Development of a nomogram to predict probability of positive initial prostate biopsy among Japanese patients

Prostate cancer nomograms and their application in Asian men: a review

Nomograms help to predict outcomes in individual patients rather than whole populations and are an important part of evaluation and treatment decision making. Various nomograms have been developed in malignancies to predict and prognosticate clinical outcomes such as severity of disease, overall survival, and recurrence-free survival. In prostate cancer, nomograms were developed for determining need for biopsy, disease course, need for adjuvant therapy, and outcomes. Most of these predictive nomograms were based on Caucasian populations. Prostate cancer is significantly affected by race, and Asian men have a significantly different racial and genetic susceptibility compared to Caucasians, raising the concern in generalizability of these nomograms. We reviewed the existing literature for nomograms in prostate cancer and their application in Asian men. There are very few studies that have evaluated the applicability and validity of the existing nomograms in these men. Most have found significant differences in the performance in this population. Thus, more studies evaluating the existing nomograms in Asian men or suggesting modifications for this population are required.

Artificial intelligence-aided detection for prostate cancer with multimodal routine health check-up data: an Asian multi-center study

BACKGROUND

The early detection of high-grade prostate cancer (HGPCa) is of great importance. However, the current detection strategies result in a high rate of negative biopsies and high medical costs. In this study, the authors aimed to establish an Asian Prostate Cancer Artificial intelligence (APCA) score with no extra cost other than routine health check-ups to predict the risk of HGPCa.

PATIENTS AND METHODS

A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses.

RESULTS

Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively.

CONCLUSIONS

The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.

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.

Prostate cancer risk prediction models in Eastern Asian populations: current status, racial difference, and future directions

Prostate cancer (PCa) risk calculators (RCs) with prostate-specific antigen (PSA) and other risk factors can greatly improve the accurate prediction of potential risk of PCa compared to PSA. The European Randomized Study of Screening for PCa Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) are developed on the Western population. However, the Western RCs showed limited diagnostic efficacy in the Eastern Asian population, mainly due to racial differences between the two populations. We aimed to review the application of Western RCs and Eastern Asian RCs in Eastern Asian cohorts and to identify the characteristics and efficacy of these RCs.

Comparing the prediction of prostate biopsy outcome using the Chinese Prostate Cancer Consortium (CPCC) Risk Calculator and the Asian adapted Rotterdam European Randomized Study of Screening for Prostate Cancer (ERSPC) Risk Calculator in Chinese and European men

PURPOSE

To externally validate the clinical utility of Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) and Asian adapted Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (A-ERSPC-RC3) for prediction prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 3 + 4) in both Chinese and European populations.

MATERIALS AND METHODS

The Chinese clinical cohort, the European population-based screening cohort, and the European clinical cohort included 2,508, 3,616 and 617 prostate biopsy-naive men, respectively. The area under the receiver operating characteristic curve (AUC), calibration plot and decision curve analyses were applied in the analysis.

RESULTS

The CPCC-RC's predictive ability for any PCa (AUC 0.77, 95% CI 0.75-0.79) was lower than the A-ERSPC-RC3 (AUC 0.79, 95% CI 0.77-0.81) in the European screening cohort (p < 0.001), but similar for HGPCa (p = 0.24). The CPCC-RC showed lower predictive accuracy for any PCa (AUC 0.65, 95% CI 0.61-0.70), but acceptable predictive accuracy for HGPCa (AUC 0.73, 95% CI 0.69-0.77) in the European clinical cohort. The A-ERSPC-RC3 showed an AUC of 0.74 (95% CI 0.72-0.76) in predicting any PCa, and a similar AUC of 0.74 (95% CI 0.72-0.76) in predicting HGPCa in Chinese cohort. In the Chinese population, decision curve analysis revealed a higher net benefit for CPCC-RC than A-ERSPC-RC3, while in the European screening and clinical cohorts, the net benefit was higher for A-ERSPC-RC3.

CONCLUSIONS

The A-ERSPC-RC3 accurately predict the prostate biopsy in a contemporary Chinese multi-center clinical cohort. The CPCC-RC can predict accurately in a population-based screening cohort, but not in the European clinical cohort.

Serum thymidine kinase 1 is associated with Gleason score of patients with prostate carcinoma

The aim of the present was to assess whether serum thymidine kinase 1 (STK1) concentration is a useful biomarker for the screening of benign prostatic hyperplasia (BPH) or prostate malignancy. Serum samples were collected from 123 patients with prostate carcinoma prior to surgery, biopsy or androgen deprivation therapy and at 3, 6 and 10 months following the procedure. A total of 205 patients with BPH and 266 healthy controls were also utilized. STK1 concentration and total prostate-specific antigen (PSA) were measured in patient serum by use of commercial assays. The pathological specimens (obtained from surgery or biopsy) were assessed according to Gleason scores (GS). STK1 concentration and total PSA were significantly higher in patients with prostate carcinoma compared with patients with BPH and healthy individuals. Furthermore, STK1 concentration was associated with Gleason score, while total PSA was not. However, no association was identified between STK1 concentration and total serum PSA. A receiver operating characteristic analysis was performed on STK1 concentrations among patients with prostate carcinoma. The results demonstrated that the sensitivity and specificity were high, with an area under the curve (AUC) of 0.97. Although the sensitivity and specificity of total PSA were also high, the AUC value was relatively low (0.74). The results indicated that STK1 concentration is a more reliable prognostic biomarker than total PSA in respect to the GS system. Additionally, since STK1 concentration is associated with Gleason score, the use of biopsies to determine Gleason score may be replaced to some extent by the STK1 concentration test, thus reducing the discomfort of patients from which biopsies are obtained.

