [40] recently published a nomogram predicting 5-, 10-, and 15-yr and the median life expectancy in men diagnosed with PCa between 1987 and 1989 (Table 9). The authors compared their model to those of Tewari et al. [41] and Albertson et al. [42]. The reported PA of the nomogram versus the Tewari and Albertson models was 73% versus 71% versus 70%, respectively, indicating a moderate degree of accuracy. However, limitations apply to the single-institution nature of the patient cohort, the relevance of men treated between 1987 and 1989 to contemporary patients, and the interpretation of the treatment effect within the nomogram [40]. Despite its limitations, this report clearly demonstrates the impact of the nomogram methodology on outcomes research.

Table 9. Prediction of life expectancy

	Reference	Type of study	n	Predictors	Length of predicted life expectancy	Accuracy (%)	Validation
	Cowen et al. [40]	Nomogram development	133	Age, Charlson performance index, presence of angina, systolic blood pressure, body mass index, smoking and marital status, PSA, Gleason sum, clinical stage, type of treatment (surgery vs. radiation vs. other)	5–15 yr, median survival	73%	Internal

PSA=prostate-specific antigen.

14. Prediction of survival 

14.1. Androgen-independent survival 

Svatek et al. developed a highly accurate, internally validated pretreatment nomogram to predict PCa-specific mortality up to 5 yr in men with an androgen-independent PCa (AIPCa) as shown in Table 10. The bootstrap-corrected PA of the nomogram was 80.9% [25].

Table 10. Prediction of androgen-independent prostate cancer survival

	Reference	Type of study	n	Predictors	Length of predicted AIPCa survival	Accuracy	Validation
	Svatek et al. [25]	Nomogram development	129	PSA at ADT initiation, PSA DT, nadir PSA on ADT, time from ADT to AIPCa diagnosis	12–60 mo	81%	Internal

PSA=prostate-specific antigen; ADT=androgen-deprivation therapy; PSA DT=prostate-specific antigen doubling time; AIPCa=androgen-independent prostate cancer.

14.2. Overall survival in progressive metastatic prostate cancer after castration 

It is highly beneficial for the individual and for the clinician to know the most accurate prognosis of patients with progressive metastatic PCa. Potential clinical trials and treatment may be adjusted accordingly [46]. Smaletz et al. reported a nomogram on overall survival in progressive metastatic disease [43] (Table 11). The nomogram was validated in an external cohort demonstrating a PA of 67%.

Table 11. Prediction of overall survival of men with progressive metastatic prostate cancer after castration

	Reference	Type of study	n	Predictors	Length of predicted AIPCa survival	Accuracy	Validation
	Smaletz et al. [43]	Nomogram development	409	Age, Karnofsky performance index, hemoglobin, PSA, LDH, alkaline phosphatase, albumin	12–24 mo, median survival	71%	Internal and external
		and	433			67%
		External validation

PSA=prostate-specific antigen; LDH=lactic dehydrogenase.

15. Nomograms in the future 

Management of PCa is constantly progressing and improving, and the same applies to outcomes research. Specifically, the emerging field of molecular marker research may substantially improve nomogram predictions. This was pioneered by Kattan et al. who combined the molecular markers interleukin 6 (IL-6) and transforming growth factor β1 (TGF-β1) with traditional clinical markers to develop a nomogram that would predict PSA recurrence after RP. The authors were able to display an 8% increase in PA related to the inclusion of IL-6 and TGF-β1 [50]. Thus, in the future, expectations of more accurate and specific PCa nomograms may be justified.

16. Conclusion 

In this update, we provide an overview, theoretical and critical considerations, and limitations of current PCa nomograms.

Editorial Comment

Michael Fröhner

In recent years, nomograms have gained popularity for predicting outcomes in prostate cancer. Dr Chun and colleagues are to be commended for providing a comprehensive and competent review of this rapidly evolving field.

Although many parameters may be predicted accurately by the prognostic tools reviewed in this article, some pertinent questions remain to be answered. Relatively few data are available to support the estimation of mortality, particularly at the time of diagnosis of early prostate cancer. Given no neoadjuvant or adjuvant treatment, prostate-specific antigen (PSA) relapse after radical prostatectomy may be reasonably well predicted by prognostic tables or nomograms. The relationship between PSA failure and death from prostate cancer is, however, not straightforward [1], [2]. Possibly, PSA velocity might be a more meaningful end point than simple PSA increase over threshold [1]. Predicting competing mortality is another critical problem. In relatively healthy candidates for curative treatment of early prostate cancer, competing mortality risk assessment studies require long-term follow-up, making the investigations troublesome and the results not necessarily applicable to contemporary patients [3].

Considering these uncertainties, the course of early prostate cancer, particularly, remains difficult to predict despite the multitude of available nomograms.

References 

[1]. [1]Ward JF, Moul J. Biochemical recurrence after definitive prostate cancer therapy. Part I: defining and localizing biochemical recurrence of prostate cancer. Curr Opin Urol. 2005;5:181–186. CrossRef

[2]. [2]D’Amico AV, Moul J, Carroll PR, Sun L, Lubeck D, Chen MH. Cancer-specific mortality after surgery or radiation for patients with clinically localized prostate cancer managed during the prostate-specific antigen era. J Clin Oncol. 2003;21:2163–2173. CrossRef

[3]. [3]Cowen ME, Halasyamani LK, Kattan MW. Predicting life expectancy in men with clinically localized prostate cancer. J Urol. 2006;175:99–103. Abstract | Full Text | Full-Text PDF (104 KB) | MEDLINE | CrossRef