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

Prostate zones and cancer: lost in transition?

Localized prostate cancer shows great clinical, genetic and environmental heterogeneity; however, prostate cancer treatment is currently guided solely by clinical staging, serum PSA levels and histology. Increasingly, the roles of differential genomics, multifocality and spatial distribution in tumorigenesis are being considered to further personalize treatment. The human prostate is divided into three zones based on its histological features: the peripheral zone (PZ), the transition zone (TZ) and the central zone (CZ). Each zone has variable prostate cancer incidence, prognosis and outcomes, with TZ prostate tumours having better clinical outcomes than PZ and CZ tumours. Molecular and cell biological studies can improve understanding of the unique molecular, genomic and zonal cell type features that underlie the differences in tumour progression and aggression between the zones. The unique biology of each zonal tumour type could help to guide individualized treatment and patient risk stratification.

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.

Zonal adjusted PSA density improves prostate cancer detection rates compared with PSA in Taiwanese males with PSA < 20 ng/ml

BACKGROUND

The current study aimed to compare the efficacy of transition zone PSA density (TZPSAD) with traditional PSA and PSA density (PSAD), for the diagnosis of prostate cancer (PCa) in Taiwanese males.

METHODS

Men with PSA between 4.0 and 20.0 ng/ml who underwent a transrectal ultrasound (TRUS) guided prostate biopsy between the studied period were retrospectively identified. The demographic data, PSAD and TZPSAD were calculated in all patients. Receiver operating characteristic (ROC) curves were used to analyze the accuracy of a positive PCa diagnosis.

RESULTS

The area under the ROC (AUC) was 0.615, 0.748 and 0.746 for PSA, PSAD and TZPSAD, respectively. The best cut-off of value for TZPSAD in predicting PCa in men with a PSA of 4.0-10.0 ng/ml was 0.367 ng/ml/ml with a sensitivity of 50% and a specificity of 77.5%. In men with a PSA of 10.1-20.0 ng/ml, the best cut-off value was 0.454 ng/ml, with a sensitivity of 74.8% and specificity of 70.9%.

CONCLUSION

The use of TZPSAD can improve the efficiency and specificity of PSA for the diagnosis of PCa in Taiwanese men with PSA 4.0-20.0 ng/ml. TZPSAD efficiency was similar to PSAD but TZPSAD had better cancer specificity.