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Article of the Month: ERSPC and PCPT risk calculators in prostate cancer risk prediction

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Every Month the Editor-in-Chief selects an Article of the Month from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

If you only have time to read one article this week, it should be this one.

Prostate cancer risk prediction using the novel versions of the European Randomised Study for Screening of Prostate Cancer (ERSPC) and Prostate Cancer Prevention Trial (PCPT) risk calculators: independent validation and comparison in a contemporary European cohort

Cedric Poyet, Daan Nieboer*, Bimal Bhindi†, Girish S. Kulkarni†, Caroline Wiederkehr, Marian S. Wettstein, Remo Largo, Peter Wild‡, Tullio Sulser and Thomas Hermanns 

 

Department of Urology, University Hospital Zurich, University of Zurich, Zurich, Switzerland, *Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands, †Division of Urology, Department of Surgery, University Health Network, University of Toronto, Toronto, ON, Canada, and ‡Institute of Surgical Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland

 

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Objectives

To externally validate and compare the two novel versions of the European Randomised Study for Screening of Prostate Cancer (ERSPC)-prostate cancer risk calculator (RC) and Prostate Cancer Prevention Trial (PCPT)-RC.

Patients and Methods

All men who underwent a transrectal prostate biopsy in a European tertiary care centre between 2004 and 2012 were retrospectively identified. The probability of detecting prostate cancer and significant cancer (Gleason score ≥7) was calculated for each man using the novel versions of the ERSPC-RC (DRE-based version 3/4) and the PCPT-RC (version 2.0) and compared with biopsy results. Calibration and discrimination were assessed using the calibration slope method and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, decision curve analyses were performed.

MarchATOM1

Results

Of 1 996 men, 483 (24%) were diagnosed with prostate cancer and 226 (11%) with significant prostate cancer. Calibration of the two RCs was comparable, although the PCPT-RC was slightly superior in the higher risk prediction range for any and significant prostate cancer. Discrimination of the ERSPC- and PCPT-RC was comparable for any prostate cancer (AUCs 0.65 vs 0.66), while the ERSPC-RC was somewhat better for significant prostate cancer (AUCs 0.73 vs 0.70). Decision curve analyses revealed a comparable net benefit for any prostate cancer and a slightly greater net benefit for significant prostate cancer using the ERSPC-RC.

Conclusions

In our independent external validation, both updated RCs showed less optimistic performance compared with their original reports, particularly for the prediction of any prostate cancer. Risk prediction of significant prostate cancer, which is important to avoid unnecessary biopsies and reduce over-diagnosis and overtreatment, was better for both RCs and slightly superior using the ERSPC-RC.

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Editorial: Prostate cancer risk prediction and the persistence of uncertainty

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Poyet et al. [1] have performed the largest external validation of the European Randomised Study for Screening of Prostate Cancer (ERSPC) and Prostate Cancer Prevention Trial (PCPT) v2.0 risk calculators (RCs) to date, having retrospectively identified 1996 men undergoing prostate biopsy in a Swiss tertiary care facility.

Asides from the validatory nature of this paper [1], there are several other findings though less novel, which are further important additions to the urological literature.

This study confirms the superior discriminative performance of multi-factorial RCs over PSA alone in the assessment of prostate cancer: where the area under the receiver operating characteristic curve (AUC) for the prediction of significant prostate cancer for PSA alone was 0.65, comparing less favourably than 0.73 and 0.70 for the ERSPC and PCPT v2.0 RCs, respectively.

The authors performed sensitivity analysis showing higher detection rates for prostate cancer (29.4% vs 18.1%) and significant prostate cancer (15.9% vs 5.9%) in patients receiving a 12-core biopsy than in those receiving a 6–8 core biopsy.

Supplementary analysis by the authors evaluated the performance of previous versions of the PCPT-RC, specifically v1.0 and PCPT-RC v1.0 with prostate volume. The inclusion of prostate volume demonstrated an improved predictive ability of this RC. The AUC for the prediction of significant prostate cancer using the PCPT-RC v1.0 with prostate volume was 0.74. This contrasts with the ERSPC risk tool: AUC of 0.73 (which includes a trichotomised estimation of prostate volume), and the novel PCPT-RC v2.0; AUC of 0.70 (which does not include prostate volume as a factor).

