Abstract
ObjectiveTo 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).MethodsMRI 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).ResultsThe 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).ConclusionsWe 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.
Highlights
Prostate cancer (PCa) is the most common cancer among men in the Western world, and it has an increasing prevalence [1]
Univariate logistic regression analysis showed that all variables were statistically significant predictors of PCa detection except for body mass index (BMI), free Prostate-specific antigen (PSA) (FPSA), free-to-total PSA (FTPSA), transPZA, and alkaline phosphatase (ALP) in the training cohort
Model 1 consists of age, PSA, MRI, transCGA, and coronal peripheral zone sectional area (coroPZA) after excluding sagiPAI, prostate volume (PV), and PSA density (PSAD)
Summary
Prostate cancer (PCa) is the most common cancer among men in the Western world, and it has an increasing prevalence [1]. Prostate-specific antigen (PSA) is the most widely used screening marker to detect PCa at an early stage. The larger clinical trial found that patients having undergone PSA screening had 25% lower PCa death rates than those who did not [2]. After tests reveal an elevated serum PSA level, most patients require puncture biopsy of the prostate, because the prostate biopsy remains the gold standard method for diagnosing PCa. we have to face a clinical problem that the prostate biopsy is an invasive operation. We have to face a clinical problem that the prostate biopsy is an invasive operation It brings pain and fear to the patients, and may cause medical complications such as infection and hemorrhage [3]. It is rational to avoid the biopsy on patients who are proved to be negative cases
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