African-American race is a predictor of prostate cancer detection: incorporation into a pre-biopsy nomogram

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data

Prostate cancer is the fourth most common cause of cancer in men in Korea, and there has been a rapid increase in cases. In the present study, we constructed a risk prediction model for prostate cancer using representative data from Korea. Participants who completed health examinations in 2009, based on the Korean National Health Insurance database, were eligible for the present study. The crude and adjusted risks were explored with backward selection using the Cox proportional hazards model to identify possible risk variables. Risk scores were assigned based on the adjusted hazard ratios, and the standardized points for each risk factor were proportional to the β-coefficient. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability was assessed by plotting the mean predicted probability against the mean observed probability of prostate cancer. Among the candidate predictors, age, smoking intensity, body mass index, regular exercise, presence of type 2 diabetes mellitus, and hypertension were included. Our risk prediction model showed good discrimination (c-statistic: 0.826, 95% confidence interval: 0.821-0.832). The relationship between model predictions and actual prostate cancer development showed good correlation in the calibration plot. Our prediction model for individualized prostate cancer risk in Korean men showed good performance. Using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding prostate cancer screening.

Self-reported Black race predicts significant prostate cancer independent of clinical setting and clinical and socioeconomic risk factors

INTRODUCTION AND OBJECTIVE

Studies have linked Black race to prostate cancer (CaP) risk but most fail to account for established risk factors such as 5-ARI use, prostate volume, socioeconomic status, and hospital setting. We assess whether Black race remains associated with CaP and Gleason ≥3 + 4 CaP, after adjusting for clinical setting and socioeconomic and clinical factors at prostate biopsy, with a focus on men aged 40-54 years, who may be excluded from current screening guidelines.

METHODS

We recruited 564 men age 40-79 undergoing initial prostate biopsy for abnormal PSA or digital rectal examination (DRE) from three publicly funded and two private hospitals from 2009-2014. Univariate and multivariate analyses examined the associations between hospital type, race, West African Ancestry (WAA), clinical, and sociodemographic risk factors with CaP diagnosis and Gleason ≥3 + 4 CaP. Given changes in CaP screening recommendations, we also assess the multivariate analyses for men aged 40-54.

RESULTS

Black and White men had similar age, BMI, and prostate volume. Black men had higher PSA (8.10 ng/mL vs. 5.63 ng/mL) and PSA density (0.22 ng/mL/cm³ vs. 0.15 ng/mL/cm³, all p < 0.001). Blacks had higher frequency of CaP (63.1% vs. 41.5%, p<0.001) and Gleason ≥3+4 CaP relative to Whites in both public (27.7% vs 11.6%, p<0.001) and private (48.4% vs 21.6%, p = 0.002) settings. In models adjusted for age, first degree family history, prostate volume, 5-ARI use, hospital type, income, marital and educational status, Black race was independently associated with overall CaP diagnosis (OR = 2.13, p = 0.002). There was a significant multiplicative interaction with Black race and abnormal DRE for Gleason ≥3 + 4 CaP (OR = 2.93, p = 0.01). WAA was not predictive of overall or significant CaP among Black men. Black race (OR = 5.66, p = 0.02) and family history (OR = 4.98, p = 0.01) were independently positively associated with overall CaP diagnosis for men aged 40 to 54.

CONCLUSIONS

Black race is independently associated with CaP and Gleason ≥3+4 CaP after accounting for clinical and socioeconomic risk factors including clinical setting and WAA, and has a higher odds ratio of CaP diagnosis in younger men. Further investigation into optimizing screening in Black men aged 40 to 54 is warranted.

The association between race and prostate cancer risk on initial biopsy in an equal access, multiethnic cohort

PURPOSE

Population-based studies have established a link between race and prostate cancer (PC) risk, but whether race predicts PC after adjusting for clinical characteristics is unclear. We investigated the association between race and risk of low- and high-grade PC in men undergoing initial prostate biopsy in an equal access medical center.

