External validation of the updated partin tables in a cohort of French and Italian men

Role of Multiparametric Magnetic Resonance Imaging in Predicting Pathologic Outcomes in Prostate Cancer

Multiparametric magnetic resonance imaging (mpMRI) and the introduction of standardized protocols for its interpretation have had a significant impact on the field of prostate cancer (PC). Multiple randomized controlled trials have shown that the sensitivity for detection of clinically significant PC is increased when mpMRI results are the basis for indication of a prostate biopsy. The added value with regards to sensitivity has been strongest for patients with persistent suspicion for PC after a prior negative biopsy. Although enhanced sensitivity of mpMRI is convincing, studies that have compared mpMRI with prostatectomy specimens prepared by whole-mount section analysis have shown a significant number of lesions that were not detected by mpMRI. In this context, the importance of an additional systematic biopsy (SB) is still being debated. While SB in combination with targeted biopsies leads to an increased detection rate, most of the tumors detected by SB only are considered clinically insignificant. Currently, multiple risk calculation tools are being developed that include not only clinical parameters but mpMRI results in addition to clinical parameters in order to improve risk stratification for PC, such as the Partin tables. In summary, mpMRI of the prostate has become a standard procedure recommended by multiple important guidelines for the diagnostic work-up of patients with suspicion of PC.

Prospective Study of the Clinical Impact of Epithelial and Mesenchymal Circulating Tumor Cells in Localized Prostate Cancer

Background

Although circulating tumor cells (CTCs) are considered as a surrogate marker in monitoring disease progression and treatment response in late stage prostate cancer (PCa), its clinical impact in localized PCa remains unclear, indicating the limitation that is simply based on cell count. This perspective observational study aimed to detect the epithelial-to-mesenchymal transition (EMT) subtypes of CTCs in localized PCa and analyze their clinical relevance and application in predicting PCa stages before surgery compared with the Partin table.

Patients and Methods

Between August 2017 and April 2019, 80 newly diagnosed localized PCa patients were enrolled in the study. Peripheral blood samples (5 mL) were collected prior to surgery. The CanPatrol^TM CTC enrichment technique, a size-based isolation method, was used to detect the EMT CTCs. Clinical relevance of the CTCs was analyzed with Spearman’s rank correlation test. Models to predict pathological were built with multivariate logistic regression. Receiver operating characteristic (ROC) curve and area under the curve (AUC) analysis were performed to evaluate the accuracy of the prediction model.

Results

CTCs were detected in 55% of all patients. The biophenotypic CTCs were most valuable and closely correlated with PSA, Gleason score, D’Amico risk classification, and pathological stage in localized PCa. The mesenchymal subtype was rare in this population but associated with seminal vesicle invasion, while the epithelial subtype had limited clinical significance. In addition, the biophenotypic CTCs combined with traditional clinical variables were analyzed by multivariate logistic regression to predict organ-confined disease before surgery, of which the AUC reached 0.818 and was superior to the Partin table 2017 in our cohort.

Conclusion

This study highlights the clinical impact of the biophenotypic CTCs in localized PCa, which was most closely related to clinical variables and could help to predict pathology outcomes before surgery.

Population-Based External Validation of the Updated 2012 Partin Tables in Contemporary North American Prostate Cancer Patients

OBJECTIVE

To externally validate the updated 2012 Partin Tables in contemporary North American patients treated with radical prostatectomy (RP) for localized prostate cancer (PCa) at community institutions.

MATERIALS AND METHODS

We examined records of 25,254 patients treated with RP and pelvic lymph node dissection (PLND) between 2010 and 2013, within the surveillance, epidemiology, and end results database. The ROC derived AUC assessed discriminant properties of the updated 2012 Partin Tables of organ confined disease (OC), extracapsular extension (ECE), seminal vesical invasion (SVI), and lymph node invasion (LNI). Calibration plots focused on calibration between predicted and observed rates.