The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer

PURPOSE

To evaluate the auxiliary effectiveness of periprostatic fat thickness (PPFT) on multi-parametric magnetic resonance imaging (mp-MRI) to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) in predicting the presence of prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 7).

RESULTS

Overall, there were 371 patients (54.3%) with PCa and 292 patients (42.8%) with HGPCa. The mean value of PPFT was 4.04 mm. Multivariate analysis revealed that age, prostatic specific antigen (PSA), volume, PI-RADS score, and PPFT were independent predictors of PCa. All factors plus abnormal digital rectal exam were independent predictors of HGPCa. In addition, the PPFT was the independent predictor of PCa (Odds ratio [OR] 2.56, p = 0.004) and HGPCa (OR 2.70, p = 0.014) for subjects with PI-RADS grade 3. The present two nomograms based on multivariate analysis outperformed the single PI-RADS in aspects of predicting accuracy for PCa (area under the curve: 0.922 vs. 0.883, p = 0.029) and HGPCa (0.919 vs. 0.873, p = 0.007). Decision-curve analysis also indicated the favorable clinical utility of the present two nomograms.

MATERIALS AND METHODS

The clinical data of 683 patients who received transrectal ultrasound guided biopsy and prior mp-MRI were reviewed. PPFT was measured as the shortest perpendicular distance from the pubic symphysis to the prostate on MRI. Univariate and multivariate analyses were performed to determine the independent predictors of PCa and HGPCa. We also constructed two nomograms for predicting PCa and HGPCa based on the logistic regression.

CONCLUSION

The PPFT on mp-MRI is an independent predictor of PCa and HGPCa, notably for patients with PI-RADS grade 3. The nomograms incorporated predictors of PPFT and PI-RADS demonstrated good predictive performance.

Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort

PURPOSE

We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort.

MATERIALS AND METHODS

Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots.

RESULTS

PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed.

CONCLUSIONS

KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy and reduce overdiagnosis and overtreatment in clinical settings.

A nomogram for prediction of prostate cancer on multi-core biopsy using age, serum prostate-specific antigen, prostate volume and digital rectal examination in Singapore

AIM

To develop and internally validate two nomograms for predicting the probability of overall and clinically-significant prostate cancer on initial biopsy in a Singaporean population.

METHODS

Data were collected from men undergoing initial prostate biopsy at a single center. The indications for biopsy were serum prostate-specific antigen (PSA) ≥4.0 ng/mL or suspicious digital rectal examination (DRE) findings. Men with PSA >30 ng/mL were excluded. Age, PSA, prostate volume (PV) and DRE were predictors included in our logistic regression model and used to construct two nomograms for overall prostate cancer and clinically-significant (Gleason sum ≥7) cancer detection. Predictive accuracies of our nomograms were assessed using area under curve (AUC) of their receiver-operator characteristic curves. Internal validation was performed using the bootstrap method. Our nomograms were compared to a model based on PSA alone using AUC and decision curve analysis (DCA).

RESULTS

Out of 672 men analyzed, our positive biopsy rate was 26.2% (n = 176), of which 63.6% (n = 112) had clinically significant disease. Age, PSA, PV and DRE status were all independent risk factors for both overall prostate cancer detection as well as clinically-significant cancer detection (all P < 0.05). Our nomogram outperformed serum PSA for both overall and clinically-significant cancer detection (0.736 vs 0.642, P < 0.001 and 0.793 vs 0.696, P < 0.001, respectively). Using DCA, our nomograms had superior net benefit and net reduction in biopsy rate compared to PSA alone.

CONCLUSIONS

Our nomograms have been shown to be superior to PSA alone, on both AUC and DCA. However, it warrants external validation.

Development of a novel nomogram to predict hypertension cure after laparoscopic adrenalectomy in patients with primary aldosteronism

BACKGROUND

Primary aldosteronism is the most common curable cause of secondary hypertension. Despite resection, however, many patients with primary aldosteronism continue to require antihypertensive drugs to control their blood pressure. Although many patients with primary aldosteronism want to know the postoperative probability of hypertension cure before surgery, there are no predictive models calculating its probability. We therefore developed a nomogram to predict hypertension cure in patients with primary aldosteronism after laparoscopic adrenalectomy.

METHODS

We retrospectively surveyed 132 Japanese patients with primary aldosteronism who were treated by unilateral laparoscopic adrenalectomy. Hypertension cure was defined as normal blood pressure (<140/90 mmHg) without antihypertensive drugs 6 months postoperatively. We developed a novel nomogram that postoperatively predicted cured hypertension in 105 (80 %) randomly selected patients and validated it with the remaining 27 (20 %).

RESULTS

At 6 months, blood pressure had normalized in 42 % of patients without antihypertensive drugs. Duration of hypertension, preoperative number of antihypertensive drug classes, age, and sex were incorporated into a novel nomogram as independent predictors of hypertension cure. The value of the area under the receiver operating characteristics curve for this nomogram was 0.83-which was significantly higher than that of the Aldosteronoma Resolution Score-on internal validation.

