Predictive modeling for the presence of prostate carcinoma using clinical, laboratory, and ultrasound parameters in patients with prostate specific antigen levels ? 10 ng/mL

Diagnostic evaluation and incorporation of PSA density and the prostate imaging and data reporting system (PIRADS) version 2 classification in risk-nomograms for prostate cancer

PURPOSE

The diagnostic approach for prostate cancer still depends on PSA and DRE.

OBJECTIVES

to evaluate the diagnostic validity of PSA-Density and PIRADSv2 as diagnostic tests regarding biopsy results, and to design nomograms that include all diagnostic variables for malignancy, significant tumor (ST) and high-grade tumor.

METHODS

Cross-sectional study which included men with PSA ≥ 4 ng/ml and/or suspicious DRE, PIRADSv2 ≥ 3 lesions on multiparametric MRI and prostate biopsy. The gold standard test was the maximum ISUP of the targeted biopsy per patient (malignancy: ISUP ≥ 1, ST: ISUP ≥ 2, high-grade tumor: ISUP ≥ 4). Association and logistic regression tests were used and diagnostic validity parameters using PSA-Density and PIRADSv2 classification was analyzed. Nomograms were designed for malignancy, ST, and high-grade tumor using the best model selection procedure from all possible equations.

RESULTS

336 men with median age, PSA and PSA-Density of 67.7 years (IQR:12.6), 6.3 ng/ml (IQR:3.3) and 0.12 ng/ml/cc (IQR:0.10), respectively; 63 index lesions were PIRADS3, 204 PIRADS4, and 69 PIRADS5. 65.8% and 37.8% were malignant and ST, respectively. The significant positive association highlighted between malignancy and ST with age, DRE, PSA-Density and PIRADSv2. PSA-Density and PIRADSv2 ≥ 3 presented the highest sensitivity to detect malignancy, and their combination showed sensitivity nearly 95% (AUC:0.803). Nomograms for malignancy and ST included the variables age, DRE, PSA-Density, and PIRADSv2 with a sensitivity closely 91% (AUC:0.833), and a specificity of almost 85% for ST, exposing risk < 5% for ST when PSA-Density is < 0.15, not suspicious DRE and PIRADS3.

CONCLUSION

PSA-Density and PIRADSv2 classification in risk nomograms can provide highly relevant information to increase the accuracy in the diagnosis of PC and ST.

A new model to predict acute kidney injury after cardiac surgery in patients with renal insufficiency

Objective

To establish a simple model for predicting postoperative acute kidney injury (AKI) requiring renal replacement therapy (RRT) in patients with renal insufficiency (CKD stages 3–4) who underwent cardiac surgery.

Methods

A total of 330 patients were enrolled. Among them, 226 were randomly selected for the development group and the remaining 104 for the validation group. The primary outcome was AKI requiring RRT. A nomogram was constructed based on the multivariate analysis with variables selected by the application of the least absolute shrinkage and selection operator. Meanwhile, the discrimination, calibration, and clinical power of the new model were assessed and compared with those of the Cleveland Clinic score and Simplified Renal Index (SRI) score in the validation group. Results: The rate of RRT in the development group was 10.6% (n = 24), while the rate in the validation group was 14.4% (n = 15). The new model included four variables such as postoperative creatinine, aortic cross‐clamping time, emergency, and preoperative cystatin C, with a C-index of 0.851 (95% CI, 0.779–0.924). In the validation group, the areas under the receiver operating characteristic curves for the new model, SRI score, and Cleveland Clinic score were 0.813, 0.791, and 0.786, respectively. Furthermore, the new model demonstrated greater clinical net benefits compared with the Cleveland Clinic score or SRI score.

Conclusions

We developed and validated a powerful predictive model for predicting severe AKI after cardiac surgery in patients with renal insufficiency, which would be helpful to assess the risk for severe AKI requiring RRT.

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.

Association between vasectomy and risk of prostate cancer: a meta-analysis

BACKGROUND

The debate over the association between vasectomy and prostate cancer has been lasted about 40 years and there is no sign of stopping. In the present study, we aimed to evaluate whether vasectomy is associated with prostate cancer based on the most comprehensive and up-to-date evidence available.