The authors conclude that the prediction of significant prostate cancer was superior using the ERSPC-RC compared with the PCPT-RC v2.0, in risk thresholds of 8–35%. Their data also shows that the PCPT-RC v2.0 offers a superior net benefit to the ERPSC-RC to a large number of men outside of this range of threshold probabilities. Their findings suggest that the older PCPT-RC v1.0 with prostate volume may offer benefits superior to both the ERSPC and PCPT v2.0 RCs.

The authors assessment of novel risk tools confirms the rationale for guidelines and consensus statements that PSA testing should not be considered on its own, but rather as part of a multivariate approach [2, 3]. This current work suggests that although calibration of risk tools is still not optimal, they offer superior discriminative ability and superior net benefit in identifying patients with significant prostate cancer. This work affirms the role for variables such as DRE, and the importance of prostate volume in addition to PSA in prostate cancer assessment.

Although further refinement of risk tools is necessary, this work encourages confidence in and should garner further traction for the routine use of such tools in the assessment and counselling of patients before prostate biopsy.

Read the full article
Dara J. Lundon*†
*Conway Institute of Biomedical and Biomolecular Science, University College Dublin School of Medicine and Medical Sciences, University College Dublin, Belfield, and †Department of Urology, Mater Misericordiae University Hospital, Dublin, Ireland

 

References

 

1 Poyet C, Nieboer D, Bhindi B et al. Prostate cancer risk prediction using the novel versions of the European Randomised Study for Screening of Prostate Cancer (ERSPC) and Prostate Cancer Prevention Trial (PCPT) risk calculators: independent validation and comparison in a contemporary Europe. BJU Int 2016; 117: 401–8

 

2 Murphy DG, Ahlering T, Catalona WJ et al. The Melbourne Consensus Statement on the early detection of prostate cancer. BJU Int 2014; 113: 186–8

 

3 Heidenreich A, Abrahamsson PA, Artibani W et al. Early detection of prostate cancer: European Association of Urology recommendation. Eur Urol 2013; 64: 347–54

 

Article of the week: Nomogram helps the preoperative prediction of early biochemical recurrence after radical prostatectomy

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Every week the Editor-in-Chief selects the Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying editorial written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

Finally, the third post under the Article of the Week heading on the homepage will consist of additional material or media. This week we feature a video of Ángel Borque discussing his paper.

If you only have time to read one article this week, it should be this one.

Genetic predisposition to early recurrence in clinically localized prostate cancer

Ángel Borque, Jokin del Amo†, Luis M. Esteban*, Elisabet Ars§, Carlos Hernández**, Jacques Planas, Antonio Arruza††, Roberto Llarena‡, Joan Palou§, Felipe Herranz**, Carles X. Raventós, Diego Tejedor†, Marta Artieda†, Laureano Simon†, Antonio Martínez†, Elena Carceller, Miguel Suárez, Marta Allué, Gerardo Sanz* and Juan Morote

‘Miguel Servet’ University Hospital, *University of Zaragoza, Zaragoza, Spain, †Progenika Biopharma S.A., ‡University Hospital of Cruces, Bilbao, §Puigvert Foundation, ‘Vall d’Hebron’ University Hospital, Barcelona, **‘Gregorio Marañón’ University Hospital, Madrid, and ††Hospital of Txagorritxu, Vitoria, Spain

Read the full article
OBJECTIVES

• To evaluate genetic susceptibility to early biochemical recurrence (EBCR) after radical prostatectomy (RP), as a prognostic factor for early systemic dissemination.

• To build a preoperative nomogram to predict EBCR combining genetic and clinicopathological factors.

PATIENTS AND METHODS

• We evaluated 670 patients from six University Hospitals who underwent RP for clinically localized prostate cancer (PCa), and were followed-up for at least 5 years or until biochemical recurrence.

• EBCR was defined as a level prostate-specific antigen >0.4 ng/mL within 1 year of RP; preoperative variables studied were: age, prostate-specific antigen, clinical stage, biopsy Gleason score, and the genotype of 83 PCa-related single nucleotide polymorphisms (SNPs).

• Univariate allele association tests and multivariate logistic regression were used to generate predictive models for EBCR, with clinicopathological factors and adding SNPs.

• We internally validated the models by bootstrapping and compared their accuracy using the area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, calibration plots and Vickers’ decision curves.