RESULTS

Proportions of OC, ECE, SVI, and LNI at RP were 69.8%, 18.4%, 7.4%, and 4.4%, respectively. Accuracy for prediction of OC, ECE, SVI, and LNI was 70.4%, 59.9%, 72.9%, and 77.1%, respectively. In subgroup analyses in patients with nodal yield >10, accuracy for LNI prediction was 76.0%. Subgroup analyses in elderly patients and in African American patients revealed decreased accuracy for prediction of all four endpoints. Last but not least, SVI and LNI calibration plots showed excellent agreement, versus good agreement for OC (maximum underestimation of 10%) and poor agreement for ECE (maximum overestimation of 12%).

CONCLUSION

Taken together, the updated 2012 Partin Tables can be unequivocally endorsed for prediction of OC, SVI, and LNI in community-based patients with localized PCa. Conversely, ECE predictions failed to reach the minimum accuracy requirements of 70%. Prostate 77:105-113, 2017. © 2016 Wiley Periodicals, Inc.

Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort

Introduction:

Partin's nomogram is an important prognostic tool to predict adverse pathological features for clinically localized prostate carcinoma. This tool is widely used by both radiation and surgical oncologists for pre-intervention counseling, treatment planning, and predicting the possible need for adjuvant treatment. However, the model is derived from a Western population with typical characteristics of prostate cancer in a prostate-specific antigen (PSA) screened population. Therefore, this study was conducted to assess the performance of the Partin's nomogram as applied to an Indian cohort by assessing the discrimination and calibration properties.

Methods:

A retrospective review of 282 patients treated with robotic radical prostatectomy from 2010 to 2015 was conducted. Partin tables (year 2007) were used to calculate the predicted probabilities for lymph node invasion (LNI), seminal vesicle invasion (SVI), and extraprostatic extension (EPE). The discrimination properties were assessed using the receiver operating characteristic (ROC) curves. Calibration of the model was done to show the relationship between predicted and observed values.

Results:

The mean age of the patients was 64.3 years. Most (59.4%) were clinical T2 disease. Patients with PSA >10 ng/ml comprised 60% of the population. ECE, SVI, and LNI were present in 39.2%, 22%, and 11% of cases, respectively. ROC analysis revealed area under curve values for EPE, SVI, and LNI of 68%, 67.5%, and 71.2%, respectively. Calibration plot suggested that the Partin tables under-predicted the risk whenever the values of predicted risk were more than 26%, 3%, and 1% for EPE, SVI, and LNI, respectively, and over predicted when the risk was lower.

Conclusion:

Our data show that Partin's tables, despite having fair discrimination properties, do not accurately predict LNI, SVI, and ECE across the entire range of predicted values in a contemporary Indian cohort.

Role for (11)C-choline PET in active surveillance of prostate cancer

INTRODUCTION

Active surveillance (AS) is an increasingly popular management strategy for men diagnosed with low-risk indolent prostate cancer. Current tests (prostate-specific antigen [PSA], clinical staging, and prostate biopsies) to monitor indolent disease lack accuracy. (11)C-choline positron emission tomography (PET) has excellent detection rates in local and distant recurrence of prostate cancer. We examine (11)C-choline PET for identifying aggressive prostate cancer warranting treatment in the AS setting.

METHODS

In total, 24 patients on AS had clinical assessment and PSA testing every 6 months and (11)C-choline PET and prostate biopsies annually. The sensitivity and specificity to identify prostate cancer and progressive disease (PD) were calculated for each (11)C-choline PET scan.

RESULTS

In total, 62 biopsy-paired, serial (11)C-choline PET scans were analyzed using a series of standard uptake value-maximum (SUVmax) cut-off thresholds. During follow-up (mean 25.3 months), 11 of the 24 low-risk prostate cancer patients developed PD and received definitive treatment. The prostate cancer detection rate with (11)C-choline PET had moderate sensitivity (72.1%), but low specificity (45.0%). PD prediction from baseline (11)C-choline PET had satisfactory sensitivity (81.8%), but low specificity (38.5%). The addition of clinical parameters to the baseline (11)C-choline PET improved specificity (69.2%), with a slight reduction in sensitivity (72.7%) for PD prediction.