METHODS

The PubMed, Cochrane Library, and EMBASE databases were systematically searched inception to March 14, 2021 without year or language restriction. Multivariable adjusted risk ratios (RRs) were used to assess each endpoint. Risk of bias was assessed using the Newcastle-Ottawa scale.

RESULTS

A total of 58 studies involving 16,989,237 participants fulfilled inclusion criteria. There was significant association of vasectomy with risk of any prostate cancer (risk ratio, 1.18, 95% CI, 1.07-1.31). Association between vasectomy and advanced prostate cancer (risk ratio, 1.06, 95% CI, 1.01-1.12), low-grade prostate cancer (risk ratio, 1.06, 95% CI, 1.02-1.10), and intermediate-grade prostate cancer (risk ratio, 1.12, 95% CI, 1.03-1.22) were significant. There was no significant association between vasectomy and prostate cancer-specific mortality (risk ratio, 1.01, 95% CI, 0.93-1.10).

CONCLUSIONS

This study found that vasectomy was associated with the risk of any prostate cancer and advanced prostate cancer. From the current evidence, patients should be fully informed of the risk of prostate cancer before vasectomy.

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

Decision Tree Analysis for Prostate Cancer Prediction in Patients with Serum PSA 10 ng/ml or Less

Serum prostate-specific antigen (PSA) testing increases the number of persons who undergo prostate biopsy. However, the best possible strategy for selecting patients for prostate biopsy has not yet been defined. The aim of this study was to develop a classification and regression tree (CART) decision model that can be used to predict significant prostate cancer (PCa) in the course of prostate biopsy for patients with serum PSA levels of 10 ng/ml or less.

The following clinicopathological characteristics of patients who had undergone ultrasound-guided transrectal prostate biopsy were collected: age, PSA, digital rectal examination, volume of the prostate, and PSA density (PSAD). CART analysis was carried out by using all predictors. Different aspects of the predictive performances of the prediction model were assessed.

In this retrospective study, significant PCa values were detected in 26 (26.8%) of a total of 97 patients. The CART model had three branching levels based on PSAD as the most decisive variable and age. The model sensitivity was 73.1%, the specificity was 80.3% and the accuracy was 78.3%. Our model showed an area under the receiver operating characteristic curve of 82.6%. The model was well calibrated.

In conclusion, CART analysis determined that PSAD was the key parameter for the identification of patients with a minimal risk for positive biopsies. The model showed a good discrimination capacity that surpassed individual predictors. However, before recommending its use in clinical practice, an evaluation of a larger and more complete database is necessary for the prediction of significant PCa.

Prediction of lateral pelvic lymph node metastasis from lower rectal cancer using magnetic resonance imaging and risk factors for metastasis: Multicenter study of the Lymph Node Committee of the Japanese Society for Cancer of the Colon and Rectum

PURPOSE

The goal of the study was to examine prediction of lateral pelvic lymph node (LPLN) metastasis from lower rectal cancer using a logistic model including risk factors for LPLN metastasis and magnetic resonance imaging (MRI) clinical LPLN (cLPLN) status, compared to prediction based on MRI alone.

METHODS

The subjects were 272 patients with lower rectal cancer who underwent MRI prior to mesorectal excision combined with LPLN dissection (LPLD) at six institutes. No patients received neoadjuvant therapy. Prediction models for right and left pathological LPLN (pLPLN) metastasis were developed using cLPLN status, histopathological grade, and perirectal lymph node (PRLN) status. For evaluation, data for patients with left LPLD were substituted into the right-side equation and vice versa.

RESULTS

Left LPLN metastasis was predicted using the right-side model with accuracy of 86.5%, sensitivity 56.4%, specificity 92.7%, positive predictive value (PPV) 61.1%, and negative predictive value (NPV) 91.2%, while these data using MRI cLPLN status alone were 80.4, 76.9, 81.2, 45.5, and 94.5%, respectively. Similarly, right LPLN metastasis was predicted using the left-side equation with accuracy of 83.8%, sensitivity 57.8%, specificity 90.4%, PPV 60.5%, and NPV 89.4%, and the equivalent data using MRI alone were 78.4, 68.9, 80.8, 47.7, and 91.1%, respectively. The AUCs for the right- and left-side equations were significantly higher than the equivalent AUCs for MRI cLPLN status alone.