CONCLUSIONS

We developed the first nomogram that can accurately predict postoperative hypertension cure in patients with primary aldosteronism. This nomogram can help clinicians calculate the probability of postoperative hypertension cure in patients with primary aldosteronism and objectively inform them of their hypertension outcome before laparoscopic adrenalectomy.

Percent free prostate-specific antigen for prostate cancer diagnosis in Chinese men with a PSA of 4.0-10.0 ng/mL: Results from the Chinese Prostate Cancer Consortium

Objective

To test the diagnostic performance of percent free prostate-specific antigen (%fPSA) in predicting any prostate cancer (PCa) and high-grade prostate cancer (HGPCa) in a retrospective multi-center biopsy cohort with a PSA level of 4.0-10.0 ng/mL in China.

Methods

Consecutive patients with a PSA of 4.0-10.0 ng/mL who underwent transrectal ultrasound-guided biopsy were enrolled at 16 Chinese medical centers from January 1st, 2010 to December 31st, 2013. Total and free serum PSA determinations were performed using three types of electro-chemiluminescence immunoassays recalibrated to the World Health Organization (WHO) standard. The diagnostic accuracy of PSA, %fPSA, and %fPSA in combination with PSA (%fPSA + PSA) was determined using the area under the receiver operating characteristic (ROC) curve (AUC).

Results

A total of 2310 consecutive men with PSA levels between 4.0 and 10.0 ng/mL were included, and the detection rate of PCa was 25.1%. The AUC of %fPSA and %fPSA + PSA in predicting any PCa was superior to PSA alone in men aged ≥60 years (0.623 vs. 0.534, p < 0.0001) but not in men aged 40-59 years (0.517 vs. 0.518, p = 0.939). Similar result was yield in predicting HGPCa.

Conclusion

In a clinical setting of Chinese men with 4.0-10.0 ng/mL PSA undergoing initial prostate biopsy, adding %fPSA to PSA can moderately improve the diagnostic accuracy for any PCa and HGPCa compared with PSA alone in patients ≥60 but not in patients aged 40-59 years.

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.

The cutoff level of free/total prostate specific antigen (f/t PSA) ratios in the diagnosis of prostate cancer: a validation study on a Turkish patient population in different age categories

We investigated an optimal cutoff level of free/total PSA ratios (f/t PSA) in predicting prostate cancer in different age groups, focusing on the avoidance of unnecessary prostate biopsies. A total of 4955 men were enrolled into the study. Serum tPSA, fPSA, and f/t PSA ratios were determined for the study population. All males who had suspicious digital rectal examination and tPSA > 4 ng/mL underwent transrectal ultrasonography-guided prostate biopsy. Receiver operating characteristic (ROC) curves for each group were generated by plotting the sensitivity versus 1-specificity for the f/t PSA ratio. The sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were obtained using various f/t PSA ratio cutoffs for different age groups. There were 657 patients with a PSA level of 4-10 ng/mL. According to sensitivity and specificity f/t% PSA cutoff points were determined to be 10%, 15%, 15%, and 10% in 50-59 years, 60-69 years, >70 years, and all ages categories, respectively, in patients with initial PSA level of 4-10 ng/mL. f/t PSA ratio had an area under the curve (AUC) value of 0.81 (95% confidence level: 0.80-0.82) for all age groups in detecting prostate cancer. f/t PSA ratio has an AUC value of 0.669 (0.632-0.705) in detecting prostate cancer among patients with a PSA level of 4-10 ng/mL. Ten percent of f/t PSA ratio had the highest specificity with PLR and 30% f/t PSA ratio had the highest sensitivity with lower NLR in the all-age categories. The current study shows that the use of f/t PSA ratio in patients with PSA levels of 4-10 ng/mL should enhance the specificity of PSA screening and decrease the number of unnecessary biopsies. The age-related changes warrant further investigation in a large, multicentric, and multinational population to improve the clinical use of f/t PSA cutoffs.

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.

Development of nomogram to non-steroidal antiandrogen sequential alternation in prostate cancer for predictive model

OBJECTIVES

To clarify clinical predictors for a prostate-specific antigen decrease ≥50% in response to alternative non-steroidal antiandrogen therapy and to develop a nomogram to predict the prostate-specific antigen decrease ≥50% in response to alternative non-steroidal antiandrogen therapy in patients with advanced prostate cancer that relapsed after initial combined androgen blockade. We previously reported that combined androgen blockade with an alternative non-steroidal antiandrogen is effective for advanced prostate cancer that has relapsed after initial combined androgen blockade.

METHODS

We enrolled 161 patients from 14 medical institutions with histologically confirmed prostate cancer who had been treated with combination therapy and in whom cancer progressed after first-line combined androgen blockade therapy. A nomogram for the prostate-specific antigen decrease ≥50% from baseline prostate-specific antigen in response to alternative non-steroidal antiandrogen therapy was developed based on the final logistic regression model.

RESULTS

Overall prostate-specific antigen decreased ≥50% in 75 of 161 patients (46.6%) in response to alternative non-steroidal antiandrogen therapy. Using five independent risk factors (initial serum level of prostate-specific antigen, hemoglobin, C-reactive protein, prostate-specific antigen nadir to second hormone therapy and Gleason sum), a nomogram was developed for the prediction of prostate-specific antigen decrease ≥50% in response to alternative non-steroidal antiandrogen therapy. The receiver operating characteristic curve showed that the accuracy of the predicted probability was 72.5% for the model.