CONCLUSIONS

Addition of (11)C-choline PET imaging during AS may help to identify aggressive disease earlier than traditional methods. However, (11)C-choline PET alone has low specificity due to overlap of SUV values with benign pathologies. Triaging low-risk prostate cancer patients into AS versus therapy will require further optimization of PET protocols or consideration of alternative strategies (i.e., magnetic resonance imaging, biomarkers).

Evaluation of prediction models for the staging of prostate cancer

Background

There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the disease.

Methods

A retrospective review was performed of the Irish Prostate Cancer Research Consortium database and 603 patients were used in the study. Statistical models based on routinely used clinical variables were built using logistic regression, random forests and k nearest neighbours to predict prostate cancer stage. The predictive ability of the models was examined using discrimination metrics, calibration curves and clinical relevance, explored using decision curve analysis. The N = 603 patients were then applied to the 2007 Partin table to compare the predictions from the current gold standard in staging prediction to the models developed in this study.

Results

30% of the study cohort had non organ-confined disease. The model built using logistic regression illustrated the highest discrimination metrics (AUC = 0.622, Sens = 0.647, Spec = 0.601), best calibration and the most clinical relevance based on decision curve analysis. This model also achieved higher discrimination than the 2007 Partin table (ECE AUC = 0.572 & 0.509 for T1c and T2a respectively). However, even the best statistical model does not accurately predict prostate cancer stage.

Conclusions

This study has illustrated the inability of the current clinical variables and the 2007 Partin table to accurately predict prostate cancer stage. New biomarker features are urgently required to address the problem clinician’s face in identifying the most appropriate treatment for their patients. This paper also demonstrated a concise methodological approach to evaluate novel features or prediction models.

International multicentre study examining selection criteria for active surveillance in men undergoing radical prostatectomy

Background:

The controversies concerning possible overtreatment of prostate cancer, highlighted by debate over PSA screening, have highlighted active surveillance (AS) as an alternative management option for appropriate men. Regional differences in the underlying prevalence of PSA testing may alter the pre-test probability for high-risk disease, which can potentially interfere with the performance of selection criteria for AS. In a multicentre study from three different countries, we examine men who were initially suitable for AS according to the Toronto and Prostate Cancer Research International: Active Surveillance (PRIAS) criteria, that underwent radical prostatectomy (RP) in regards to:1.the proportion of pathological reclassification(Gleason score ⩾7, ⩾pT3 disease),2.predictors of high-risk disease,3.create a predictive model to assist with selection of men suitable for AS.

Methods:

From three centres in the United Kingdom, Canada and Australia, data on men who underwent RP were retrospectively reviewed (n=2329). Multivariable logistic regression was performed to identify predictors of high-risk disease. A nomogram was generated by logistic regression analysis, and performance characterised by receiver operating characteristic curves.

Results:

For men suitable for AS according to the Toronto (n=800) and PRIAS (410) criteria, the rates for upgrading were 50.6, 42.7%, and upstaging 17.6, 12.4%, respectively. Significant predictors of high-risk disease were:•Toronto criteria: increasing age, cT2 disease, centre of diagnosis and number of positive cores.•PRIAS criteria: increasing PSA and cT2 disease.Cambridge had a high pT3a rate (26 vs 12%). To assist selection of men in the United Kingdom for AS, from the Cambridge data, we generated a nomogram predicting high-risk features in patients who meet the Toronto criteria (AUC of 0.72).

Conclusion:

The proportion of pathological reclassification in our cohort was higher than previously reported. Care must be used when applying the AS criteria generated from one population to another. With more stringent selection criteria, there is less reclassification but also fewer men who may benefit from AS.

The identification and internal validation of a preoperative serum biomarker panel to determine extracapsular extension in patients with prostate cancer

BACKGROUND

Accurate preoperative staging of prostate cancer (PCa) is important but current diagnostic methods cannot accurately determine extracapsular extension (ECE), resulting in the possible triage of patients towards a less appropriate arm of therapy. This has consequences to patient care and better methods of preoperatively determining ECE are required.

METHODS

We followed a biomarker development pathway and compared the preoperative serum expressions of VEGF-D, PEDF, IGF-I, IGFBP3, and CD14 in patients from the Irish Prostate Cancer Research Consortium (PCRC) with radical prostatectomy determined ECE against patients with nonECE.