CONCLUSIONS

A logistic model including risk factors for LPLN metastasis and MRI findings had significantly better performance for prediction of LPLN metastasis compared with a model based on MRI findings alone.

The Association Between Vasectomy and Prostate Cancer: A Systematic Review and Meta-analysis

Importance

Despite 3 decades of study, there remains ongoing debate regarding whether vasectomy is associated with prostate cancer.

Objective

To determine if vasectomy is associated with prostate cancer.

Data Sources

The MEDLINE, EMBASE, Web of Science, and Scopus databases were searched for studies indexed from database inception to March 21, 2017, without language restriction.

Study Selection

Cohort, case-control, and cross-sectional studies reporting relative effect estimates for the association between vasectomy and prostate cancer were included.

Data Extraction and Synthesis

Two investigators performed study selection independently. Data were pooled separately by study design type using random-effects models. The Newcastle-Ottawa Scale was used to assess risk of bias.

Main Outcomes and Measures

The primary outcome was any diagnosis of prostate cancer. Secondary outcomes were high-grade, advanced, and fatal prostate cancer.

Results

Fifty-three studies (16 cohort studies including 2 563 519 participants, 33 case-control studies including 44 536 participants, and 4 cross-sectional studies including 12 098 221 participants) were included. Of these, 7 cohort studies (44%), 26 case-control studies (79%), and all 4 cross-sectional studies were deemed to have a moderate to high risk of bias. Among studies deemed to have a low risk of bias, a weak association was found among cohort studies (7 studies; adjusted rate ratio, 1.05; 95% CI, 1.02-1.09; P < .001; I2 = 9%) and a similar but nonsignificant association was found among case-control studies (6 studies; adjusted odds ratio, 1.06; 95% CI, 0.88-1.29; P = .54; I2 = 37%). Effect estimates were further from the null when studies with a moderate to high risk of bias were included. Associations between vasectomy and high-grade prostate cancer (6 studies; adjusted rate ratio, 1.03; 95% CI, 0.89-1.21; P = .67; I2 = 55%), advanced prostate cancer (6 studies; adjusted rate ratio, 1.08; 95% CI, 0.98-1.20; P = .11; I2 = 18%), and fatal prostate cancer (5 studies; adjusted rate ratio, 1.02; 95% CI, 0.92-1.14; P = .68; I2 = 26%) were not significant (all cohort studies). Based on these data, a 0.6% (95% CI, 0.3%-1.2%) absolute increase in lifetime risk of prostate cancer associated with vasectomy and a population-attributable fraction of 0.5% (95% CI, 0.2%-0.9%) were calculated.

Conclusions and Relevance

This review found no association between vasectomy and high-grade, advanced-stage, or fatal prostate cancer. There was a weak association between vasectomy and any prostate cancer that was closer to the null with increasingly robust study design. This association is unlikely to be causal and should not preclude the use of vasectomy as a long-term contraceptive option.

Integrated predictive model for prostatic cancer using clinical, laboratory and ultrasound data

OBJECTIVE

to develop a predictive model to estimate the probability of prostate cancer prior to biopsy.

METHODS

from September 2009 to January 2014, 445 men underwent prostate biopsy in a radiology service. We excluded from the study patients with diseases that could compromise the data analysis, who had undergone prostatic resection or used 5-alpha-reductase inhibitors. Thus, we selected 412 patients. Variables included in the model were age, prostate specific antigen (PSA), digital rectal examination, prostate volume and abnormal sonographic findings. We constructed Receiver Operating Characteristic (ROC) curves and calculated the areas under the curve, as well as the model's Positive Predictive Value (PPV) .

RESULTS

of the 412 men, 155 (37.62%) had prostate cancer (PC). The mean age was 63.8 years and the median PSA was 7.22ng/ml. In addition, 21.6% and 20.6% of patients had abnormalities on digital rectal examination and image suggestive of cancer by ultrasound, respectively. The median prostate volume and PSA density were 45.15cm3 and 0.15ng/ml/cm3, respectively. Univariate and multivariate analyses showed that only five studied risk factors are predictors of PC in the study (p<0.05). The PSA density was excluded from the model (p=0.314). The area under the ROC curve for PC prediction was 0.86. The PPV was 48.08% for 95%sensitivity and 52.37% for 90% sensitivity.