METHODS

We conducted a retrospective record review of 887 men (48.6 % black, 51.4 % white) from the Durham Veterans Affairs Medical Center who underwent initial prostate biopsy between 2001 and 2009. Multivariable logistic regression analysis of race and biopsy outcome was conducted adjusting for age, body mass index, number of cores taken, prostate-specific antigen (PSA), and digital rectal examination findings. Multinomial logistic regression was used to test the association between black race and PC grade (Gleason <7 vs. ≥7).

RESULTS

Black men were younger at biopsy (61 vs. 65 years, p < 0.001) and had a higher pre-biopsy PSA (6.6 vs. 5.8 ng/ml, p = 0.001). A total of 499 men had PC on biopsy (245 low grade; 254 high grade). In multivariable analyses, black race was significantly predictive of PC overall [odds ratio 1.50, p = 0.006] and high-grade PC [relative risk ratio (RRR) 1.84, p = 0.001], but was not significantly associated with low-grade PC (RRR 1.29, p = 0.139).

CONCLUSION

In an equal access healthcare facility, black race was associated with greater risk of PC detection on initial biopsy and of high-grade PC after adjusting for clinical characteristics. Additional investigation of mechanisms linking black race and PC risk and PC aggressiveness is needed.

The development of nomograms for stratification of men at risk of prostate cancer prior to prostate biopsy

A main limitation of early prostate cancer (PCa) detection due to elevated PSA levels is caused by the low specificity of PSA, which is associated with a high proportion of men detected with nonmalignant findings at first or subsequent prostate biopsy (PBX). Multivariate prediction models, such as nomograms, have been developed, providing a more accurate method to prospectively determine the risk of a positive PBX. Combining established clinical risk factors with novel diagnostic markers of PCa appears promising to further improve predictive accuracy estimates. Ideally, these nomograms should be capable of identifying PCa at PBX without missing men with high-grade PCa, and preventing a significant proportion of men without, or with insignificant, PCa from undergoing PBX. The intention is to reduce disease morbidity and mortality by detecting significant PCa at an early stage, and at the same time to avoid overdiagnosis as well as overintervention.

Mobile application-based Seoul National University Prostate Cancer Risk Calculator: development, validation, and comparative analysis with two Western risk calculators in Korean men

Objectives

We developed a mobile application-based Seoul National University Prostate Cancer Risk Calculator (SNUPC-RC) that predicts the probability of prostate cancer (PC) at the initial prostate biopsy in a Korean cohort. Additionally, the application was validated and subjected to head-to-head comparisons with internet-based Western risk calculators in a validation cohort. Here, we describe its development and validation.

Patients and Methods

As a retrospective study, consecutive men who underwent initial prostate biopsy with more than 12 cores at a tertiary center were included. In the development stage, 3,482 cases from May 2003 through November 2010 were analyzed. Clinical variables were evaluated, and the final prediction model was developed using the logistic regression model. In the validation stage, 1,112 cases from December 2010 through June 2012 were used. SNUPC-RC was compared with the European Randomized Study of Screening for PC Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC). The clinical value was evaluated using decision curve analysis.

Results

PC was diagnosed in 1,240 (35.6%) and 417 (37.5%) men in the development and validation cohorts, respectively. Age, prostate-specific antigen level, prostate size, and abnormality on digital rectal examination or transrectal ultrasonography were significant factors of PC and were included in the final model. The predictive accuracy in the development cohort was 0.786. In the validation cohort, AUC was significantly higher for the SNUPC-RC (0.811) than for ERSPC-RC (0.768, p<0.001) and PCPT-RC (0.704, p<0.001). Decision curve analysis also showed higher net benefits with SNUPC-RC than with the other calculators.

Conclusions

SNUPC-RC has a higher predictive accuracy and clinical benefit than Western risk calculators. Furthermore, it is easy to use because it is available as a mobile application for smart devices.

Development, validation and comparison of two nomograms predicting prostate cancer at initial 12-core biopsy

AIM

Our aim was to establish, validate and compare two nomograms in an Iranian population for the first time using clinical, laboratory and transrectal ultrasonography (TRUS) findings for predicting prostate cancer at initial biopsy.