RESULTS

The expression measurements of five proteins were fitted into a logistic regression model and backwards variable elimination methods were applied which resulted in a model with IGFBP3 and CD14 as the best combination biomarker panel. This panel was tested in an independent cohort of patients using an optimized multiplex electrochemiluminescence assay. Receiver operating characteristic curves were generated and the areas under the curve (AUC) were calculated as an estimation of prediction accuracy. The biomarker panel was validated with an AUC of 76.6%, and a sensitivity and specificity of 80% and 75% was obtained.

CONCLUSIONS

This is the first internally validated, preoperative serum biomarker panel that identifies ECE in patients with Gleason score 7 PCa with AUC 76.6%. The panel surpasses the routinely used diagnostic standards in accuracy and may help to improve preoperative cancer staging, better inform treatment options, and improve the referral patterns of patients with urgently treatable cancers towards more appropriate arms of therapy.

The need for, and utilization of prostate-bed radiotherapy after radical prostatectomy for patients with prostate cancer in British Columbia

INTRODUCTION

Three randomized trials have demonstrated that post-radical prostatectomy (RP) radiotherapy decreases biochemical relapse for those with adverse pathology. Our purpose was to describe the incidence of pathologic risk factors for recurrence in a contemporary series of patients treated with RP and to describe the use of post-RP radiotherapy.

METHODS

All incident prostate cancers diagnosed between January 2005 and December 2007 were identified from the tumour registry. Cases were then linked to radiotherapy records which included dose and modality (external beam radiotherapy and brachytherapy). The pathology reports in the tumour registry were reviewed for pathologic stage, grade and margin status.

RESULTS

We identified 9223 patients with prostate cancer. Overall, 36.3% of patients treated with RP had positive margins, and may have benefited from adjuvant radiotherapy. After RP, 332 (15%) patients had radiotherapy to the prostate bed; of these, only 25 (1.1%) received truly adjuvant radiotherapy (delivered within 6 months with a prostate-specific antigen of <0.2 ng/mL). Of the 2181 patients treated with RP, 270 (12%) were seen by a radiation oncologist within 6 months of RP. Of the 1015 patients (47%) with adverse RP pathology (positive margins, extracapsular extension or seminal vesicle invasion), 230 (23%) were seen by a radiation oncologist within 6 months of RP.

CONCLUSION

Not all patients with adverse prostatectomy pathology were seen by a radiation oncologist post-prostatectomy, and very few received adjuvant radiotherapy despite almost half of them having risk factors for relapse.

Predicting the risk of lymph node invasion during radical prostatectomy using the European Association of Urology guideline nomogram: a validation study

BACKGROUND

The 2011 European Association of Urology (EAU) guidelines for prostate cancer recommend a pelvic lymph node dissection (PLND) at radical prostatectomy (RP) in all individuals with a nomogram predicted lymph node invasion (LNI) risk of >7%.

METHODS

To test the performing characteristics for several thresholds (1-14%) and to examine the overall accuracy and calibration plot of the EAU nomogram at our institution. The study population consisted of 3081 patients treated with RP and PLND limited to the obturator fossa and the external iliac vein between 2008 and 2010 at a single European institution from Germany. More extensive PLNDs were performed at the surgeon's discretion.

RESULTS

Overall, 260 patients (9.2%) had LNI. The 7% threshold would have avoided 30% of PLNDs, at the cost of missing 8% of patients with LNI. The use of 8% and 9% threshold would have allowed the avoidance of respectively 39% and 48% of PLNDs, at the cost of missing respectively 12% and 14% of patients with LNI. The accuracy of the LNI nomogram was 78%, and the unadjusted departure from ideal calibration was 5.3%.

CONCLUSIONS

We confirmed adequate accuracy and calibration of the LNI nomogram. The 7% cut-off may be overly conservative. Better trade-offs between avoided PLNDs and missed LNI cases may be achieved with a limit of 8 or even 9%.