CONCLUSION

the results indicate that clinical, laboratory and ultrasound data, besides easily obtained, can better stratify the risk of patients undergoing prostate biopsy.

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.

Targeted Drug Delivery in Cancer Therapy

Chemotherapy has been the main modality of treatment for cancer patients; however, its success rate remains low, primarily due to limited accessibility of drugs to the tumor tissue, their intolerable toxicity, development of multi-drug resistance, and the dynamic heterogeneous biology of the growing tumors. Better understanding of tumor biology in recent years and new targeted drug delivery approaches that are being explored using different nanosystems and bioconjugates provide optimism in developing successful cancer therapy. This article reviews the possibilities and challenges for targeted drug delivery in cancer therapy.

The nomogram conundrum: a demonstration of why a prostate cancer risk model in Turkish men underestimates prostate cancer risk in the USA

PURPOSE

The utility of a nomogram is based on the patient population it is designed for-and their inherent properties and biases. Our aim was to demonstrate the variability in predictive model accuracy and utility between different populations.

METHODS

Our model is based on 761 men who underwent initial TRUS biopsy at a single institution in Turkey. Patients were included if they had at least 10 cores on biopsy and PSA level <20 ng/ml. Multivariable logistic regression models were used to develop a new nomogram. External validity was tested with two different cohorts one from another institution in Turkey (N = 136) and cohort from USA (N = 2242).

RESULTS

Prostate cancer (PCa) and high-grade PCa was diagnosed in 249/761 (32.7 %) and 101/761 (13.3 %) patients from Ankara, Turkey, respectively. Predictors of PCa were age (p < 0.0001, OR 2.11), PSA (p = 0.044, OR 1.44), PV (p < 0.0001, OR 0.38), %fPSA (p = 0.016, OR 0.72), and abnormal DRE (p < 0.0001, OR 2.05). The predictive accuracy (c-index) of our nomogram was 73 %. C-indices of 71 and 70 % were recorded in external validation cohorts from Turkey and the USA, respectively. Virtually ideal calibration was recorded for the internal validated predictive model, and good calibration was recorded when applied to the Istanbul cohort. However, the model/nomogram underestimates PCa risk in the US cohort.

CONCLUSION

This is the first nomogram predicting the risk of PCa at initial biopsy in a Turkish population and provides a good risk estimation tool with good predictive accuracy and calibration in the Turkish populations. However, our study demonstrates the poor transferability of predictive tools to widely different populations.

Developing a follow-up strategy for patients with PSA ranging from 4 to 10 ng/ml via a new model to reduce unnecessary prostate biopsies

Objective

The aim of this study was to develop a follow-up strategy based on the new model to reduce unnecessary prostate biopsies in patients with prostate specific antigen (PSA) ranging from 4 to 10 ng/ml.

Methods

A total of 436 patients with PSA ranging from 4 to 10 ng/ml who had undergone transrectal ultrasound (TRUS)-guided prostate biopsy were evaluated during the first stage. Age, PSA, free PSA (fPSA), digital rectal examination (DRE) findings, ultrasonic hypoechoic mass, ultrasonic microcalcifications, prostate volume (PV) and PSA density (PSAD) were considered as predictive factors. A multiple logistic regression analysis involving a backward elimination selection procedure was applied to select independent predictors. After a comprehensive analysis of all results, we developed a new model to assess the risk of prostate cancer and an effective follow-up strategy.

Results

Age, PSA, PV, fPSA, rate of abnormal DRE findings and rate of hypoechoic masses detected by TRUS were included in our model. A significantly greater area under the receiver-operating characteristic curve was obtained in our model when compared with using PSA alone (0.782 vs. 0.566). Patients were grouped according to the value of prostate cancer risk (PCaR). In the second stage of our study, patients with PCaR>0.52 were recommended to undergo biopsies immediately while the rest of the patients continued close follow-up observation. Compared with the first stage, the detection rate of PCa in the second stage was significantly increased (33.0% vs 21.1%, p = 0.012). There was no significant difference between the two stages in distribution of the Gleason score (p = 0.808).

Conclusions

We developed a follow-up strategy based on the new model, which reduced unnecessary prostate biopsies without delaying patients’ diagnoses and treatments.