RESULTS

• Four common SNPs at KLK3, KLK2, SULT1A1 and BGLAP genes were independently associated with EBCR.

• A significant increase in AUC was observed when SNPs were added to the model: AUC (95% confidence interval) 0.728 (0.674–0.784) vs 0.763 (0.708–0.817).

• Net reclassification improvement showed a significant increase in probability for events of 60.7% and a decrease for non-events of 63.5%.

• Integrated discrimination improvement and decision curves confirmed the superiority of the new model.

CONCLUSIONS

• Four SNPs associated with EBCR significantly improved the accuracy of clinicopathological factors.

• We present a nomogram for preoperative prediction of EBCR after RP.

 

Read Previous Articles of the Week

Editorial: Prostate cancer families – predicting disease before and after the radical

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In this issue of BJUI, Borque et al. discuss a subject that is now very close to my heart. Aged 48 years, I am 6 weeks post radical prostatectomy for a Gleason 3 + 4 prostate adenocarcinoma measuring ~2 mL in volume, with a PSA level of 2.54 ng/mL. Histology reassures me it is organ confined and seminal vesicle negative. My father and his brother both died aged 63 years of Gleason 10 prostate cancer and my brother is awaiting his radical prostatectomy in a few weeks. I have two sons, one of whom has asked me when he should be tested. Any prognostic information is going to help me advise my family.

In all, 85% of prostate cancers appear to be sporadic. The incidence of all prostate cancers is 1 in 8500 under the age of 40 years, rising to 1 in 15 at 60–69 years and 1 in 8 after that. The lifetime risk in the UK for all men is 8–10%.

The genetics of prostate cancer are confused by case clustering; the family members of men with a prostate cancer diagnosis seek out early advice from their physician resulting in detection of some clinically questionable cancers and an apparent higher incidence in certain families. These families do not necessarily have genetically determined prostate cancer.

The lifetime risk is altered dramatically by having two or more first-degree relatives with a diagnosis of prostate cancer; if the disease in the relative is identified before the age of 65 years the risk is increased further. Bratt suggests the risk rises from 15 to 20% when a single first-degree relative is diagnosed aged < 60 years. Zeegers et al., in a meta-analysis, have shown that diagnosing prostate cancer in a relative aged < 65 years increases the relative risk of having prostate cancer by 3.3, and having two first-degree relatives increases the relative risk by a factor of 5.1.

Analysis of a huge database from Sweden including data on 182 000 fathers and 3700 sons with prostate cancer suggest a standardised incidence ratio of 9.4 in men with a father and brother diagnosed with prostate cancer, with further analysis also showing unsurprisingly that the risk increases as an individual ages. Some true ‘prostate cancer families’ have been identified. These families have three or more relatives with prostate cancer often associated with a diagnosis at a young age, possibly with an increased tendency to an aggressive
phenotype; my uncle was 18 months from diagnosis to death from his disease, my father 4 years. In these families, the relative risk in male family members is 3.39 in those where the diagnosis of identified sufferers was made aged > 65 years, and 7.33 where the diagnosis is in men aged < 65 years. These risks which effectively give a lifetime risk in the individual of 45–50% are associated with carriage of a gene identified as increasing the prostate cancer risk. The best identified of these genes is the BRCA2 (breast cancer type 2 susceptibility protein) gene, which is associated with an increased risk of other cancers including breast, ovarian, gallbladder and pancreatic cancer, as well as malignant melanoma. This gene, carried in 1% of
Ashkenazi Jewish families, is associated with prostate cancer families in this population.

Now my prostate has been removed, I need to determine my chance of treatment failure. It would be interesting to know whether my genes and my single nucleotide polymorphisms (SNPs), which have almost certainly been responsible for me developing prostate cancer, can also predict my chance of developing early biochemical recurrence (EBCR) and the possibility of needing further treatment. In the Borque et al. article, I would appear on the first model (Fig. 1) to have a chance of ECBR of between 1 and 5%. This risk, according to this study, could increase to up to 30%, if I was to have four SNPs associated with prostate cancer (Fig. 2). Furthermore, we need to know whether identification of SNPs is any better than other possible predictors of EBCR and disease progression, such as the identification of lymphovascular invasion and tumour volume in the final specimen and the presence of extraprostatic extension, data not included in this study. Incidentally, I had no evidence of lymphovascular invasion.