CONCLUSIONS

This predictive nomogram could predict the prostate-specific antigen decrease ≥50% in response to alternative non-steroidal antiandrogen therapy and might be of benefit to determine the sequential treatment strategy in patients with relapse after first combined androgen blockade.

Chinese nomogram to predict probability of positive initial prostate biopsy: a study in Taiwan region

Several nomograms for prostate cancer detection have recently been developed. Because the incidence of prostate cancer is lower in Chinese men, nomograms based on other populations cannot be directly applied to Chinese men. We, therefore, developed a model for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Chinese male population. Data were collected from 893 Chinese male referrals, 697 in the derivation set and 196 in the external validation set, who underwent initial prostate biopsies as individual screening. We analyzed age, prostate volume, total prostate-specific antigen (PSA), PSA density (PSAD), digital rectal examinations (DRE) and transrectal ultrasound (TRUS) echogenicity. Logistic regression analysis estimated odds ratio, 95% confidence intervals and P values. Independent predictors of a positive biopsy result included advanced age, small prostate volume, elevated total PSA, abnormal digital rectal examination, and hyperechoic or hypoechoic TRUS echogenicity. We developed a predictive nomogram for an initial positive biopsy using these variables. The area under the receiver-operating characteristic curve for the model was 88.8%, which was greater than that of the prediction based on total PSA alone (area under the receiver-operating characteristic curve 74.7%). If externally validated, the predictive probability was 0.827 and the accuracy rate was 78.1%, respectively. Incorporating clinical and laboratory data into a prebiopsy nomogram improved the prediction of prostate cancer compared with predictions based solely on the individual factors.

Nomogram using transrectal ultrasound-derived information predicting the detection of high grade prostate cancer on initial biopsy

PURPOSE

To develop a nomogram using transrectal ultrasound (TRUS)-derived information for predicting high grade (HG) prostate cancer (PCa) on initial biopsy.

METHODS

Data were collected on 1,048 men with serum prostate-specific antigen (PSA) levels 4.0 to 9.9 ng/mL who underwent an initial prostate biopsy. Two logistic regression-based nomograms were constructed to predict the detection of PCa. Nomogram-1 incorporated age, digital rectal examination, PSA and percent free PSA data, whereas nomogram-2 incorporated those factors plus TRUS-derived information (i.e., prostate volume and the presence of hypoechoic lesions). The prediction of any PCa and HGPCa (Gleason score≥7) were determined. Twenty percent of the data were randomly reserved for study validation, and the predictive accuracies of the two nomograms were directly compared.

RESULTS

Of the 1,048 men who underwent biopsy, 216 (20.6%) were found to have any PCa, and 97 (9.3%) were found to have HGPCa. All six risk factors were found to be independent predictors for both any PCa and HGPCa. The area under curve (AUC) for nomogram-2 was 0.76 (95% confidence interval [CI], 0.72 to 0.81) for predicting any PCa, and 0.83 (95% CI, 0.79 to 0.88) for predicting HGPCa. These AUCs were greater than those for nomogram-1 (0.72 [95% CI, 0.68 to 0.76 for any PCa; P<0.001], 0.78 [95% CI, 0.72 to 0.83 for HGPCa; P<0.001]). Removing the TRUS-derived information from nomogram-2 resulted in an incremental AUC decrease of 0.052 for any PCa and 0.063 for HGPCa.

CONCLUSIONS

The nomogram using TRUS-derived information had a high predictive accuracy for HGPCa on initial prostate biopsy.

The prognostic significance of loss of the androgen receptor and neuroendocrine differentiation in prostate biopsy specimens among castration-resistant prostate cancer patients

Prostate cancer (PCa) is a leading cause of mortality, and despite good response to androgen ablation this response is eventually lost. In the present study, androgen receptor (AR) expression and neuroendocrine differentiation (NED) were evaluated in hormone-sensitive (HSPC) and castration-resistant prostate cancers (CRPC). Prostate tissues were obtained from 20 HSPC patients at diagnosis and 28 CRPC patients at castration-resistant progression. AR, chromogranin A (CGA) and neuron-specific enolase (NSE) were evaluated by immunohistochemical staining (IHS) in representative positive cores for PCa. IHS intensity was graded as negative, 0; positive, 1+ and strongly positive, 2+. The proportion of the 1+ and 2+ areas in PCa cells was determined. PCa was considered to be in NED if ≥50% of the tumor cells were 1+ or 2+ for CGA or NSE. The observed IHS intensity (0/1+/2+) for AR, CGA and NSE was 0/4/16, 5/11/4 and 11/4/5 in HSPC patients and 9/3/16, 5/8/15 and 8/4/16 in CRPC patients, respectively. AR expression was positive in all the HSPC and 19/28 CRPC patients (P=0.0049). NED was observed in 9/20 HSPC and 20/28 CRPC patients (P=0.0649). NED was significantly associated with a negative AR expression in CRPC patients (P=0.0292). Multivariate analysis revealed that age, AR expression and strong NED were independent parameters for prognosis following castration-resistant progression. In conclusion, prostate biopsy following castration-resistant progression was necessary. AR was lost in a subset of CRPC. NED was observed more frequently in CRPC vs. HSPC and was associated with a worse prognosis.

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.