METHODS

Data were collected on a total of 916 men referred for an initial prostate biopsy in our center in a 7-year period. Variables analyzed included age, prostate-specific antigen (PSA), free/total PSA (%fPSA), digital rectal examination (DRE) findings, prostate volume (PV) and presence of hypoechoic lesion on TRUS. Univariate logistic regression models were fitted to test cancer predictors. Two multivariate logistic regression models were fitted to create nomograms. Both models were internally validated. Calibration of nomograms was assessed graphically. The area under the receiver operating characteristic curve (AUC) was calculated as a scale of discrimination and predictive accuracy and also used to compare models.

RESULTS

Prostate cancer was detected in 221/669 (33%) men. Based on univariate logistic regression, all of variables except DRE were significant predictors of prostate cancer, with highest AUC for PV (AUC 0.696, 95% CI 0.653-0.738).AUC of nomogram with and without TRUS findings and PSA alone were 0.791, 0.721 and 0.624, respectively. In internal validation, both nomograms had acceptable calibration plots.

CONCLUSION

Our nomogram based on age, DRE, PSA, %fPSA and TRUS finding was significantly more accurate in predicting initial prostate biopsy outcome in men.

Subjects with prostate cancer are less likely to develop esophageal cancer: analysis of SEER 9 registries database

Esophageal adenocarcinoma (EAC) is five times more common among men. EAC tissue exhibits an increased concentration of androgen receptors. We previously reported lower EAC incidence following prostate cancer (PC), suggesting androgen deprivation therapy may reduce EAC incidence, but were unable to demonstrate reducing incidence of EAC with time (latency effect) that would support a cumulative effect of anti-androgen treatment.The Survival Epidemiology and End Results (SEER9) dataset from 1977–2004 was therefore examined to identify subjects with a first malignant primary of PC.Subjects were followed until second primary cancer diagnosis,death, or time period end. Age- and period-adjusted standardized incidence ratios (SIR) were calculated as an estimate of relative risk of an esophageal second malignant primary. Between 1977 and 2004, 343,538 subjects (following exclusion criteria) developed PC as a first primary malignant tumor, providing 2,014,337 years of follow-up.Subsequently 604 esophageal cancers developed, with 763 expected. The incidence of EAC fell following PC [SIR0.83 (95 % CI 0.74–0.93)] with a latency effect identified with SIR 1.1 3 months to 1 year post-PC, SIR 0.85 1–5 years post-PC, and SIR 0.75 greater than five years post-PC. The incidence of esophageal squamous cell carcinoma (ESCC) after PC was also reduced [SIR, 0.79 (0.69-0.89)],with evidence of a latency effect also seen. There is a reduced risk of developing esophageal cancer, both EAC and ESCC, following PC. Androgen deprivation therapy may contribute, but changes in lifestyle following PC diagnosis and decrease in ESCC incidence are also plausible explanations.

Transrectal prostate biopsy

Grayscale transrectal ultrasonographic prostate biopsy using local anesthesia remains the standard approach to the definitive diagnosis of prostate cancer. Careful patient evaluation and preparation are essential to maximize the results and minimize the complications of the biopsy procedure.

Prediction of the risk of harboring prostate cancer by a prebiopsy nomogram based on extended biopsy protocol

OBJECTIVE

We aimed to build a nomogram allowing to predict the probability of prostate cancer (PC) after an initial 21-core biopsy and with readily available clinical data.

METHODS

1,490 screened men who underwent an initial 21-core biopsy protocol were included. A multivariate logistic regression was realized including age, prostate volume, prostate-specific antigen (PSA) level, digital rectal examination (DRE) and transrectal ultrasonography (TRUS). Receiver-operating characteristic estimates were used to quantify accuracy of each model.

RESULTS

PC was detected in 41.3% of the patients. Median PSA, age and prostate volume were 6.2 ng/ml (range 0.2-50), 64.6 years (range 33-87) and 40 ml (range 10-270), respectively. Abnormal TRUS findings were detected in 14.7% of patients. Age, PSA level, prostate volume, DRE and TRUS were significantly associated with PC (all p ≤ 0.004) in univariable logistic regression analysis. In multivariate logistic regression analysis, significant associations were found for age, PSA level, prostate volume and DRE. Predictive accuracy estimate of this model was equal to 0.70. TRUS was not an independent predictor of PC.