The authors identify that this study needs repeating, particularly in a more ethnically diverse group (this study included Caucasian origin as an entry criterion), and we await longer term data to see how SNPs predict metastasis and prostate cancer-related death.

Jonathan M. Glass
Department of Urology, Guys & St Thomas’ Hospital Trust, London, UK

Read the full article

Video: Genetic predisposition to early recurrence in clinically localized prostate cancer

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Genetic predisposition to early recurrence in clinically localized prostate cancer

Ángel Borque, Jokin del Amo†, Luis M. Esteban*, Elisabet Ars§, Carlos Hernández**, Jacques Planas, Antonio Arruza††, Roberto Llarena‡, Joan Palou§, Felipe Herranz**, Carles X. Raventós, Diego Tejedor†, Marta Artieda†, Laureano Simon†, Antonio Martínez†, Elena Carceller, Miguel Suárez, Marta Allué, Gerardo Sanz* and Juan Morote

‘Miguel Servet’ University Hospital, *University of Zaragoza, Zaragoza, Spain, †Progenika Biopharma S.A., ‡University Hospital of Cruces, Bilbao, §Puigvert Foundation, ‘Vall d’Hebron’ University Hospital, Barcelona, **‘Gregorio Marañón’ University Hospital, Madrid, and ††Hospital of Txagorritxu, Vitoria, Spain

Read the full article

• To evaluate genetic susceptibility to early biochemical recurrence (EBCR) after radical prostatectomy (RP), as a prognostic factor for early systemic dissemination.

• To build a preoperative nomogram to predict EBCR combining genetic and clinicopathological factors.

PATIENTS AND METHODS

• We evaluated 670 patients from six University Hospitals who underwent RP for clinically localized prostate cancer (PCa), and were followed-up for at least 5 years or until biochemical recurrence.

• EBCR was defined as a level prostate-specific antigen >0.4 ng/mL within 1 year of RP; preoperative variables studied were: age, prostate-specific antigen, clinical stage, biopsy Gleason score, and the genotype of 83 PCa-related single nucleotide polymorphisms (SNPs).

• Univariate allele association tests and multivariate logistic regression were used to generate predictive models for EBCR, with clinicopathological factors and adding SNPs.

• We internally validated the models by bootstrapping and compared their accuracy using the area under the curve (AUC), net reclassification improvement, integrated discrimination improvement, calibration plots and Vickers’ decision curves.

RESULTS

• Four common SNPs at KLK3, KLK2, SULT1A1 and BGLAP genes were independently associated with EBCR.

• A significant increase in AUC was observed when SNPs were added to the model: AUC (95% confidence interval) 0.728 (0.674–0.784) vs 0.763 (0.708–0.817).

• Net reclassification improvement showed a significant increase in probability for events of 60.7% and a decrease for non-events of 63.5%.

• Integrated discrimination improvement and decision curves confirmed the superiority of the new model.

CONCLUSIONS

• Four SNPs associated with EBCR significantly improved the accuracy of clinicopathological factors.

• We present a nomogram for preoperative prediction of EBCR after RP.

Article of the Week: The New Partin Tables

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Every week the Editor-in-Chief selects the Article of the Week from the current issue of BJUI. The abstract is reproduced below and you can click on the button to read the full article, which is freely available to all readers for at least 30 days from the time of this post.

In addition to the article itself, there is an accompanying blog written by a prominent member of the urological community. This blog is intended to provoke comment and discussion and we invite you to use the comment tools at the bottom of each post to join the conversation.

Finally, the third post under the Article of the Week heading on the homepage will consist of additional material or media. This week we feature a video of John Eifler and Alan Partin discussing their paper.

If you only have time to read one article this week, it should be this one.

 

An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011

John B. Eifler, Zhaoyang Feng, Brian M. Lin, Michael T. Partin, Elizabeth B. Humphreys, Misop Han, Jonathan I. Epstein, Patrick C. Walsh, Bruce J. Trock, Alan W. Partin

Read the full article
OBJECTIVE

• To update the 2007 Partin tables in a contemporary patient population.

