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

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.

Prognostic significance of surgery and radiotherapy in elderly patients with localized prostate cancer: establishing and time-based external validation a nomogram from SEER-based study

OBJECTIVE

Prostate cancer (PC) is a significant disease affecting men's health worldwide. More than 60% of patients over 65 years old and more than 80% are diagnosed with localized PC. The current choice of treatment modalities for localized PC and whether overtreatment is controversial. Therefore, we wanted to construct a nomogram to predict the risk factors associated with cancer-specific survival (CSS) and overall survival (OS) in elderly patients with localized PC while assessing the survival differences in surgery and radiotherapy for elderly patients with localized PC.

METHODS

Data of patients with localized PC over 65 years were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for CSS and OS. Nomograms predicting CSS and OS were built using multivariate Cox regression models. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the prediction model. Decision curve analysis (DCA) was used to test the potential clinical value of this model.

RESULTS

A total of 90,434 patients over 65 years and diagnosed with localized PC from 2010 to 2018 were included in the study. All patients were randomly assigned to the training set (n = 63,328) and the validation set (n = 27,106). Univariate and multivariate Cox regression model analysis showed that age, race, marriage, T stage, surgical, radiotherapy, prostate-specific antigen (PSA), and Gleason score (GS) were independent risk factors for predicting CSS in elderly patients with localized PC. Age, race, marriage, surgery, radiotherapy, PSA, and GS were independent risk factors for predicting OS in elderly patients with localized PC. The c-index of the training and validation sets for the predicted CSS is 0.802(95%CI:0.788-0.816) and 0.798(95%CI:0.776-0.820, respectively). The c-index of the training and validation sets for predicting OS is 0.712(95%:0.704-0.720) and 0.724(95%:0.714-0.734). It shows that the nomograms have excellent discriminatory ability. The AUC and the calibration curves also show good accuracy and discriminability.

CONCLUSION

We have developed new nomograms to predict CSS and OS in elderly patients with localized PC. After internal validation and external temporal validation with reasonable accuracy, reliability and potential clinical value, the model can be used for clinically assisted decision-making.

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.

Establishment and validation of serum lipid-based nomogram for predicting the risk of prostate cancer

BACKGROUND

This study aimed to explore the value of combined serum lipids with clinical symptoms to diagnose prostate cancer (PCa), and to develop and validate a Nomogram and prediction model to better select patients at risk of PCa for prostate biopsy.

METHODS

Retrospective analysis of 548 patients who underwent prostate biopsies as a result of high serum prostate-specific antigen (PSA) levels or irregular digital rectal examinations (DRE) was conducted. The enrolled patients were randomly assigned to the training groups (n = 384, 70%) and validation groups (n = 164, 30%). To identify independent variables for PCa, serum lipids (TC, TG, HDL, LDL, apoA-1, and apoB) were taken into account in the multivariable logistic regression analyses of the training group, and established predictive models. After that, we evaluated prediction models with clinical markers using decision curves and the area under the curve (AUC). Based on training group data, a Nomogram was developed to predict PCa.

RESULTS

210 (54.70%) of the patients in the training group were diagnosed with PCa. Multivariate regression analysis showed that total PSA, f/tPSA, PSA density (PSAD), TG, LDL, DRE, and TRUS were independent risk predictors of PCa. A prediction model utilizing a Nomogram was constructed with a cut-off value of 0.502. The training and validation groups achieved area under the curve (AUC) values of 0.846 and 0.814 respectively. According to the decision curve analysis (DCA), the prediction model yielded optimal overall net benefits in both the training and validation groups, which is better than the optimal net benefit of PSA alone. After comparing our developed prediction model with two domestic models and PCPT-RC, we found that our prediction model exhibited significantly superior predictive performance. Furthermore, in comparison with clinical indicators, our Nomogram's ability to predict prostate cancer showed good estimation, suggesting its potential as a reliable tool for prognostication.

CONCLUSIONS

The prediction model and Nomogram, which utilize both blood lipid levels and clinical signs, demonstrated improved accuracy in predicting the risk of prostate cancer, and consequently can guide the selection of appropriate diagnostic strategies for each patient in a more personalized manner.

Development and validation of a multi-parameter nomogram for predicting prostate cancer: a retrospective analysis from Handan Central Hospital in China

Background

To explore the possible predicting factors related to prostate cancer and develop a validated nomogram for predicting the probability of patients with prostate cancer.