Biomarker research in prostate cancer--towards utility, not futility

The identification of an appropriate clinical question is critical for any biomarker project. Despite rapid advances in technology, few biomarkers have been forthcoming for prostate cancer. This could be because the clinical questions under investigation have not actually originated from clinical practice. These clinical questions are difficult to identify in the complex and heterogeneous pathogenesis of prostate cancer. In this Review, we have developed a prostate cancer 'roadmap' to identify the aspects of prostate cancer that may be amenable to biomarker discovery and serve as a guide for future projects in prostate cancer biomarker research.

Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers

OBJECTIVES

Prediction of prostate cancer pathological stage is an essential step in a patient's pathway. It determines the treatment that will be applied further. In current practice, urologists use the pathological stage predictions provided in Partin tables to support their decisions. However, Partin tables are based on logistic regression (LR) and built from US data. Our objective is to investigate a range of both predictive methods and of predictive variables for pathological stage prediction and assess them with respect to their predictive quality based on U.K. data.

METHODS AND MATERIAL

The latest version of Partin tables was applied to a large scale British dataset in order to measure their performances by mean of concordance index (c-index). The data was collected by the British Association of Urological Surgeons (BAUS) and gathered records from over 1700 patients treated with prostatectomy in 57 centers across UK. The original methodology was replicated using the BAUS dataset and evaluated using concordance index. In addition, a selection of classifiers, including, among others, LR, artificial neural networks and Bayesian networks (BNs) was applied to the same data and compared with each other using the area under the ROC curve (AUC). Subsets of the data were created in order to observe how classifiers perform with the inclusion of extra variables. Finally a local dataset prepared by the Aberdeen Royal Infirmary was used to study the effect on predictive performance of using different variables.

RESULTS

Partin tables have low predictive quality (c-index=0.602) when applied on UK data for comparison on patients with organ confined and extra prostatic extension conditions, patients at the two most frequently observed pathological stages. The use of replicate lookup tables built from British data shows an improvement in the classification, but the overall predictive quality remains low (c-index=0.610). Comparing a range of classifiers shows that BNs generally outperform other methods. Using the four variables from Partin tables, naive Bayes is the best classifier for the prediction of each class label (AUC=0.662 for OC). When two additional variables are added, the results of LR (0.675), artificial neural networks (0.656) and BN methods (0.679) are overall improved. BNs show higher AUCs than the other methods when the number of variables raises

CONCLUSION

The predictive quality of Partin tables can be described as low to moderate on U.K. data. This means that following the predictions generated by Partin tables, many patients would received an inappropriate treatment, generally associated with a deterioration of their quality of life. In addition to demographic differences between U.K. and the original U.S. population, the methodology and in particular LR present limitations. BN represents a promising alternative to LR from which prostate cancer staging can benefit. Heuristic search for structure learning and the inclusion of more variables are elements that further improve BN models quality.

Prostate cancers detected by saturation repeat biopsy impairs the Partin tables' accuracy to predict final pathological stage

OBJECTIVE

• To analyse the overall accuracy of Partin tables, with special emphasis to potential limitations resulting from differences between prostate cancers detected by different biopsy schedules.

PATIENTS AND METHODS

• Clinical characteristics from 599 patients treated with radical prostatectomy defined the 2007 Partin probabilities of organ confinement (OC), seminal vesicle invasion (SVI) and extracapsular extension (ECE). Prostate cancers were detected by initial biopsy (IBx) with ≤12 cores in 405 patients (67.6%), by conventional repeat biopsy (CRBx) with ≤12 cores in 99 (16.5%) and by saturation repeat biopsy (SRBx) with ≥20 cores in 95 patients (15.9%). • The area under the curve (AUC) estimated by the receiver operating characteristic curve, assessed the predictive accuracy of the 2007 Partin tables.

RESULTS

• The Partin tables AUC of the IBx, CRBx and the SRBx groups were 0.730 vs 0.701 vs 0.585 for OC, 0.631 vs 0.689 vs 0.547 for ECE, and 0.775 vs 0.755 vs 0.641 for SVI, respectively.

CONCLUSIONS

• The overall accuracy of the 2007 Partin tables was clearly inferior in patients with prostate cancers detected by SRBx. • Prostate cancers detected by SRBx undermine the Partin tables' overall accuracy, and this group of patients may be miscounselled by vague predictions.