PATIENTS AND METHODS

The study population consisted of 5,629 consecutive men who underwent RP and staging lymphadenectomy at the Johns Hopkins Hospital between January 1, 2006 and July 30, 2011 and met inclusion criteria.

• Polychotomous logistic regression analysis was used to predict the probability of each pathologic stage category: organ-confined disease (OC), extraprostatic extension (EPE), seminal vesicle involvement (SV+), or lymph node involvement (LN+) based on preoperative criteria.

• Preoperative variables included biopsy Gleason score (6, 3+4, 4+3, 8, and 9–10), serum PSA (0–2.5, 2.6–4.0, 4.1–6.0, 6.1–10.0, greater than 10.0 ng/mL), and clinical stage (T1c, T2c, and T2b/T2c).

• Bootstrap re-sampling with 1000 replications was performed to estimate 95% confidence intervals for predicted probabilities of each pathologic state.

RESULTS

• The median PSA was 4.9 ng/mL, 63% had Gleason 6 disease, and 78% of men had T1c disease.

• 73% of patients had OC disease, 23% had EPE, 3% had SV+ but not LN+, and 1% had LN+ disease. Compared to the previous Partin nomogram, there was no change in the distribution of pathologic state.

• The risk of LN+ disease was significantly higher for tumors with biopsy Gleason 9–10 than Gleason 8 (O.R. 3.2, 95% CI 1.3–7.6).

• The c-indexes for EPE vs. OC, SV+ vs. OC, and LN+ vs. OC were 0.702, 0.853, and 0.917, respectively.

• Men with biopsy Gleason 4+3 and Gleason 8 had similar predicted probabilities for all pathologic stages.

• Most men presenting with Gleason 6 disease or Gleason 3+4 disease have <2% risk of harboring LN+ disease and may have lymphadenectomy omitted at RP.

CONCLUSIONS

• The distribution of pathologic stages did not change at our institution between 2000–2005 and 2006–2011.

• The updated Partin nomogram takes into account the updated Gleason scoring system and may be more accurate for contemporary patients diagnosed with prostate cancer.

Erratum:

A typographical error was identified in Table 2, for the cell corresponding to the probability for EPE in a man with clinical stage T1c, PSA >10, and biopsy Gleason 4+3. The cell should read “38 (32-45)” rather than “28 (32-45).” Also, in the third paragraph of the Results section, the fourth sentence should be changed to “In contrast, the predicted risk of LN+ is no more than 3% for T1c tumours with biopsy Gleason score <9 for an PSA below 10.”

Editorial: What have we learned from the Partin table update?

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The controversies surrounding a physician’s best treatment strategy advice to an individual patient with clinically localized prostate cancer create a continuing need for advanced statistics. Historically, the Partin tables [1] were one of the first statistical tools that physicians and patients found readily usable. The tables have been updated and always focused on prediction of pathologic stage from standard clinical variables. The next commonly cited/used tool was the Kattan nomogram [2] that carried the prediction the next step to the endpoint of biochemical relapse. By 2008, Shariat et al catalogued over 100 predictive tools published from 1966 to 2007 on various endpoints of prostate cancer [3].

 

 

 

What have we learned from this update of the Partin tables?

  1. The pre-operative grade distribution has shifted up slightly with no change in prostatectomy grade/stage distribution. The authors discuss possible causes such as changes in interpreting the Gleason scoring system, shifts in selection for surgery away from lower grade patients, and a possible plateau in stage migration.
  2. The tables have split off Gleason 3+4, 4+3, 8, and 9–10, and found the latter significantly more aggressive, while Gleason 4+3 and 4+4 are more similar. Gleason 9–10 must have a pattern 5 component >5% and may therefore have more aggressive biology. On the other hand, two cases of prostate cancer may have identical volumes of 4 pattern, but if one adds additional 3 pattern, that additional tumour foci paradoxically lowers the sum to 7, but perhaps not the risk of non-organ confined stage.
  3. In the past, the tables were commonly used to predict pT3 stage, with possible change in management away from surgery as that risk increased. Clearly the literature on surgery for higher risk disease has matured, and augmented by the adjuvant/salvage radiation literature such that it is less likely to use the tables for this reason any more. On the other hand, prediction of N1 disease for the purpose of omitting a lymph node dissection remains a useful tool. In this update, using a <2% cut-off you would essentially omit all node dissections in Gleason 6 with PSA < 10 and cT1c/cT2a, while continuing with a dissection for any dominant Gleason 4 pattern. It is noteworthy that this experience was largely based upon standard templates, and those advocating extended templates will find these N1 rates too low. Indeed, when our center adopted the extended template using a robotic technique, the N1 rate for high-risk disease was 39% and 9% for intermediate risk [4]. Moving forward, what tools do we need to provide useful statistics to our patients? Updating old tools with more contemporary patient cohorts is certainly a worthy exercise. Multicentre study based tools will be required for endpoints such as positive surgical margins, quality of life, biochemical recurrence, and other endpoints that may be significantly affected by the experience of the treating physician. Beyond this, the next step should be adaptive nomograms that update in real time rather than en masse every 4–5 years [5].