Method

Clinical data of 697 patients who underwent prostate biopsy in Handan Central Hospital from January 2014 to January 2020 were retrospectively collected. Cases were randomized into two groups: 80% (548 cases) as the development group, and 20% (149 cases) as the validation group. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors for prostate cancer. The nomogram prediction model was generated using the finalized independent risk factors. Decision curve analysis (DCA) and the area under receiver operating characteristics curve (ROC) of both development group and validation group were calculated and compared to validate the accuracy and efficiency of the nomogram prediction model. Clinical utility curve (CUC) helped to decide the desired cut-off value for the prediction model. The established nomogram with Prostate Cancer Prevention Trial Derived Cancer Risk Calculator (PCPT-CRC) and other domestic prediction models using the entire study population were compared.

Results

The independent risk factors determined through univariate and multivariate logistic regression analyses were: age, tPSA, fPSA, PV, DRE, TRUS and BMI. Nomogram prediction model was developed with the cut-off value of 0.31. The AUC of development group and validation group were 0.856 and 0.797 respectively. DCA exhibits consistent observations with the findings. Through validating our prediction model as well as other three domestic prediction models based on the entire study population of 697 cases, our prediction model demonstrated significantly higher predictive value than all the other models.

Conclusion

The nomogram for predicting prostate cancer can facilitate more accurate evaluation of the probability of having prostate cancer, and provide better ground for prostate biopsy.

The influence of age on prostate cancer screening index

PURPOSE

This study aimed to identify parameters with a higher diagnostic value for early screening of prostate cancer (PCa) at different ages.

MATERIALS AND METHODS

A total of 294 patients were included and divided into two groups according to the age of patients (≤66 and >66 years). Receiver operating characteristic (ROC) curves of total prostate-specific antigen (TPSA), free PSA (FPSA), (F/T)PSA, PSA density (PSAD), PSA-AV score, the ratio of patients' age to prostate volume (AVR) and (F/T)/PSAD were constructed. The area under the ROC curve (AUC) was calculated, and differences in the AUC values among the above-mentioned parameters were compared.

RESULTS

There were 121 patients in the ≤66 years age group (benign prostatic hyperplasia BPH, 103 patients; PCa 18 patients) and 173 patients in the >66 years age group (BPH, 100 patients; PCa, 73 patients). In the ≤66 years age group, the AUC value of AVR for PCa diagnosis was the highest; however, there was no statistically significant difference compared with the AUC values of PSAD and (F/T)/PSAD; compared with TPSA, FPSA, (F/T)PSA and PSA-AV, the differences were statistically significant. In the >66 years age group, the AUC values of PSAD and PSA-AV for PCa diagnosis were higher than those of TPSA, FPSA, (F/T)PSA and (F/T)/PSAD, and the difference was statistically significant; however, the difference was not statistically significant when compared with the AUC value of AVR.

CONCLUSION

In different age groups, screening indices for PCa diagnosis should be selected according to the age of patients.

MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer

Objective

To reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).

Methods

MRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).

Results

The positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).

Conclusions

We found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

Development and internal validation of a prediction model of prostate cancer on initial transperineal template-guided prostate biopsy

Background

Due to the invasiveness of prostate biopsy, a prediction model of the individual risk of a positive biopsy result could be helpful to guide clinical decision-making. Most existing models are based on transrectal ultrasonography (TRUS)-guided biopsy. On the other hand, transperineal template-guided prostate biopsy (TTPB) has been reported to be more accurate in evaluating prostate cancer. The objective of this study is to develop a prediction model of the detection of high-grade prostate cancer (HGPC) on initial TTPB.

Result

A total of 1352 out of 3794 (35.6%) patients were diagnosed with prostate cancer, 848 of whom had tumour with Grade Group 2–5. Age, PSA, PV, DRE and f/t PSA are independent predictors of HGPC with p < 0.001. The model showed good discrimination ability (c-index 0.886) and calibration during internal validation and good clinical performance was observed through decision curve analysis. The external validation of CPCC-RC, an existing model, demonstrated that models based on TRUS-guided biopsy may underestimate the risk of HGPC in patients who underwent TTPB.

Conclusion

We established a prediction model which showed good discrimination ability and calibration in predicting the detection of HGPC by initial TTPB. This model can be used to aid clinical decision making for Chinese patients and other Asian populations with similar genomic backgrounds, after external validations are conducted to further confirm its clinical applicability.