A nomogram based on age, prostate-specific antigen level, prostate volume and digital rectal examination for predicting risk of prostate cancer

Nomograms for predicting the risk of prostate cancer developed using other populations may introduce sizable bias when applied to a Chinese cohort. In the present study, we sought to develop a nomogram for predicting the probability of a positive initial prostate biopsy in a Chinese population. A total of 535 Chinese men who underwent a prostatic biopsy for the detection of prostate cancer in the past decade with complete biopsy data were included. Stepwise logistic regression was used to determine the independent predictors of a positive initial biopsy. Age, prostate-specific antigen (PSA), prostate volume (PV), digital rectal examination (DRE) status, % free PSA and transrectal ultrasound (TRUS) findings were included in the analysis. A nomogram model was developed that was based on these independent predictors to calculate the probability of a positive initial prostate biopsy. A receiver-operating characteristic curve was used to assess the accuracy of using the nomogram and PSA levels alone for predicting positive prostate biopsy. The rate for positive initial prostate biopsy was 41.7% (223/535). The independent variables used to predict a positive initial prostate biopsy were age, PSA, PV and DRE status. The areas under the receiver-operating characteristic curve for a positive initial prostate biopsy for PSA alone and the nomogram were 79.7% and 84.8%, respectively. Our results indicate that the risk of a positive initial prostate biopsy can be predicted to a satisfactory level in a Chinese population using our nomogram. The nomogram can be used to identify and counsel patients who should consider a prostate biopsy, ultimately enhancing accuracy in diagnosing prostate cancer.

Development of a nomogram for predicting the stone-free rate after transurethral ureterolithotripsy using semi-rigid ureteroscope

OBJECTIVES

To develop and to internally validate a novel nomogram for predicting the stone-free rate after transurethral ureterolithotripsy.

METHODS

A total of 412 patients with 534 ureteral stones were treated with transurethral ureterolithotripsy using semi-rigid ureteroscopes. Treatment efficacy was evaluated 3 months after the procedure. Multivariate stepwise logistic regression analysis was used to identify independent predictors of being stone-free in the model-building set. A total of 427 stones (80% of 534) were randomly allocated for identification and statistical analysis to build the model, and the remaining 107 (20%) were used for cross-validation. A nomogram for the stone-free rate was developed based on the final logistic regression model.

RESULTS

Stone length, number of stones, stone location and the presence of pyuria were independent factors related to the stone-free rate after transurethral ureterolithotripsy treatment, and these were used to develop a nomogram. In this nomogram, the area under the receiver operating characteristic curve was 0.7432 for the nomogram, 0.5641 for stone size, 0.5908 for the number of stones, 0.6594 for stone location and 0.6076 for pyuria. Validation using 20% of the data also achieved a reasonable predictive accuracy (area under the receiver operating characteristic curve = 0.682).

CONCLUSIONS

The first nomogram for predicting the stone-free rate after transurethral ureterolithotripsy was developed. It has a reasonable predictive accuracy, and in combination with extracorporeal shock wave lithotripsy nomograms, it might be useful for deciding treatment methods.

Investigation of optimal prostate biopsy schemes for Chinese patients with different clinical characteristics

PURPOSE

To investigate the optimal schemes of prostate biopsy according to prostate volume (PV), age and transrectal ultrasound (TRUS) status in Chinese men.

METHODS

923 consecutive patients who underwent initial TRUS-guided systematic 12-core prostate biopsy (12PBx) were enrolled in this study. The 12PBx was obtained by overlapping of conventional sextant, lateral base, mid-gland of peripheral zone and apex. Each sample was individually marked and inked before fixation. Patients were divided into 8 subgroups on the basis of independent risk factors investigated using logistic regression model. Subsequently, 12PBx was defined as self-control for the analysis of biopsy schemes (6-, 8- and 10PBx) on individual core basis. The prostate cancer detection rates (CDRs) of 6-, 8-, 10- and 12PBx were compared for each individual subgroup.

RESULTS

The 12PBx detected 253 (27.4%) cases of prostate cancer (PCa), of which 67.2, 47.1 and 61.3% were located in the base, mid-gland and apex, respectively. Multivariate analysis indicated that age, TRUS status and PV were independent risk factors for PCa detection. CDR increased with increasing biopsy cores. However, for patients with age ≥65 years, positive TRUS and PV <38.5 cm(3), CDR of 8PBx (30.6%) was similar to 10PBx (32.2%) and 12PBx (32.2%); for patients with age ≥65 years, negative TRUS and PV <38.5 cm(3) or ones with age ≥65 years, positive TRUS and PV ≥38.5 cm(3), 10PBx was as effective as 12PBx in detecting PCa (27.8, 27.5 vs. 28.9, 29.3%, respectively).

CONCLUSION

Age, TRUS status and PV were independent risk factors for PCa detection. Traditional sextant biopsy is not recommended. 8-, 10-, or 12PBx as an individual biopsy scheme might be adopted according to these risk factors for Chinese patients.

Early quality of life outcomes in patients with prostate cancer managed by high-dose-rate brachytherapy as monotherapy

OBJECTIVES

To evaluate the early quality of life outcomes in prostate cancer patients managed by high-dose-rate brachytherapy as monotherapy.