CONCLUSIONS

We constructed the first prebiopsy predictive nomogram based on an extended 21-core biopsy procedure with age, PSA level, DRE and prostate volume which are readily available clinical data to urologists.

African American men significantly underestimate their risk of having prostate cancer at the time of biopsy

BACKGROUND

Guidelines for prostate cancer (PCa) screening recommend physicians to have an informational discussion with patients. At the time of biopsy, patients should be informed of their heightened PCa risk, particularly African Americans (AA) who have significantly higher diagnostic and mortality risk. We tested predictors of patients' estimation of their likelihood of having PCa at the time of biopsy.

METHODS

A convenience sample of AA (n = 207) and white (n = 271) biopsy patients was surveyed at the time of prostate biopsy. Participants gave likelihood estimations of having PCa and data on their socio-demographics, health, clinical status, and general and PCa-specific anxiety. Binary logistic regressions tested for predictors of the patients' estimations and biopsy results.

RESULTS

Fifty-one percent of AA men answered that they had a '0%' likelihood of having PCa versus 19% of whites, whereas 57% of AA men had abnormal biopsies compared with 42% of whites. In logistic regressions, predictors of patient answers of 0% chance of PCa were AA ethnicity (OR = 4.50; p < 0.001), lower cancer-specific anxiety (OR = 0.93; p < 0.01), less education (OR = 2.38; p < 0.05), and less urinary disturbance (OR = 0.70; p < .05). In a second regression, AA patients trended towards higher positive biopsy rates (OR = 1.43; p = 0.17).

CONCLUSIONS

At biopsy, AA more often estimated their likelihood of PCa as 0%, despite higher risks. Reasons for these low estimates and their potential contribution to poor treatment outcomes of AA patients require further investigation.

Initial biopsy outcome prediction in Korean patients-comparison of a noble web-based Korean prostate cancer risk calculator versus prostate-specific antigen testing

We developed and validated a novel Korean prostate cancer risk calculator (KPCRC) for predicting the probability of a positive initial prostate biopsy in a Korean population. Data were collected from 602 Koreans who underwent initial prostate biopsies due to an increased level of prostate-specific antigen (PSA), a palpable nodule upon digital rectal examination (DRE), or a hypoechoic lesion upon transrectal ultrasound (TRUS). The clinical and laboratory variables were analyzed by simple and multiple logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was computed to compare its performance to PSA testing alone. Prostate cancer was detected in 172 (28.6%) men. Independent predictors included age, DRE findings, PSA level, and prostate transitional zone volume. We developed the KPCRC using these variables. The AUC for the selected model was 0.91, and that of PSA testing alone was 0.83 (P < 0.001). The AUC for the selected model with an additional dataset was 0.79, and that of PSA testing alone was 0.73 (P = 0.004). The calculator is available on the website: http://pcrc.korea.ac.kr. The KPCRC improved the performance of PSA testing alone in predicting the risk of prostate cancer in a Korean population. This calculator would be a practical tool for physicians and patients.

Critical review of prostate cancer predictive tools

Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities and potential treatment-related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, look-up and propensity scoring tables, risk-class stratification prediction tools, classification and regression tree analysis, nomograms and artificial neural networks. Criteria to evaluate tools include discrimination, calibration, generalizability, level of complexity, decision analysis and ability to account for competing risks and conditional probabilities. The available predictive tools and their features, with a focus on nomograms, are described. While some tools are well-calibrated, few have been externally validated or directly compared with other tools. In addition, the clinical consequences of applying predictive tools need thorough assessment. Nevertheless, predictive tools can facilitate medical decision-making by showing patients tailored predictions of their outcomes with various alternatives. Additionally, accurate tools may improve clinical trial design.

Evaluation of the Prostate Cancer Prevention Trial Risk calculator in a high-risk screening population

STUDY TYPE

Diagnostic (exploratory cohort).