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.

A novel screening strategy for clinically significant prostate cancer in elderly men over 75 years of age

A standard modality for prostate cancer detection in men 75 years and older has not been established. A simple screening method for elderly patients is needed to avoid unnecessary biopsies and to effectively diagnose prostate cancer. A retrospective study was conducted on elderly patients who had prostate biopsy at Kanazawa University Hospital (Kanazawa, Japan) between 2000 and 2017. Of the 2251 patients who underwent prostate biopsy, 254 had clinically significant prostate cancer (CSPC) with a Gleason score (GS) of≥7 and 273 had a GS of <7 or no malignancy. In this study, patients aged 75 years or older were classified as elderly patients. GS ≥ 7 was characterized by a prostate-specific antigen (PSA) of the maximum area under the curve of 12 ng ml^-1 with a sensitivity of 76.2% and a specificity of 73.2%. For PSA levels between 4 ng ml^-1 and 12 ng ml^-1, based on the maximum area under the curve, patients with three or four of the following factors may present a GS of ≥ 7: percent free PSA >24, PSA density≥ 0.24 ng ml^-2, positive findings on digital rectal examination, and transrectal with 90.0% sensitivity and 67.4% specificity. In this study, we found that raising the PSA cutoff to 12 ng ml^-1 for CSPC in elderly individuals can significantly reduce unnecessary prostate biopsies. Furthermore, CSPC could be efficiently discovered by combining the four supplementary markers in patients with a PSA level of 4-12 ng ml^-1. By performing this screening for elderly men over 75 years of age, unnecessary biopsies may be reduced and CSPC may be detected efficiently.

Multivariable Models Incorporating Multiparametric Magnetic Resonance Imaging Efficiently Predict Results of Prostate Biopsy and Reduce Unnecessary Biopsy

Purpose

We sought to develop diagnostic models incorporating mpMRI examination to identify PCa (Gleason score≥3+3) and CSPCa (Gleason score≥3+4) to reduce overdiagnosis and overtreatment.

Methods

We retrospectively identified 784 patients according to inclusion criteria between 2016 and 2020. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Age, PSA derivatives, prostate volume, and mpMRI parameters were assessed as predictors for PCa and CSPCa. The multivariable models based on clinical parameters were evaluated using area under the curve (AUC), calibration plots, and decision curve analysis (DCA).

Results

Univariate analysis showed that age, tPSA, PSAD, prostate volume, MRI-PCa, MRI-seminal vesicle invasion, and MRI-lymph node invasion were significant predictors for both PCa and CSPCa (each p≤0.001). PSAD has the highest diagnostic accuracy in predicting PCa (AUC=0.79) and CSPCa (AUC=0.79). The multivariable models for PCa (AUC=0.92, 95% CI: 0.88–0.96) and CSPCa (AUC=0.95, 95% CI: 0.92–0.97) were significantly higher than the combination of derivatives for PSA (p=0.041 and 0.009 for PCa and CSPCa, respectively) or mpMRI (each p<0.001) in diagnostic accuracy. And the multivariable models for PCa and CSPCa illustrated better calibration and substantial improvement in DCA at threshold above 10%, compared with PSA or mpMRI derivatives. The PCa model with a 30% cutoff or CSPCa model with a 20% cutoff could spare the number of biopsies by 53%, and avoid the number of benign biopsies over 80%, while keeping a 95% sensitivity for detecting CSPCa.

Conclusion

Our multivariable models could reduce unnecessary biopsy without comprising the ability to diagnose CSPCa. Further prospective validation is required.

Establishing a novel prediction model for improving the positive rate of prostate biopsy

Background

At present, prostate-specific antigen (PSA) is the primary evaluation index for judging the necessity of prostate cancer (PCa) biopsy. However, there is a high false-positive rate and a low predictive value due to many interference factors. In this study, we tried to find a novel prediction model that could improve the positive rate of prostate biopsy and reduce unnecessary biopsy.

Methods

We retrospectively studied 237 patients, including their age, body mass index (BMI), PSA, prostate volume (PV), prostate imaging-reporting and data system (PI-RADS) v2 score, neutrophil-lymphocyte ratio (NLR), biopsy Gleason score (BGS), and other information. The univariate and multivariate logistic analyses were used to screen out indicators related to PCa. After establishing a prediction formula model, we used receiver operating characteristic (ROC) curves to assess its prediction performance.