METHODS

A total of 51 patients with cT1c-T3aN0M0 prostate cancer treated between July 2007 and January 2010 were included in this study. The average age was 69 years, and the average initial serum prostate-specific antigen was 10.98 ng/mL. A total of 25, 18 and eight patients were considered to be low, intermediate and high risk, respectively. All patients received one implant of Ir-192 and seven fractions of 6.5 Gy within 3.5 days for a total prescribed dose of 45.5 Gy. For high-risk prostate cancer, neoadjuvant androgen deprivation therapy was carried out for at least 6 months, and continued after high-dose-rate brachytherapy. Quality of life outcomes were measured by using the International Prostate Symptom Score, the Functional Assessment of Cancer Therapy-Prostate and the International Index of Erectile Function Questionnaire. The oncological outcome was assessed by serum prostate-specific antigen and diagnostic imaging. Adverse events were also recorded.

RESULTS

The Functional Assessment of Cancer Therapy-Prostate scores decreased for a few months after high-dose-rate brachytherapy, and recovered to pretreatment condition thereafter. The International Prostate Symptom Score significantly increased 2 weeks after treatment for each of its items and their sum, and it returned to baseline after 12 weeks. Sexual function decreased at 2 and 4 weeks, and recovered after 12 weeks. Severe complications were rare. Within a median follow up of 17.2 months, two patients showed a prostate-specific antigen recurrence.

CONCLUSIONS

High-dose-rate brachytherapy for prostate cancer is a feasible treatment modality with acceptable toxicity and only a limited impact on the quality of life.

Can the prostate risk calculator based on Western population be applied to Asian population?

BACKGROUND

We developed a korean prostate cancer risk calculator (KPCRC) for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Korean male population (http://pcrc.korea.ac.kr). We compared its performance to prostate-specific antigen (PSA) testing and the Prostate Risk Calculator 3 (PRC 3) based on data from the Dutch part of European Randomized Study of Screening for Prostate Cancer (ERSPC), which predicts biopsy results for previously unscreened men.

METHODS

Data were collected from 602 Korean men who were previously unscreened and underwent initial ten-core prostate biopsies. Multiple logistic regression analysis was performed to determine the significant predictors. Area under the receiver operating characteristic curve (AUC) and calibration plots of both calculators were evaluated.

RESULTS

Prostate cancer (PCa) was detected in 172 (28.6%) men. Independent predictors of a positive biopsy included advanced age, elevated PSA levels, reduced volume of the transition zone, and abnormal digital rectal examination findings. The AUC of the KPCRC was higher than the PRC 3 and PSA alone on internal and external validation. Calibration plots of the KPCRC showed better performance than the other models on internal and external validation. Applying a cut-off of 10% of KPCRC implied that 251 of the 602 men (42%) would not have been biopsied and that 12 of the 172 PCa cases (7%) would not have been diagnosed.

CONCLUSIONS

The KPCRC improves the performance of the PRC 3 and PSA testing in predicting Korean population's risk of PCa. It implies that Asian populations need their own risk calculators for PCa.

Development and External Validation of a Nomogram Predicting the Probability of Significant Gleason Sum Upgrading among Japanese Patients with Localized Prostate Cancer

Objective. The aim of this study is to develop a prognostic model capable of predicting the probability of significant upgrading among Japanese patients. Methods. The study cohort comprised 508 men treated with RP, with available prostate-specific antigen levels, biopsy, and RP Gleason sum values. Clinical and pathological data from 258 patients were obtained from another Japanese institution for validation. Results. Significant Gleason sum upgrading was recorded in 92 patients (18.1%) at RP. The accuracy of the nomogram predicting the probability of significant Gleason sum upgrading between biopsy and RP specimens was 88.9%. Overall AUC was 0.872 when applied to the validation data set. Nomogram predictions of significant upgrading were within 7.5% of an ideal nomogram. Conclusions. Nearly one-fifth of Japanese patients with prostate cancer will be significantly upgraded. Our nomogram seems to provide considerably accurate predictions regardless of minor variations in pathological assessment when applied to Japanese patient populations.

Pre-operative prediction of advanced prostatic cancer using clinical decision support systems: accuracy comparison between support vector machine and artificial neural network

Objective

The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models.

Materials and Methods

Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n = 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05).

Results

The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer.

Conclusion

The performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

Prognostic models for renal cell carcinoma recurrence: external validation in a Japanese population

The aim of the present study was to compare the accuracy of three prognostic models in predicting recurrence-free survival among Japanese patients who underwent nephrectomy for non-metastatic renal cell carcinoma (RCC). Patients originated from two centers: Chiba University Hospital (n = 152) and Chiba Cancer Center (n = 65). The following data were collected: age, sex, clinical presentation, Eastern Cooperative Oncology Group performance status, surgical technique, 1997 tumor-node-metastasis stage, clinical and pathological tumor size, histological subtype, disease recurrence, and progression. Three western models, including Yaycioglu's model, Cindolo's model and Kattan's nomogram, were used to predict recurrence-free survival. Predictive accuracy of these models were validated by using Harrell's concordance-index. Concordance-indexes were 0.795 and 0.745 for Kattan's nomogram, 0.700 and 0.634 for Yaycioglu's model, and 0.700 and 0.634 for Cindolo's model, respectively. Furthermore, the constructed calibration plots of Kattan's nomogram overestimated the predicted probability of recurrence-free survival after 5 years compared with the actual probability. Our findings suggest that despite working better than other predictive tools, Kattan's nomogram needs be used with caution when applied to Japanese patients who have undergone nephrectomy for non-metastatic RCC.