Impact of PSA and DRE on histologic findings at prostate biopsy in Turkish men over 75 years of age

Prostate specidic antigen (PSA) and digital rectal examination (DRE) are the known predictive factors for positive prostate biopsies differing according to the age, region and race. There have been only very limited studies about the impact of PSA on histological findings at prostate biopsy in Turkey. The aim of this study was to evaluate the impact of PSA and clinical stage on histologic findings of prostate biopsy in men older than 75 years of age as a first study in the Turkish population. A total of 1,645 consecutive prostate biopsies were included, with 194 men aged 75 or older. Cancer was identified in 104 patients (53.6%). Of the 104 positive biopsies, Gleason scores were less than 7 in 53 (49%) patients, 7 or greater in 51 (51%) patients. Positive prostate biopsies were significantly correlated with advanced age (p=0.0001), abnormal DRE (p=0.0001) and raised PSA (p=0.0001). The prostate volume was significantly correlated with advanced age especially in prostate cancer patients over 75 years, compared with those under 75 (p=0.0001). These results are useful for counseling men older than 75 years for prostate cancer detection. However, PCa screening decisions are currently based on urologist judgment and detection of latent asymptomatic disease is an important concern regarding costs, overdiagnosis, overtreatment and quality of life (QOL) for men aged 75 years and older. Healthy old patients with a long life expectancy need to be carefully evaluated for eligibility for PCa screening.

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.

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.

Left lobe of the prostate during clinical prostate cancer screening: the dark side of the gland for right-handed examiners

BACKGROUND

The predictive value of the abnormality side during digital rectal examination (DRE) has never been studied, suggesting that physicians examined the left lobe of the gland as well as the right lobe. We aimed to assess the predictive value of the side of DRE abnormality for prostate cancer (PCa) detection and aggressiveness in right-handed urologists.

METHODS

An analysis of a prospective database was carried out that included all consecutive men undergoing prostate biopsies between 2001 and 2012. The main end point was the predictive value of the abnormality side during DRE for cancer detection in clinically suspicious unilateral T2 disease. The diagnostic performance of left- versus right-sided abnormality was also assessed in terms of sensitivity, specificity and negative/positive predictive values.

RESULTS

Overall, 308 patients had a suspicious unilateral clinical disease (detection rate 57.5%). The cancer detection rate was significantly higher in case of left-sided compared with right-sided clinical T2 stage (odds ratio 2.1). In case of left-sided disease, the number of positive cores, the rate of perineural invasion, the rate of primary grade 4 pattern and the percentage of cancer involvement per core were significantly higher compared with those reported for right-sided disease. The predictive value of abnormality laterality for cancer detection and aggressiveness remained statistically independent in multivariate models. The positive predictive value for cancer detection was 64.6 in case of suspicious left-sided disease versus 46.9 in case of right-sided disease.

CONCLUSIONS

The risks of detecting PCa and aggressive disease on biopsy are significantly higher when DRE reveals a suspicious left-sided clinical disease as compared with right-sided disease. Right-handed physicians should be aware of this variance in diagnostic performance and potential underdetection of left-sided clinical disease, and should improve their examination of the left lobe of the gland by conducting longer exams or changing the patient's position.

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.

Clinico-pathological Correlation of Digital Rectal Examination Findings Amongst Nigerian Men with Prostatic Diseases: A Prospective Study of 236 Cases

Aims and Objective:

This study aims at correlating different digital rectal examination (DRE) abnormalities with histopathological results in patients with prostatic diseases.

Materials and Methods:

A prospective study of 236 patients who underwent prostate needle biopsy (PNB). Inclusion criteria were presence of abnormal DRE findings or elevated prostate specific antigen above 4 ng/ml or both. They all had 10-core extended transrectal biopsy and specimens were sent for histopathological examination. Correlations were made between DRE findings and histopathology results. Two separate multivariate logistic regression models were created; the first evaluated the relationship of predictors (DRE findings) to the likelihood of detecting cancer and the second explored predictors of high-grade cancer on PNB.

Results:

Two hundred and thirty-six patients were enrolled with a mean age of 66.9 years and range of 43-90 years. Histopathology results were malignant in 102 (43.2%) and benign in 134 (56.8%). Ninety-one (38.6%) and 145 (61.4%) had normal DRE and abnormal DRE findings with cancer detection rates of 23.1% and 55.8% respectively. Nodular prostate is the most common abnormality in 63.4% patients with abnormal DRE. Each sign of DRE had different predictive value with enhanced positive predictive value when combinations of abnormalities are present. Abnormal DRE is an independent predictor of high-grade tumor. Mean Gleason scores were 4.7 and 7.1 in patients with normal and abnormal DRE respectively.