Predictive and prognostic models in radical prostatectomy candidates: a critical analysis of the literature

CONTEXT

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).

OBJECTIVE

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.

EVIDENCE ACQUISITION

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.

EVIDENCE SYNTHESIS

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.

CONCLUSIONS

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.

Decision curve analysis to compare 3 versions of Partin Tables to predict final pathologic stage

OBJECTIVE

To perform a decision curve analysis (DCA) to compare the Partin Tables 1997, 2001, and 2007 for their clinical applicability.

MATERIAL AND METHODS

Clinical and pathologic data of 687 consecutive patients treated with open radical prostatectomy for clinically localized prostate cancer between 2003 and 2008 at a single institution were used. DCA quantified the net benefit relating to specific threshold probabilities of extraprostatic extension (EPE), seminal vesicle involvement (SVI), and lymph node involvement (LNI).

RESULTS

Overall, EPE, SVI, and LNI were recorded in 17.8, 6.0, and 1.2%, respectively. For EPE predictions, the DCA favored the 2007 version vs. 1997 for SVI vs. none of the versions for LNI.

CONCLUSIONS

DCA indicate that for very low prevalence conditions such as LNI (1.2%), decision models are not useful. For low prevalence rates such as SVI, the use of different versions of the Partin Tables does not translate into meaningful net gains differences. Finally, for intermediate prevalence conditions such as EPE (18%), despite apparent performance differences, the net benefit differences were also marginal. In consequence, the current analysis could not confirm an important benefit from the use of the Partin Tables and it could not identify a clearly better version of any of the 3 available iterations.

Predictive models before and after radical prostatectomy

CONTEXT

In the last 10 years, several user-friendly predictive tools have been developed to help clinicians in decision-making process before and after radical prostatectomy.

OBJECTIVE

To review the most known and used predictive models in pre-operative and post-operative setting.

EVIDENCE ACQUISITION

A structured, comprehensive literature review was performed using data retrieved from recent review articles, original articles, and abstracts. Used keywords were predictive models, nomograms, look-up tables, classification and regression-tree analysis, artificial neural networks, and radical prostatectomy.

EVIDENCE SYNTHESIS

A great amount of predictive models has been provided in oncology setting: nomograms, look-up tables, classification and regression-tree analysis, propensity scores, risk-group stratification models, and artificial neural networks. Pre-surgery predictive tools offer the opportunity of getting the most evidence-based and individualized selection of available treatment alternatives. Post-operative predictive models usually provide higher accuracy relative to the pre-surgery models.

CONCLUSIONS

Decisions and treatment should be tailored to each individual patient and to the specific characteristics of patients. A number of available predictive models represent a tool to provide accurate prediction of cancer natural history and to improve patients' care.

Head to head comparison of three generations of Partin tables to predict final pathological stage in clinically localised prostate cancer

OBJECTIVE

To perform a head to head comparison between three generations of Partin tables, namely from 1997, 2001 and the last updated version of 2007.

MATERIAL AND METHODS

The external validations were based on clinical and pathological data of 687 consecutive patients undergoing radical prostatectomy for clinically localised prostate cancer between 2003 and 2008. Three versions of the Partin tables were compared for their accuracy and performance to predict final pathological stage using receiver operating characteristic (ROC) curve and Loess plots analyses.

RESULTS

Of the whole cohort, 76.2% of men were presented with organ-confined disease (OC), 17.0% had extraprostatic extension (ECE), 6.0% showed seminal vesicle involvement (SVI) and 1.2% had lymph node involvement (LNI). The area under the receiver operating characteristic curve (AUC) of the Partin Tables 1997, 2001 and 2007 was 0.731, 0.727 and 0.722 for OC; 0.671, 0.662 and 0.650 for ECE; 0.795, 0.788 and 0.779 for SVI as well as 0.826, 0.786 and 0.746 for LNI, respectively.

CONCLUSION

All three generations of the Partin tables showed a good accuracy to predict OC, SVI and LNI. However, the predictive accuracy for ECE was only modest. Overall, the newer versions of the Partin tables could not exceed the version of 1997 in their predictive accuracy for any pathological stage and they failed to demonstrate a clear advantage. Our results underline the necessity to perform external validations before the implementation of a new predicting tool.