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.

Clinical effect of naftopidil on the quality of life of patients with lower urinary tract symptoms suggestive of benign prostatic hyperplasia: a prospective study

OBJECTIVES

To investigate the benefit of alpha1-adrenoceptor antagonist naftopidil on the quality of life (QOL) of patients with lower urinary tract symptoms suggestive of benign prostatic hyperplasia (BPH/LUTS).

METHODS

A total of 99 men with BPH/LUTS were prospectively recruited. The Short Form-8 (SF-8) was used for generic QOL assessment and each parameter was compared with the norm in these patients. Longitudinal changes were evaluated using the SF-8 and the International Prostatic Symptoms Score (I-PSS) at baseline, 4 and 8 weeks after naftopidil administration. The relationship between SF-8 and I-PSS was analyzed.

RESULTS

Five of eight components in the SF-8 were significantly lower than the Japanese national norm at baseline. SF-8 score was improved by naftopidil at 4 and 8 weeks in general health (GH) and physical component summary (PCS) in the patients in their 70s. Mental health (MH) and mental component summary (MCS) were improved at 8 weeks in patients in their 60s. When analyzing the whole cohort, SF-8 GH, role emotional (RE) and MH had improved at 8 weeks, which was similar to the norm, and bodily pain (BP) results were better. Compared with the baseline, total I-PSS, storage/voiding symptoms and QOL index scores improved significantly under naftopidil. Each component of I-PSS (except for hesitancy) correlated with SF-8 sub-scales (except for BP) to some extent.

CONCLUSIONS

BPH/LUTS impairs generic QOL, which is improved by naftopidil treatment. SF-8 can be a useful instrument to assess the efficacy of BPH/LUTS treatment because its simplicity to complete and analyze, and its meaningful relationship to I-PSS.

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.

Predicting the probability of significant prostate cancer in Japanese men with serum prostate-specific antigen less than 10 ng/mL: development of a novel pre-biopsy nomogram

OBJECTIVES

To develop and assess a new nomogram incorporating pre-biopsy clinical data to predict significant prostate cancer in Japanese men with a serum prostate-specific antigen (PSA) level of less than 10 ng/mL.

METHODS

We collected pre-biopsy data from 620 men with a serum total PSA of less than 10 ng/mL. They included 491 men with a negative biopsy and 129 men who were confirmed to have histological prostate cancer and subsequently underwent radical prostatectomy. Clinically significant tumors were defined as those with a tumor volume larger than 0.5 mL and/or a Gleason score of 7 or more.

RESULTS

One hundred and seven prostatectomy patients had clinically significant cancers. Stepwise multivariate logistic regression analysis showed that digital rectal examination findings, PSA adjusted for transition zone volume and free-to-total PSA ratio were the significant independent predictors of significant cancers (P < 0.0001). Using these pre-biopsy independent factors, a nomogram was developed to predict significant cancers. According to a receiver operating characteristics analysis, the nomogram showed an area under the curve of 0.831.

CONCLUSION

This represents the first nomogram to predict the probability of clinically significant cancers before biopsy. This tool is most likely to be useful in the management of patients with moderate to elevated PSA.

Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine

PURPOSE

We developed a multiple logistic regression model, an artificial neural network (ANN), and a support vector machine (SVM) model to predict the outcome of a prostate biopsy, and compared the accuracies of each model.

METHOD

One thousand and seventy-seven consecutive patients who had undergone transrectal ultrasound (TRUS)-guided prostate biopsy were enrolled in the study. Clinical decision models were constructed from the input data of age, digital rectal examination findings, prostate-specific antigen (PSA), PSA density (PSAD), PSAD in transitional zone, and TRUS findings. The patients were divided into the training and test groups in a randomized fashion. Areas under the receiver operating characteristic (ROC) curve (AUC, Az) were calculated to summarize the overall performance of each decision model for the task of prostate cancer prediction.

RESULTS

The Az values of the ROC curves for the use of multiple logistic regression analysis, ANN, and the SVM were 0.768, 0.778, and 0.847, respectively. Pairwise comparison of the ROC curves determined that the performance of the SVM was superior to that of the ANN or the multiple logistic regression model.

CONCLUSION

Image-based clinical decision support models allow patients to be informed of the actual probability of having a prostate cancer.

A population-based assessment of perioperative mortality after nephroureterectomy for upper-tract urothelial carcinoma

OBJECTIVES

To examine the perioperative mortality rates at 90 days (90 dM) after nephroureterectomy (NU) and to devise a model capable of identifying individuals at an elevated 90 dM risk. NU represents the surgical standard of care for patients with invasive, nonmetastatic upper-tract urothelial carcinoma. However, this major abdominal surgery may be associated with a nonnegligible rate of perioperative mortality.

METHODS

We identified 6078 upper-tract urothelial carcinoma patients treated with NU from 17 registries of the Surveillance, Epidemiology, and End Results database, between 1988 and 2006. Stratified analyses quantified 90 dM rates according to age, gender, race, year of diagnosis, tumor location, surgery type, T stage, tumor grade, and lymph node status. Subsequently, multivariable logistic regression models identified predictors of 90 dM within the development cohort (n = 3039). The accuracy and calibration of the model were tested in an independent validation cohort (n = 3039).