Conclusion:

DRE is a useful and important tool in assessing patients with suspected prostate diseases who need prostate biopsy. An abnormal DRE correlated well with prostate cancer and independently predicted high-grade disease in these men.

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.

Benign prostatic hyperplasia: clinical manifestations and evaluation

Benign prostatic hyperplasia (BPH) is a very common condition, related to aging and causing symptoms, called lower urinary tract symptoms. On account of its huge prevalence, it is important for clinicians who are involved in the management of patients with BPH to be aware of the very strict recommendations for BPH evaluation. In this article, we describe the different steps and procedures doctors should follow to evaluate these patients; symptoms and signs of BPH are reviewed, as well as the clinical evaluation steps and examinations available. The basic evaluation of the patients with BPH should include, according to the recommendations of the most relevant international guidelines, lower urinary tract symptoms evaluation with appropriate symptom scores, digital rectal examination, voiding charts, prostate-specific antigen and creatinine measurement, urinalysis, and imaging of the urinary tract.

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.

Positive predictive value of prostate biopsy indicated by prostate-specific-antigen-based prostate cancer screening: trends over time in a European randomized trial*

Study Type--Diagnosis (validating cohort) Level of Evidence 1b. What's known on the subject? and What does the study add? The European Randomized study of Screening for Prostate Cancer (ERSPC) showed a reduction in prostate cancer mortality of 21% for PSA-based screening at a median follow-up of 11 years. In the ERSPC, men are screened at 4-year intervals. A prostate biopsy is recommended for men with a PSA level ≥ 3.0 ng/mL. The study shows that the positive predictive value (PPV) of a prostate biopsy indicated by PSA-based screening remains equal throughout consecutive screening rounds in men without a previous biopsy. In men who have previously had a benign biopsy, the PPV drops considerably, but 20% of the cancers detected still show aggressive characteristics.

OBJECTIVE

• To assess the positive predictive value (PPV) of prostate biopsy, indicated by a prostate-specific antigen (PSA) threshold of ≥ 3.0 ng/mL, over time, in the Rotterdam section of the European Randomized study of Screening for Prostate Cancer (ERSPC).

PATIENTS AND METHODS

• In the Rotterdam section of the ERSPC, a total of 42,376 participants, aged 55-74 years, identified from population registries were randomly assigned to a screening or control arm. • For the ERSPC men undergo PSA screening at 4-year intervals. A total of three screening rounds were evaluated; therefore, only men aged 55-69 years at the first screening were eligible for the present study.

RESULTS

• PPVs for men without previous biopsy remained equal throughout the three subsequent screenings (25.5, 22.3 and 24.8% respectively). • Conversely, PPVs for men with a previous negative biopsy dropped significantly (12.0 and 15.2% at the second and third screening, respectively). • Additionally, in men with and without previous biopsy, the percentage of aggressive prostate cancers (clinical stage >T2b, Gleason score ≥ 7) decreased after the first round of screening from 44.4 to 23.8% in the second (P < 0.001) and 18.6% in the third round (P < 0.001). • Repeat biopsies accounted for 24.6% of all biopsies, but yielded only 8.6% of all aggressive cancers.

CONCLUSIONS

• In consecutive screening rounds the PPV of PSA-based screening remains equal in previously unbiopsied men. • In men with a previous negative biopsy the PPV drops considerably, but 20% of cancers detected still show aggressive characteristics. • Individualized screening algorithms should incorporate previous biopsy status in the decision to perform a repeat biopsy with the aim of further reducing unnecessary biopsies.

[Transrectal biopsy scheme can predict incorrect histological grading in prostate cancer]

OBJECTIVE

To identify factors that might explain why a prostate with a Gleason score (GS) <7 in the biopsy specimen can turn out to have a GS ≥7 in the surgical specimen.