Results

Our study found that age, PSA, PI-RADS v2 score, and diabetes significantly correlated with PCa. Based on multivariate logistic regression analysis results, we created the following prediction formula: Y = 2.599 × PI-RADS v2 score + 1.766 × diabetes + 0.052 × age + 1.005 × PSAD - 9.119. ROC curves showed the formula's threshold was 0.3543. The composite formula had an excellent capacity to detect PCa with the area under the curve (AUC) of 0.91. In addition, the composite formula also achieved significantly better sensitivity, specificity, and diagnostic accuracy than PSA, PSA density (PSAD), and PI-RADS v2 score alone.

Conclusions

Our predictive formula predicted performance better than PSA, PSAD, and PI-RADS v2 score. It can thus contribute to the diagnosis of PCa and be used as an indicator for prostate biopsy, thereby reducing unnecessary biopsy.

The prediction value of PI-RADS v2 score in high-grade Prostate Cancer: a multicenter retrospective study

Background: To explore the prediction value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with related variables of prostate cancer. Material and Methods: A total of 316 patients with newly discovered prostate cancer at Zhongnan Hospital of Wuhan University and Renmin Hospital of Wuhan University from December 2017 to August 2019 were enrolled in this study. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade prostate cancer. ROC curves were generated for the risk factors to distinguish the cut-off points. Logistic regression analyses were used to investigate the independent risk factors of high-grade prostate cancer. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve, ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. Results: Of 316 patients, a total of 187 patients were diagnosed as high-grade prostate cancer. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 score were significantly different between the high- and low-grade prostate cancer patients. Univariate and multivariate logistic regression analyses showed only tPSA, prostate volume and PI-RADS v2 score were the independent risk factors of high-grade prostate cancer. The nomogram could predict the probability of high-grade prostate cancer, with a sensitivity of 79.4% and a specificity of 77.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.840 (0.797-0.884). Leave-one-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. External data validation was performed with a sensitivity of 80.6% and a specificity of 77.3%, the Kappa value was 0.5755. Conclusions: PI-RADS v2 score had the value in predicting high-grade prostate cancer and the nomogram prediction model may help early diagnose the high risk prostate cancer.

Establishment and Validation of Extra-transitional Zone Prostate Specific Antigen Density (ETzD), a Novel Structure-based Parameter for Quantifying the Oncological Hazard of Prostates with Enlarged Stroma

Extra-transitional zone density (ETzD), a novel parameter is proposed to stratify the deviation of prostate specific antigen (PSA) due to structural change according to stromal hyperplasia of prostate. ETzD was conducted on a concept to estimate the PSA density (PSAD) after hypothetical enucleation of the transitional zone of an enlarged prostate by a non-linear regression prediction model with intrinsic linearity, from the retrospective analysis of PSA change observed actual enucleation by laser. The performance to predict the presence and severity of malignancy was validated by two cohorts of 3,440 prostate biopsies and 2,783 radical prostatectomy specimens. The performance of ETzD was compared with conventional parameters. The receiver operative curve of area under curve (AUC) of ETzD to predict the presence of malignacy was 0.862 (95% CI; 0.843~0.881), better than PSA, PSAD or transitional zone PSAD (TzPSAD). The AUC of ETzD to predict an unfavorable cancer among prostate cancer patients was 0.736 (95% CI; 0.705~0.768), which performs better than PSA and comparable to PSAD or TzPSAD. In summary, the performance of ETzD as a universal parameter to quantify the oncological hazard of a prostate was validated and the superiority to conventional parameters was verified.

A Nomogram Based on a TRUS Five-Grade Scoring System for the Prediction of Prostate Cancer and High Grade Prostate Cancer at Initial TRUS-Guided Biopsy

Purpose: To evaluate the efficacy of transrectal ultrasound five-grade scoring system (TRUS-5) in predicting prostate cancer (PCa) and high grade PCa (HGPCa), compared with TRUS two-grade scoring system (TRUS-2), and establish a TRUS-5 based nomogram for the prediction of PCa and HGPCa at initial biopsy (IPBx).

Methods: Data were collected from 862 men who underwent initial TRUS-guided 12-core prostate biopsy. Age, prostate-specific antigen (PSA), percent free PSA, digital rectal examination (DRE), prostate volume (PV), PSA density (PSAD) and TRUS findings were included in the analysis. For TRUS-5, the probability of PCa was quantified on a scale from 1 (benign) to 5 (malignant). TRUS-2 used the grades “normal” and “suspicious”. After univariate and multivariate logistic regression analyses, nomogram models were developed and internally validated based on independent predictors to predict the probability of PCa and HGPCa.