RESULTS

The overall 90 dM rate was 4.4%. Continuously coded age and T and N stages achieved an independent predictor status in multivariable logistic regression models and represented key variables for prediction of individual 90 dM risk after NU, with 73.4% accuracy. Excellent correlation between predicted and observed 90 dM rates after NU was recorded.

CONCLUSIONS

In this large-scale population-based analysis of perioperative mortality after NU, age and T and N stages emerged as the most informative predictor of 90 dM. We recommend the use of this tool in individual decision-making and in informed consent considerations.

External validation of a nomogram predicting the probability of prostate cancer Gleason sum upgrading between biopsy and radical prostatectomy pathology among Japanese patients

OBJECTIVES

To explore the rate of upgrading in a contemporary cohort from 2 Japanese institutions, and evaluating the predictive accuracy of the nomogram when applied to patients, regardless of race. Previous reports have indicated that a maximum of 43% of men with prostate cancer will show an upgraded Gleason score from biopsy to radical prostatectomy (RP). A preparative nomogram was developed at the University of Hamburg to predict the probability of upgrading from biopsy to RP specimen.

METHODS

Clinical and pathologic data of 503 patients from 2 Japanese institutions were supplied for validation. Nomogram-predicted probabilities of upgrading from biopsy to RP specimen were compared with actual rate of upgrading. The area under the receiver operating characteristic curve (AUC) was calculated for all patients. Calibration of the nomogram was achieved by comparing the predicted upgrading rate with that of an ideal nomogram.

RESULTS

Gleason sum upgrading was recorded in 29.8% of patients at RP. Accuracy of the nomogram was 79.2% (confidence interval, 75.1%-83.2%). Overall AUC was 0.79 when applied to the validation dataset, with individual institutional AUCs ranging from 0.79-0.80. Nomogram predictions of upgrading were not within 10% of an ideal nomogram.

CONCLUSIONS

Gleason sum upgrading between biopsy and final pathology represents an important consideration in treatment decision-making, and nearly one third of patients with prostate cancer will be upgraded. The Hamburg nomogram seems to provide reasonably accurate predictions regardless of minor variations in pathologic assessment, but is not necessarily so accurate when applied to Japanese patient population.

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.

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.

Clinical effect of alpha 1D/A adrenoceptor inhibitor naftopidil on benign prostatic hyperplasia: an international prostate symptom score and King's Health Questionnaire assessment

OBJECTIVES

To examine the effect of alpha 1D/A adrenoceptor inhibitor naftopidil on health-related quality of life (QOL) in men with benign prostatic hyperplasia (BPH).

METHODS

A total of 56 newly diagnosed patients with symptomatic BPH were prospectively enrolled and treated with 50 mg naftopidil daily for more than 12 weeks. All underwent pre-treatment documentation of lower urinary tract symptoms, QOL assessment using the international prostate symptom score (IPSS) and King's Health Questionnaire (KHQ), and uroflowmetry. A post-treatment assessment was performed at 12 weeks.

RESULTS

IPSS scores as well as QOL index showed a significant improvement after naftopidil administration. Similarly, all seven domains except general health perceptions and social limitations in the KHQ questionnaire were significantly improved. When dividing the patients into overactive bladder (OAB) and non-OAB groups, only the OAB group showed significant improvement in almost all the domains of KHQ. Change ratios of the IPSS were not associated with those of KHQ domain scores in the OAB group. On the other hand, in the non-OAB group more domains presented improvements, which were associated with those of IPSS scores.

CONCLUSIONS

Twelve-week treatment with naftopidil for symptomatic BPH patients is associated with significant improvement in the IPSS, QOL index, maximum urinary flow rate, post-void residual urine volume (PVR) and almost all domains in KHQ. KHQ is useful for the evaluation of clinical response in BPH patients, particularly in those with associated OAB.

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.

Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy

OBJECTIVES

Using cohorts examined by extended biopsy, we developed and validated multivariate models predicting prostate cancer on initial biopsy and examined whether these extended biopsy-based models outperform previously established models.

METHODS

Initial extended biopsy (median 22 cores) was performed in 1509 Japanese men including 1083 at Tokyo Medical and Dental University Hospital (TMDU) and 426 at Cancer Institute Hospital (CIH). Logistic regression-based nomograms 1 and artificial neural network (ANN) 1 incorporating age, digital rectal examination, and prostate-specific antigen (PSA) and free PSA, and nomogram 2 and ANN2 further incorporating transrectal ultrasound (TRUS) findings and prostate volume were constructed on the TMDU data. These and previously established models were externally validated on the CIH data set and predictive accuracy was compared directly.

RESULTS

Without TRUS-derived information, nomogram 1 outperformed the ANN1. With TRUS-derived information, nomogram 2 was more accurate than ANN2. External validation revealed applicability of the Western models to Japanese population, superiority of the nomograms over ANN models, and better predictive accuracy of our extended biopsy-based nomograms than the previous 6-10-core biopsy-based models. Using nomograms 1 and 2, 16% and 19% unnecessary biopsies would be saved at 95% sensitivity.

CONCLUSIONS

We developed new nomograms predicting prostate cancer on initial biopsy in men with PSA <20ng/ml. Predictive accuracy of these extended biopsy-based nomograms is better than those of previously established models based on 6-10-core biopsies. Our models might help clinicians to decide if a patient requires biopsy and to avoid unnecessary biopsies.