LEVEL OF EVIDENCE

2b.

OBJECTIVE

To evaluate the Prostate Cancer Prevention Trial (PCPT) risk calculator in a screening cohort of young, racially diverse, high-risk men with a low baseline prostate-specific antigen (PSA) level and enrolled in the Prostate Cancer Risk Assessment Program (PRAP). The PCPT calculator provides an assessment of prostate cancer risk based on age, PSA level, race, previous biopsy, and family history.

PATIENTS AND METHODS

Eligibility for PRAP includes men aged 35-69 years who are African-American, have a family history of prostate cancer, or have a known BRCA1/2 mutation. PCPT risk scores were determined for PRAP participants, and were compared to observed prostate cancer rates.

RESULTS

In all, 624 participants were evaluated, including 382 (61.2%) African-American men and 242 (38.7%) men with a family history of prostate cancer; the median (range) age was 49.0 (34.0-69.0) years and the median PSA level 0.9 (0.1-27.2) ng/mL. The PCPT risk score correlated with prostate cancer diagnosis, as the median baseline risk score in patients diagnosed with prostate cancer was 31.3%, vs 14.2% in patients not diagnosed with prostate cancer (P < 0.001). The PCPT calculator similarly stratified the risk of diagnosis of Gleason score > or =7 disease, as the median risk score was 36.2% in patients diagnosed with Gleason > or =7 prostate cancer vs 15.2% in all other participants (P < 0.001).

CONCLUSION

The PCPT risk calculator score was found to stratify prostate cancer risk in a cohort of young, primarily African-American men with a low baseline PSA level. These results support further evaluation of this predictive tool for assessing the risk of prostate cancer in high-risk men.

Techniques and predictive models to improve prostate cancer detection

The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.

Patients with prostate cancer are less likely to develop oesophageal adenocarcinoma: could androgens have a role in the aetiology of oesophageal adenocarcinoma?

Oesophageal adenocarcinoma (OAC) is more common in men. Androgens may therefore contribute to the pathogenesis of OAC. Prostate cancer (PC), an androgen sensitive tumor with a long natural history, may allow insights into this putative association. West Midlands Cancer Intelligence Unit data from 1977 to 2004 were examined to identify patients with a first malignant primary of PC. Patients were followed until diagnosis of a second primary cancer, death or end of the time period. Age- and period-adjusted standardized incidence ratios (SIR) were calculated as an estimate of the relative risk of a second malignant primary of the oesophagus. Between 1977 and 2004, 44,819 men within the West Midlands developed PC as a first primary malignancy. After exclusion for lack of follow-up, 38,627 men were eligible, providing 143,526 person years at risk for analysis. 86 second primary oesophageal cancers were observed, compared with 110 expected, resulting in an SIR of 0.78 (95% CI 0.62-0.96). There was a reduced risk of OAC 0.7 (0.5-0.95) but not of oesophageal squamous cell carcinoma (OSCC) 1.03 (0.69-1.47). The risk of developing OAC, but not OSCC, is lower than expected in patients with PC. A diagnosis of PC may be associated with aetiological factors that are negatively associated with OAC, or anti-androgen therapy may influence the development of OAC.

Predicting the outcome of prostate biopsy: comparison of a novel logistic regression-based model, the prostate cancer risk calculator, and prostate-specific antigen level alone

OBJECTIVES

To develop a logistic regression-based model to predict prostate cancer biopsy at, and compare its performance to the risk calculator developed by the Prostate Cancer Prevention Trial (PCPT), which was based on age, race, prostate-specific antigen (PSA) level, a digital rectal examination (DRE), family history, and history of a previous negative biopsy, and to PSA level alone.

PATIENTS AND METHODS

We retrospectively analysed the data of 1280 men who had a biopsy while enrolled in a prospective, multicentre clinical trial. Of these, 1108 had all relevant clinical and pathological data available, and no previous diagnosis of prostate cancer. Using the PCPT risk calculator, we calculated the risks of prostate cancer and of high-grade disease (Gleason score > or =7) for each man. Receiver operating characteristic (ROC) curves for the risk calculator, PSA level and the novel regression-based model were compared.