MATERIAL AND METHODS

We compared the GS of biopsy specimens with the GS of surgical specimens in 185 patients who underwent surgery for prostate cancer. We calculated the sensitivity, specificity, and predictive values for the GS of the biopsy specimens. We used Cohen's kappa to determine the degree of concordance between a GS of <7 and ≥7 for the biopsy specimen and the surgical specimen. Age, a family history of prostate cancer, total prostate-specific antigen (tPSA), digital rectal examination, prostate structure and volume, and the number of biopsy cores (biopsy scheme) were analyzed using multivariable logistic regression.

RESULTS

Histological study of biopsy specimens yielded high sensitivity (98%) but low specificity (49%) for GS ≤6 and low sensitivity (35, 26%) and high specificity (93, 99%) for GS=7 and GS ≥7, respectively. Cohen's kappa for the GS from the biopsy and surgical specimens was 0.43 (95% CI=30-56%). The biopsy scheme was the only predictor of discordance in the GS between the two techniques. Among the other variables included in the model, only tPSA showed a slightly significant association. Taking a scheme with less than 7 cores as a reference, we found no difference with 8 to 9 cores but we did find a difference with 10 to 11 cores and with 12 or more cores, with a prevalence ratio of 0.138 (95% CI=0.030-0.513) and 0.277 (95% CI=0.091-0.806), respectively.

CONCLUSION

The GS of the biopsy depends on the scheme. This factor must be taken into account when choosing a treatment option in patients with low tumor grade in biopsy specimens.

External validation of an artificial neural network and two nomograms for prostate cancer detection

Background. Multivariate models are used to increase prostate cancer (PCa) detection rate and to reduce unnecessary biopsies. An external validation of the artificial neural network (ANN) "ProstataClass" (ANN-Charité) was performed with daily routine data. Materials and Methods. The individual ANN predictions were generated with the use of the ANN application for PSA and free PSA assays, which rely on age, tPSA, %fPSA, prostate volume, and DRE (ANN-Charité). Diagnostic validity of tPSA, %fPSA, and the ANN was evaluated by ROC curve analysis and comparisons of observed versus predicted probabilities. Results. Overall, 101 (35.8%) PCa were detected. The areas under the ROC curve (AUCs) were 0.501 for tPSA, 0.669 for %fPSA, 0.694 for ANN-Charité, 0.713 for nomogram I, and 0.742 for nomogram II, showing a significant advantage for nomogram II (P = 0.009) compared with %fPSA while the other model did not differ from %fPSA (P = 0.15 and P = 0.41). All models overestimated the predicted PCa probability. Conclusions. Beside ROC analysis, calibration is an important tool to determine the true value of using a model in clinical practice. The worth of multivariate models is limited when external validations were performed without knowledge of the circumstances of the model's development.

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.

Immunophenotypic Characterization of Benign and Malignant Prostatic Lesions

BACKGROUND

Biopsy diagnosis is the gold standard for differentiating benign and malignant prostatic enlargements. This study was aimed at supplementing biopsy diagnosis with immunophenotypic characters of prostatic lesions.

METHODS

Twenty five cases each of nodular hyperplasia and adenocarcinoma prostate were compared for their morphologic appearances and immunophenotyping, by studying antibodies to prostate specific antigen (PSA), transglutaminase, chromogranin and high molecular weight keratin, proliferating cell nuclear antigen, cell death (apoptosis) and neovascularisation (CD 34).

RESULTS

Markers of differentiation (PSA and transglutaminase) aided recognition of higher-grade tumours. PSA negativity avoided metaplasia being overcalled as carcinoma. Loss of basal cells around malignant prostatic acini as determined by high molecular weight keratin (HMWK), was useful in foci of atypical small acinar proliferation and in prostatic intraepithelial neoplasia. Assessment of proliferation indices identified subsets of tumours, within conventional morphologic Gleason's grades, with a higher growth fraction. Cell death determination and study of tumour vessels did not offer any improvement on morphology.

CONCLUSION

Immunophenotypic assessment helps in refining morphologic diagnosis of prostatic lesions. Differentiation and proliferation markers objectively assess tumour characteristics with their biologic growth potential and are recommended for diagnostic use. They also help in assessement of response to therapy.

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.

Outcome prediction for prostate cancer detection rate with artificial neural network (ANN) in daily routine

BACKGROUND

We evaluated the use of the artificial neural network (ANN) program "ProstataClass" of the Department of Urology and the Institute of Medical Informatics at the Charité-Universitätsmedizin Berlin in daily routine to increase prostate cancer (CaP) detection rate and to reduce unnecessary biopsies.

