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

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

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

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.

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.

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.

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.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

Developing a model for forecasting Gleason score ≥7 in potential prostate cancer patients to reduce unnecessary prostate biopsies

PURPOSE

The diagnosis of Gleason score (GS) ≥7 with distinction from GS < 7 remains a difficult problem instructing clinical decisions. Moreover, the present wide application of prostate biopsy to increase prostate cancer detection rate might cause unnecessary and excessive examination or treatment. Therefore, a risk assessment model for forecasting GS ≥ 7 in potential prostate cancer patients was established to reduce unnecessary prostate biopsies.

METHODS

Patients (n = 981; September 2009 to January 2013) who underwent trans-rectal ultrasound (TRUS)-guided core prostate biopsy were retrospectively evaluated in the first stage of the study. Age, prostate-specific antigen (PSA), free PSA (fPSA), the free/total PSA ratio (f/t), prostate volume (PV), PSA density (PSAD), digital rectal examination (DRE) findings (texture, nodules) and B-ultrasound detection results (normal or abnormal, presence of hypoechoic mass or microcalcification) were considered as potential predictive factors. After multiple logistic regression analysis, independent variables used to build a nomogram were selected using a backward elimination selection procedure. Then, a model to forecast GS ≥ 7 was designed for potential prostate cancer patients. In the second stage of the study, 410 cases (January 2013 to March 2015) were subsequently evaluated using our model for prostate biopsies, and the outcomes of biopsies were compared between the two stages.

RESULTS

PSA, DRE texture, DRE nodules and B-ultrasound results were finally brought into our nomogram; a obviously greater area under the receiver operating characteristic (ROC) curve was obtained for the model than utilizing PSA, fPSA or PSAD alone (0.831 vs. 0.803, 0.770, 0.780 separately). We thereafter sought the best cutoff value in the ROC curve at 0.87, which provided sensitivity as high as 90%. Meanwhile, the specificity was 45.8%, which was much higher than the specificity of PSA, fPSA and PSAD at the same sensitivity level (37.7, 24.6 and 35.2%, respectively). In the first stage, the detection rate of GS ≥ 7 in the high-risk group was significantly higher than in the low-risk group (80.3 vs. 35.0%, p < 0.001). Furthermore, in the second stage, with the application of the new model associated with our former models, the rate of GS ≥ 7 was improved from 71.0 (697/981) to 79.2% (267/337) (p = 0.003).

CONCLUSIONS

The model for forecasting GS ≥ 7 is effective, which could reduce unnecessary prostate biopsies without delaying patients' diagnoses and treatments.