Results: Overall PCa was detected in 42% (362/862) with 26.22% (226/862) showing HGPCa. TRUS-5 significantly outperformed TRUS-2 for the risk prediction of PCa and HGPCa (area under the receiver operating characteristic curve [AUC]: 0.787 vs. 0.694 for PCa, 0.841 vs. 0.713 for HGPCa, P<0.05). The TRUS-5 based nomogram showed higher AUCs (0.905 for PCa, 0.903 for HGPCa) than PSA alone, clinical base model, the TRUS-2 based model, and other predictive models (P<0.05).

Conclusions: TRUS-5 represents a better imaging predictor than TRUS-2 for PCa and HGPCa. Our TRUS-5 based nomogram models performed well for the prediction of PCa and HGPCa at IPBx, which may help to make the decision to biopsy.

A Novel Scoring System for Prediction of Prostate Cancer Based on Shear Wave Elastography and Clinical Parameters

OBJECTIVE

To develop a novel scoring system for the prediction of prostate cancer (PCa).

METHODS

We assessed 127 patients who underwent a prostate biopsy. Prior to biopsy, we performed shear wave elastography (SWE), transrectal ultrasound, digital rectal exam, total prostatic specific antigen, PSA density (PSAD), and free PSA/total PSA ratio (F/T). We developed an 11-point scoring system based on SWE and these clinical parameters.

RESULTS

PCa was diagnosed in 51 (40.2%) of 127 patients and 192 (25.2%) of 762 sextants on initial biopsy. ROC curve analyses showed that the cutoff value (COV) for SWE was 40.8 kpa at the sextant level. The AUC of score system based on the SWE and clinical parameters (0.911) was significantly different from scoring systems based on SWE alone (0.842) or clinical parameters alone (0.868). For this 11-point scoring system, the optimal COV, Youden index, sensitivity, specificity, PPV, NPV, and AUC were 3 points, 0.66, 76.5% 89.5%, 82.98%, 85.00%, and 0.911, respectively. There were 68 negative biopsy results in patients with 0-3 points, and the detection rate of PCa was 100% in patients with scores exceeding 6 points.

CONCLUSION

This 11-point scoring system based on SWE and clinical parameters has the good diagnostic performance for predicting PCa. It may be useful in selecting patients for biopsy, substantially reducing the number of unnecessary biopsies while ensuring that few cancers are missed.

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.

Radical prostatectomy in patients aged 75 years or older: review of the literature

Given the demographic trends toward a considerably longer life expectancy, the percentage of elderly patients with prostate cancer will increase further in the upcoming decades. Therefore, the question arises, should patients ≥75 years old be offered radical prostatectomy and under which circumstances? For treatment decision-making, life expectancy is more important than biological age. As a result, a patient's health and mental status has to be determined and radical treatment should only be offered to those who are fit. As perioperative morbidity and mortality in these patients is increased relative to younger patients, patient selection according to comorbidities is a key issue that needs to be addressed. It is known from the literature that elderly men show notably worse tumor characteristics, leading to worse oncologic outcomes after treatment. Moreover, elderly patients also demonstrate worse postoperative recovery of continence and erectile function. As the absolute rates of both oncological and functional outcomes are still very reasonable in patients ≥75 years, a radical prostatectomy can be offered to highly selected and healthy elderly patients. Nevertheless, patients clearly need to be informed about the worse outcomes and higher perioperative risks compared to younger patients.

Prostate transitional zone volume-based nomogram for predicting prostate cancer and high progression prostate cancer in a real-world population

PURPOSE

To evaluate and compare the efficacy of prostate volume (PV), transitional zone volume (TZV), and prostate volume index (PVI, the ratio of TZV to peripheral zone volume) in the identification of men at risk of prostate cancer (PCa) and high-progression PCa (HPPCa) at the initial biopsy (IBX) in a real-world population.

METHODS

From Jul 2014 to Aug 2016, data on 1144 patients who had undergone the initial prostate biopsies were prospectively collected and analyzed. Univariate and multivariate logistic regression analyses were performed to identify the independent predictors for PCa and HPPCa. Based on independent predictors, nomogram models were developed and internally validated to assess a man's risk of harboring PCa and HPPCa.