Validation of the partin nomogram for prostate cancer in a national sample

PURPOSE

The Partin tables are a nomogram that is widely used to discriminate prostate cancer pathological stages, given common preoperative clinical characteristics. The nomogram is based on patients undergoing radical prostatectomy at The Johns Hopkins Medical Institutions. We validated the Partin tables in a large, population based sample.

MATERIALS AND METHODS

The National Cancer Institute Surveillance, Epidemiology and End Results database was used to identify patients treated from 2004 to 2005 who underwent radical prostatectomy. The 2007 Partin tables were used to estimate the prevalence of positive lymph nodes, seminal vesicle invasion, extraprostatic extension and organ confined disease in men with prostate cancer in the database using clinical stage, preoperative prostate specific antigen and Gleason score. The discriminative ability of the tables was explored by constructing ROC curves.

RESULTS

We identified 11,185 men who underwent radical prostatectomy for prostate cancer in 2004 to 2005. The Partin tables discriminated well between patient groups at risk for positive lymph nodes and seminal vesicle invasion (AUC 0.77 and 0.74, respectively). The discrimination of extraprostatic extension and organ confined disease was more limited (AUC 0.62 and 0.68, respectively). The AUC for positive lymph nodes was 0.78 in white men, 0.73 in black men and 0.83 in Asian/Pacific Islander men (p = 0.17). The AUC for positive lymph nodes in men 61 years old or younger was 0.80 vs 0.74 in men older than 61 years (p = 0.03).

CONCLUSIONS

The Partin tables showed excellent discrimination for seminal vesicle invasion and positive lymph nodes. Discrimination of extraprostatic extension and organ confined disease was more limited. The Partin tables performed best in young men.

Novel predictive tools for Irish radical prostatectomy pathological outcomes: development and validation

AIMS

We developed and validated prostate cancer predictive models for Irish patients, allowing individualised predictions of radical prostatectomy pathological outcomes.

METHODS

Retrospective review of the Irish Prostate Cancer Research Consortium database from 2003 to 2008 was performed. Two predictive models were formulated: a replica of the Partin tables (n = 169) and a look-up table based on PSA and biopsy Gleason Score (n = 253). Clinico-pathological parameters were compared to the Partin data set. Internal validation was performed.

RESULTS

In total, 70% of patients were at clinical stage T1c. 5.8% had a PSA less than 4.1 ng/ml, whereas 25% of the Partin patients had a PSA in this range. Maximal predictive accuracy was seen for seminal vesicle invasion (area under the curve = 72%). Prediction of extra-prostatic extension and lymph node involvement was only equivalent to that of a chance phenomenon.

CONCLUSIONS

Our current results do not support the introduction of the formulated predictive models into routine clinical practice.

Predicting cancer-control outcomes in patients with renal cell carcinoma

PURPOSE OF REVIEW

An increasing number of models are becoming available for patients with either suspected or established renal cell carcinoma (RCC) of various stages. In this review, we propose a systematic approach to the assessment of the quantity of the existing predictive and prognostic models.

RECENT FINDINGS

Only one model was designed to distinguish between malignant or benign histology prior to nephrectomy and another tool attempts to discriminate between low-grade and high-grade histology. Four tools predict the natural history of RCC using preoperative tumor characteristics. Postnephrectomy recurrence can be predicted with four tools. Finally, mortality predictions can be quantified with 21 predictive tools. Although several of these tools are validated, formal tests were performed in surprisingly few such models.

SUMMARY

Multiple models can be applied to nephrectomy candidates, to patients treated with nephrectomy, or to individuals with metastatic RCC regardless of nephrectomy status. For newly diagnosed and untreated patients, these tools can guide the clinician with respect to treatment selection. For patients treated with nephrectomy, they can assess the risk of recurrence and/or mortality and can guide the type and frequency of follow-up considerations. Finally, for patients with metastatic RCC, the models can provide the best estimate of remaining life expectancy. Unfortunately, virtually no data are available to model the prognosis of patients subjected to surveillance or nonextirpative treatment models.