RESULTS

Prostate cancer was detected in 394 (35.6%) men, and 155 (14.0%) had Gleason > or =7 disease. For cancer prediction, the area under the ROC curve (AUC) for the risk calculator was 66.7%, statistically greater than the AUC for PSA level of 61.9% (P < 0.001). For predicting high-grade disease, the AUCs were 74.1% and 70.7% for the risk calculator and PSA level, respectively (P = 0.024). The AUCs increased to 71.2% (P < 0.001) and 78.7% (P = 0.001) for detection and high-grade disease, respectively, with our novel regression-based models.

CONCLUSIONS

ROC analyses show that the PCPT risk calculator modestly improves the performance of PSA level alone in predicting an individual's risk of prostate cancer or high-grade disease on biopsy. This predictive tool might be enhanced by including percentage free PSA and the number of biopsy cores.

Inventory of prostate cancer predictive tools

PURPOSE OF REVIEW

We created an inventory of current predictive tools available for prostate cancer. This review may serve as an initial step toward a comprehensive reference guide for physicians to locate published nomograms that apply to the clinical decision in question. Using MEDLINE a literature search was performed on prostate cancer predictive tools from January 1966 to November 2007. We describe the patient populations to which they apply and the outcomes predicted, and record their individual characteristics.

RECENT FINDINGS

The literature search generated 111 published prediction tools that may be applied to patients in various clinical stages of disease. Of the 111 prediction tools, only 69 had undergone validation. We present an inventory of models with input variables, prediction form, number of patients used to develop the prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed.

SUMMARY

Decision rules, such as nomograms, provide evidence-based and at the same time individualized predictions of the outcome of interest. Such predictions have been repeatedly shown to be more accurate than those of clinicians, regardless of their level of expertise. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous high-risk patient groups for whom new cancer therapeutics will be investigated.

The comparability of models for predicting the risk of a positive prostate biopsy with prostate-specific antigen alone: a systematic review

CONTEXT

The sensitivity and specificity profile of measuring levels of prostate-specific antigen (PSA) to select men for prostate biopsy is not optimal. This has prompted the construction of nomograms and artificial neural networks (ANNs) to increase the performance of PSA measurements.

OBJECTIVE

A systematic review of nomograms and ANNs designed to predict the risk of a positive prostate biopsy for cancer was conducted in order to determine their value versus measuring PSA levels alone.

EVIDENCE ACQUISITION

Medical Literature Analysis and Retrieval System Online (U.S. National Library of Medicine's life science database; MEDLINE) was searched using the terms "nomogram" "artificial neural network" and "prostate cancer" for dates up to and including July 2007 and was supplemented by manual searches of reference lists. Included studies used an assessment tool to examine the risk of a positive prostate biopsy in a man without a known cancer diagnosis. Intramodel comparisons with evaluation of PSA levels alone, and intermodel comparisons of area under the curve (AUC) from receiver operating characteristic (ROC) curves were conducted. Individual case examples were also used for comparisons.

EVIDENCE SYNTHESIS

Twenty-three studies examining 36 models were included. With the exception of two studies, all the models had AUC values of 0.70 or greater, with eight reporting an AUC of >/=0.80 and four (all ANNs) reporting an AUC >/=0.85, with variable validation status. Fourteen studies compared the AUC with PSA levels alone: all showed a benefit from using AUCs which varied from 0.02 to 0.26. Of the 16 external validation comparisons, in 13 the AUC was lower in the external population than in the model population.

CONCLUSIONS

Nomograms and ANNs produce improvements in AUC over measurement of PSA levels alone, but many lack external validation. Where this is available, the benefits are often diminished, although most remain significantly better than with evaluation of PSA levels alone. In men without additional risk factors, PSA cutoff values alone provide a relatively precise risk estimate, but if additional risk factors are known, PSA values alone are less accurate.