RESULTS

The detection rates of PCa and HPPCa were 43.09% (493/1144) and 39.16% (448/1144), respectively. In the multivariate analyses, age, PSA, TZV, DRE, and TRUS instead of PV or PVI were independent predictors for PCa and HPPCa, percent free PSA was independent predictor for PCa not for HPPCa. Such independent predictors were finally included in the nomogram models. The AUCs of TZV-based nomogram models were 87.0% for PCa and 87.7% for HPPCa, which were higher than that of PSA alone or other predictive models.

CONCLUSIONS

TZV is a better predictive biomarker than PV or PVI for PCa and HPPCa, we recommend adding TZV but not PV or PVI to the nomogram models to improve the predictive accuracy of PCa and HPPCa at IBX.

Prostate volume index stratified prostate cancer risk in patients elected to a first random biopsy set

OBJECTIVE

To investigate prostate volume index (PVI), defined as the ratio of volume of the transitional zone on that of the peripheral zone, as a factor stratifying prostate cancer (PCA) risk in patients elected to a first random biopsy set.

METHODS

The study evaluated 596 patients who were elected to a first random biopsy set because of suspected PCA in a period between September 2010 and September 2015. Prostate volume index was dichotomized to PVI ≤1 vs PVI >1. The multivariate logistic regression model investigated clinical factors with dichotomized PVI associating with PCA.

RESULTS

The detection rate of PCA was 49%. The dichotomized PVI >1 stratified PCA risk (odds ratio [OR] 0.455; p<0.0001) beyond age (OR 1.062; p<0.0001), PSA (OR 1.167; p<0.0001), PV (OR 0.957; p<0.0001), and abnormal digital rectal examination (OR 2.094; p<0.0001). The goodness of fit statistics assessed model efficacy.

CONCLUSIONS

A large cohort of patients elected to a first random biopsy set had PCA risk stratified by dichotomized PVI beyond other clinical independent factors. Confirmatory studies are required.

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 and external multicenter validation of Chinese Prostate Cancer Consortium prostate cancer risk calculator for initial prostate biopsy

OBJECTIVE

Substantial differences exist in the relationship of prostate cancer (PCa) detection rate and prostate-specific antigen (PSA) level between Western and Asian populations. Classic Western risk calculators, European Randomized Study for Screening of Prostate Cancer Risk Calculator, and Prostate Cancer Prevention Trial Risk Calculator, were shown to be not applicable in Asian populations. We aimed to develop and validate a risk calculator for predicting the probability of PCa and high-grade PCa (defined as Gleason Score sum 7 or higher) at initial prostate biopsy in Chinese men.

MATERIALS AND METHODS

Urology outpatients who underwent initial prostate biopsy according to the inclusion criteria were included. The multivariate logistic regression-based Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) was constructed with cases from 2 hospitals in Shanghai. Discriminative ability, calibration and decision curve analysis were externally validated in 3 CPCC member hospitals.

RESULTS

Of the 1,835 patients involved, PCa was identified in 338/924 (36.6%) and 294/911 (32.3%) men in the development and validation cohort, respectively. Multivariate logistic regression analyses showed that 5 predictors (age, logPSA, logPV, free PSA ratio, and digital rectal examination) were associated with PCa (Model 1) or high-grade PCa (Model 2), respectively. The area under the curve of Model 1 and Model 2 was 0.801 (95% CI: 0.771-0.831) and 0.826 (95% CI: 0.796-0.857), respectively. Both models illustrated good calibration and substantial improvement in decision curve analyses than any single predictors at all threshold probabilities. Higher predicting accuracy, better calibration, and greater clinical benefit were achieved by CPCC-RC, compared with European Randomized Study for Screening of Prostate Cancer Risk Calculator and Prostate Cancer Prevention Trial Risk Calculator in predicting PCa.

CONCLUSIONS

CPCC-RC performed well in discrimination and calibration and decision curve analysis in external validation compared with Western risk calculators. CPCC-RC may aid in decision-making of prostate biopsy in Chinese or in other Asian populations with similar genetic and environmental backgrounds.

Age-Specific Cutoff Value for the Application of Percent Free Prostate-Specific Antigen (PSA) in Chinese Men with Serum PSA Levels of 4.0-10.0 ng/ml

OBJECTIVE

The influence of age on the performance of percent free prostate-specific antigen (%fPSA) in diagnosing prostate cancer (PCa) in East Asians is controversial. We tested the diagnostic performance of %fPSA in a multi-center biopsy cohort in China and identified the proper age-specific cutoff values to avoid unnecessary biopsies.