John W. Davis
Department of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

References
1 Eifler JB, Feng Z, Lin BM et al. An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011. BJU Int 2013; 111: 26–33
2 Kattan MW, Eastham JA, Stapleton AM et al. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst 1998; 90: 766–71
3 Shariat SF, Karakiewicz PI, Roehborn CG, Kattan MW. An updated catalog of prostate cancer predictive tools. Cancer 2008; 113: 3075–99
4 Davis JW, Shah JB, Achim M. Robot-assisted extended pelvic lymph node dissection (PLND) at the time of radical prostatectomy (RP): a video-based illustration of technique, results, and unmet patient selection needs. BJUI 2011; 108: 993–8
5 Vickers AJ, Fearn P, Scardino PT et al. Why can’t nomograms be more like Neflix? Urology 2010; 75: 511–3

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John Eifler and Alan Partin discuss their article

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An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011.

John B. Eifler, Zhaoyang Feng, Brian M. Lin, Michael T. Partin, Elizabeth B. Humphreys, Misop Han, Jonathan I. Epstein, Patrick C. Walsh, Bruce J. Trock and Alan W. Partin
James Buchanan Brady Urological Institute and the Department of Urology, Johns Hopkins Medical Institutions, Baltimore, MD, USA

Objective

  • To update the 2007 Partin tables in a contemporary patient population.

Patients and Methods

The study population consisted of 5,629 consecutive men who underwent RP and staging lymphadenectomy at the Johns Hopkins Hospital between January 1, 2006 and July 30, 2011 and met inclusion criteria.

  • Polychotomous logistic regression analysis was used to predict the probability of each pathologic stage category: organ-confined disease (OC), extraprostatic extension (EPE), seminal vesicle involvement (SV+), or lymph node involvement (LN+) based on preoperative criteria.
  • Preoperative variables included biopsy Gleason score (6, 3+4, 4+3, 8, and 9–10), serum PSA (0–2.5, 2.6–4.0, 4.1–6.0, 6.1–10.0, greater than 10.0 ng/mL), and clinical stage (T1c, T2c, and T2b/T2c).
  • Bootstrap re-sampling with 1000 replications was performed to estimate 95% confidence intervals for predicted probabilities of each pathologic state.

Results

  • The median PSA was 4.9 ng/mL, 63% had Gleason 6 disease, and 78% of men had T1c disease.
  • 73% of patients had OC disease, 23% had EPE, 3% had SV+ but not LN+, and 1% had LN+ disease. Compared to the previous Partin nomogram, there was no change in the distribution of pathologic state.
  • The risk of LN+ disease was significantly higher for tumors with biopsy Gleason 9–10 than Gleason 8 (O.R. 3.2, 95% CI 1.3–7.6).
  • The c-indexes for EPE vs. OC, SV+ vs. OC, and LN+ vs. OC were 0.702, 0.853, and 0.917, respectively.
  • Men with biopsy Gleason 4+3 and Gleason 8 had similar predicted probabilities for all pathologic stages.
  • Most men presenting with Gleason 6 disease or Gleason 3+4 disease have <2% risk of harboring LN+ disease and may have lymphadenectomy omitted at RP.

Conclusions

  • The distribution of pathologic stages did not change at our institution between 2000–2005 and 2006–2011.
  • The updated Partin nomogram takes into account the updated Gleason scoring system and may be more accurate for contemporary patients diagnosed with prostate cancer.

Eifler JB, Feng Z, Lin BM, et al. An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011. BJU Int 2013; 111: 26–33.

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