Impact of socioeconomic factors on prostate cancer outcomes in black patients treated with surgery

OBJECTIVES

The role of socioeconomic factors in the worse outcome of black men with prostate cancer remains unclear. To determine whether socioeconomic factors affect prostate cancer outcomes, we studied a cohort of only black patients to minimize known confounding factors.

METHODS

We studied black men treated with radical prostatectomy at New York Veterans Administration Medical Center and Memorial Sloan-Kettering Cancer Center between 1990 and 2005. A centralized pathology review process determined the Gleason score of all cases. Prostate-specific antigen (PSA) recurrence at both sites was defined as PSA of 0.2 or greater with a confirmatory rise. By matching patients' home zip codes to the U.S. Census Bureau database, we obtained corresponding socioeconomic data regarding median household income (income) and percentage of population with a high school (degree). We analyzed income, education, and clinical and pathological parameters for the whole cohort.

RESULTS

We studied 430 black patients. They resided in neighborhoods where median household income was $41,498.10 and mean percentage of high school graduates was 73.4%. A total of 88 patients (20.9%) had PSA recurrence. Median follow-up for survivors was 37 months. Neither income nor education evaluated as continuous or categorical variables were predictors of PSA recurrence. When evaluated as composite categorical variable, the combination of greater income and education did not predict disease-free survival.

CONCLUSIONS

Data suggest that socioeconomic factors have limited impact on PSA recurrence in black men treated with radical prostatectomy. Thus, biologic factors might have a role in the poor outcomes in this population.

Role of nomograms for prostate cancer in 2007

Nomograms have been developed to predict prostate cancer (PCa) related outcomes. We report what has been achieved and what can be expected in 2007 and in the future. We reviewed the literature to provide guidelines in terms of criteria, limitations and clinical value of nomograms in 2007. Further, we report a set of recent PCa nomograms, where certain criteria are listed which were used to develop each nomogram. Our findings suggest a demand for an update of nomograms as well as head-to-head comparisons to determine the best-suited model in select fields of PCa outcomes. In 2007 and the future, an increasing number of nomograms will address important endpoints such as PSA recurrence, local and distant metastases, or androgen-independent PCa-specific survival. Our results suggest that nomograms represent valid risk stratification models to achieve most accurate predictions. In 2007 and the future, more specific and refined nomograms will be available which address relevant clinical end points. Moreover, novel markers in PCa outcomes will be quantified using the nomogram approach.

External validation of the Prostate Cancer Prevention Trial risk calculator in a screened population

OBJECTIVES

To evaluate the recently developed Prostate Cancer Prevention Trial (PCPT) prostate cancer risk calculator in the San Antonio Center of Biomarkers of Risk for Prostate Cancer (SABOR) cohort of the Early Detection Research Network, a younger and more ethnically diverse population than that in the PCPT.

METHODS

From 3488 SABOR participants, we identified 446 who had undergone prostate biopsy and had undergone prostate-specific antigen measurement and digital rectal examination before biopsy. Most biopsies were performed for abnormal digital rectal examination findings, a prostate-specific antigen level of more than 2.5 ng/mL, or elevated risk because of a first-degree relative with prostate cancer. We evaluated the operating characteristics of the PCPT calculator for detecting prostate cancer in this cohort of SABOR participants. Of the 446 men in this cohort, 24% were younger than 55 years of age.

RESULTS

Of the 446 men who had undergone biopsy, 148 (33.2%) had prostate cancer. The observed SABOR prostate cancer rates increased with increasing PCPT risk: 15.7%, 39.0%, 48.8%, and 100.0% for a PCPT risk calculator value of less than 25%, 25% to 50%, 50% to 75%, and greater than 75%, respectively. The PCPT risk calculator had an area under the receiver operating characteristic curve of 65.5% (95% confidence interval 60.2% to 70.8%, P < 0.0001), was greater in African-American men (area under curve of 80.0%, 95% confidence interval 67.8% to 92.2%) than in other races (P = 0.02), and was not different in Hispanic men (P > 0.05).

CONCLUSIONS

The results of our study have shown that the PCPT risk calculator, available from the Internet and incorporating the current best panel of risk factors, is valid in other, more diverse, populations.