METHODS

Consecutive patients with a prostate-specific antigen (PSA) level of 4.0-10.0 ng/ml or 10.1-20.0 ng/ml who underwent transrectal ultrasound-guided or transperineal prostate biopsy were enrolled from 22 Chinese medical centers from Jan 1, 2010 to Dec 31, 2013. The diagnostic accuracy of PSA and %fPSA was determined using the area under the receiver operating characteristic (ROC) curve (AUC). Age-specific cutoff values were calculated using ROC curve analysis.

RESULTS

The median %fPSA was much lower in younger patients compared with older patients with a PSA level of 4.0-10.0 ng/ml or 10.1-20.0 ng/ml. The AUC of %fPSA was higher than PSA only in older patients. In patients aged 50 to 59 years, %fPSA failed to improve the diagnosis compared with PSA in these two PSA ranges. Age-specific cutoff values were 24%, 27% and 32% for patients aged 60-69, 70-79 and ≥80 years, respectively, to reduce unnecessary biopsies in men with PSA levels of 4.0-10.0 ng/ml to detect 90% of all PCa.

CONCLUSIONS

The effectiveness of %fPSA is correlated with age in the Chinese population. Age-specific cutoff values would help avoid unnecessary biopsies in the Chinese population.

Configuration and validation of a novel prostate disease nomogram predicting prostate biopsy outcome: A prospective study correlating clinical indicators among Filipino adult males with elevated PSA level

Objective

To configure and validate a novel prostate disease nomogram providing prostate biopsy outcome probabilities from a prospective study correlating clinical indicators and diagnostic parameters among Filipino adult male with elevated serum total prostate specific antigen (PSA) level.

Methods

All men with an elevated serum total PSA underwent initial prostate biopsy at our institution from January 2011 to August 2014 were included. Clinical indicators, diagnostic parameters, which include PSA level and PSA-derivatives, were collected as predictive factors for biopsy outcome. Multiple logistic-regression analysis involving a backward elimination selection procedure was used to select independent predictors. A nomogram was developed to calculate the probability of the biopsy outcomes. External validation of the nomogram was performed using separate data set from another center for determination of sensitivity and specificity. A receiver-operating characteristic (ROC) curve was used to assess the accuracy in predicting differential biopsy outcome.

Results

Total of 552 patients was included. One hundred and ninety-one (34.6%) patients had benign prostatic hyperplasia, and 165 (29.9%) had chronic prostatitis. The remaining 196 (35.5%) patients had prostate adenocarcinoma. The significant independent variables used to predict biopsy outcome were age, family history of prostate cancer, prior antibiotic intake, PSA level, PSA-density, PSA-velocity, echogenic findings on ultrasound, and DRE status. The areas under the receiver-operating characteristic curve for prostate cancer using PSA alone and the nomogram were 0.688 and 0.804, respectively.

Conclusion

The nomogram configured based on routinely available clinical parameters, provides high predictive accuracy with good performance characteristics in predicting the prostate biopsy outcome such as presence of prostate cancer, high Gleason prostate cancer, benign prostatic hyperplasia, and chronic prostatitis.

Prostate cancer nomograms: a review of their use in cancer detection and treatment

As prostate cancer treatment discussions have grown more complex, increasing numbers of nomograms to guide decision-making have been found in the literature. Such nomograms can influence every step in the prostate cancer therapeutic process, from determining the need for biopsy to the need for adjuvant therapy. With a properly counseled patient who is aware of the limitations of nomograms, such tools assist in the shared decision-making that characterizes modern informed consent.

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.

A prostate biopsy strategy based on a new clinical nomogram reduces the number of biopsy cores required in high-risk patients

Background

The nomograms used for prostate cancer risk assessment in Western countries are not directly applicable to Chinese males; consequently, we have developed a new model to evaluate the risk of them developing this disease.