MATERIALS AND METHODS

From May 2005 to April 2007, a total of 204 patients were included in the study. The Beckman Access PSA assay was used, and pretreatment prostate specific antigen (PSA) was measured prior to digital rectal examination (DRE) and 12 core systematic transrectal ultrasound (TRUS) guided biopsies. The individual ANN predictions were generated with the use of the ANN application for the Beckman Access PSA and free PSA assays, which relies on age, PSA, percent free prostate specific antigen (%fPSA), prostate volume, and DRE. Diagnostic validity of total prostate specific antigen (tPSA), %fPSA, and the ANN was evaluated by ROC curve analysis.

RESULTS

PSA and %fPSA ranged from 4.01 to 9.91 ng/ml (median: 6.65) and 5% to 48% (median: 15%), respectively. Of all men, 46 (22.5%) demonstrated suspicious DRE findings. Total prostate volume ranged from 7.1 to 119.2 cc (median: 35). Overall, 71 (34.8%) CaP were detected. Of men with suspicious DRE, 28 (60.9%) had CaP on initial biopsy. The ANN was 78% accurate in the original report. The AUC of ROC curve analysis was 0.51 for PSA, 0.66 for %PSA, and 0.72 for the ANN-Output, respectively.

CONCLUSIONS

Our results in this independent cohort show that ANN is a very helpful parameter in daily routine to increase the CaP detection rate and reduce unnecessary biopsies.

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.

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.

A graphical device to represent the outcomes of a logistic regression analysis

BACKGROUND

Ongoing research on the best way to diagnose prostate cancer has yielded and will continue to yield vast amounts of information. Based on, for example, information from prostate cancer screening trials models have been made that enable urologists to predict which man has prostate cancer on the basis of pre-biopsy data. In patients with screen detected prostate cancer, the presence of indolent disease can now be identified with reasonable certainty by a different model. For these men active surveillance may be a better option than aggressive treatment with its possible side effects as impotence, incontinence and bowel damage. Both models mentioned above are logistic regression models. Nomograms enable their use in daily clinical practice.

METHODS

Nomograms require the memorization and addition of intermediate results. We aimed to design a device that has the same function as a nomogram without this draw back.

RESULTS

A new device that resembles a circular slide rule was developed and is currently being tested in prostate cancer diagnosis and in prostate cancer patient counseling.

CONCLUSIONS

The new device has identical functionality to the nomogram without the drawback of the latter. The application of the device is not limited to the field of prostate cancer research.

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 interobserver variability of digital rectal examination in a large randomized trial for the screening of prostate cancer

BACKGROUND

To analyze to what extent the percentage of suspicious digital rectal examination (DRE) findings vary between examiners and to what extent the percentage of prostate cancers (PCs) detected in men with these suspicious findings varies between examiners.

METHODS

In the first screening round of the European Randomized study of Screening for PC (ERSPC) Rotterdam, 7,280 men underwent a PSA-determination and DRE of whom 2,102 underwent prostate biopsy (biopsy indication PSA > or = 4.0 ng/ml and/or suspicious DRE and/or TRUS). Descriptive statistics of DRE-outcome per PSA-range were used to determine the observer variability of six examiners. Because this analysis did not correct properly for other predictors of a suspicious DRE (PSA-level, biopsy indication, TRUS-outcome, prostate volume and age), a logistic regression analysis controlling for these explanatory variables was performed as well.

RESULTS

In 2,102 men biopsied, 443 PCs were detected (PPV = 21%). For all PSA levels the percentage suspicious DRE varied between examiners from 4% to 28% and percentage PC detected in men with a suspicious DRE varied from 18% to 36%. Logistic regression analysis showed that three of six examiners considered DRE significantly more often abnormal than others (ORs 3.48, 2.80, 2.47, P < 0.001). For all examiners the odds to have PC was statistically significantly higher in case of a suspicious DRE (ORs 2.21-5.96, P < 0.05). This increased chance to find PC was not significantly observer-dependent.

CONCLUSIONS

Three of six examiners considered DRE significantly more often suspicious than the others. However, under equal circumstances a suspicious DRE executed by each examiner increased the chance of the presence of PC similarly.

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.