Methods

A total of 1104 patients who had undergone trans-rectal ultrasound (TRUS)-guided 12 + 1-core prostate biopsy were retrospectively evaluated in the first stage of the study. Age, prostate-specific antigen (PSA), the free/total PSA ratio (f/t), digital rectal examination (DRE) findings, the presence of a hypoechoic mass revealed using ultrasound, ultrasonic detection of microcalcifications, prostate volume (PV) and PSA density were considered as predictive factors. Multiple logistic regression analysis involving a backward elimination selection procedure was used to select independent predictors. We compared positive rates regarding 6-core and 12-core biopsy schemes at different risk levels. In the second stage of the study, 238 cases were evaluated using our nomogram. In higher risk patients, we employed a 6 + 1 core biopsy. Positive rates in the first and second stages of the study were compared.

Results

Age, the baseline median natural logarithm of PSA (Ln[PSA]), Ln(PV), f/t, rate of abnormal DRE findings and rate of hypoechoic masses detected using TRUS were the factors that were finally submitted into our nomogram. A significantly greater area under the receiver-operating characteristic curve was obtained for the nomogram than for PSA level alone (0.853 vs. 0.761). A cancer probability cutoff value of 0.5 suggested no significant difference between the 6-core and 12-core biopsy schemes at higher risk levels. In the second stage of the study we verified that in patients with a cancer probability cutoff value >0.5, a 6 + 1-core biopsy could be used without a reduction in the positive detection rate, and significantly reducing the number of biopsy cores required.

Conclusions

A nomogram based on data from Chinese males was developed to predict the positive detection rate, ratio of positive cores and Gleason score at each risk level. According to this nomogram, a reasonable biopsy strategy could be constituted to reduce the number of biopsy cores required in subjects at high risk.

Reference Ranges of Age-Related Prostate-Specific Antigen in Men without Cancer from Beijing Area

BACKGROUND

To determine the normal ranges of serum age-related prostate-specific antigen (PSA) level in men from Beijing area without cancer.

METHODS

In this cross sectional study, form April 2010 to October 2011, 1611 healthy men undergoing a routine health check-up in our hospital and all men received three examinations including serum PSA test, digital rectal ex-amination and transrectal ultrasound. Men with any two abnormal results of the three examinations were undergone a prostate biopsy. Men with any two normal results of the three examinations or with negative biopsy were defined as men without cancer. Men with a prior history of prostate cancer/surgery or with urinary tract infection/obstruction were excluded. 1572 men without cancer were recruited into the study finally and were stratified into 10-year age groups: 40 to 49, 50 to 59, 60 to 69, 70 to 79, and older than 80.

RESULTS

The median PSA value (95th percentile range) was 0.506(1.565), 1.04(2.920), 1.16(4.113), 1.34(5.561)and 2.975 (7.285) for each age group respectively, and the 25th percentile to 75 percentile was 0.343 to 0.923, 0.663 to 1.580, 0.693 to 2.203, 0.789 to 2.368 and 1.188 to 4.295 respectively. The serum PSA value is directly correlated with age (r=0.314, P＜0.001).

CONCLUSIONS

Use the age-related range for PSA increases the sensitivity in younger men and decreases the biopsy rate in older patients.

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.

A retrospective study of prostate cancer cases mimicking urothelial cell carcinoma of the bladder

BACKGROUND

Prostate cancer (PCa) originating from the prostate base may intrude into the urinary bladder and may be misdiagnosed as bladder cancer. In this retrospective study, we reviewed the clinic data on PCa cases which were initially misdiagnosed as bladder cancer in order to identify diagnostic methods that would allow a better differential diagnosis for PCa.

METHODS

Out of a total of 455 patients treated for PCa at our hospital between April 2003 and June 2011, 14 patients (3.1%) had been initially misdiagnosed as urinary bladder urothelial cell carcinoma. The clinical data on these 14 cases was retrieved and analyzed.

RESULTS

Of the 14 patients, 11 patients were eventually diagnosed with PCa after MRI examination, and seven out of these had PCa with bladder neck invasion. Prostate needle biopsy or transurethral resection of prostate (TURP) revealed that all 14 patients had adenocarcinoma of prostate with Gleason scores ranging from 7 to 9. Nine patients received TURP for hematuria or lower urinary tract blockage. The mean follow-up was 37 months, during which six patients survived.

CONCLUSIONS

As clinical presentation and in emergency settings, prostate cancer originating from the prostate base can be confused with bladder cancer originating from the neck or the triangle region of the urinary bladder. Serum prostate specific antigen (PSA) levels and digital rectal examination, in combination with transrectal ultrasound (TRUS), MRI, and prostate needle biopsy are valuable tools for definitive differential diagnosis of the basal prostate cancer.