Development and validation of a nomogram combining PI-RADS v2.1 and clinical indicators for the diagnosis of prostate cancer in patients with PSA ≤ 20 ng/mL.

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Abstract

This investigation focused on developing a predictive clinical tool that combines biparametric MRI-derived PI-RADS v2.1 assessments with patient-specific biomarkers. The model was designed to optimize prostate cancer detection reliability in individuals exhibiting prostate-specific antigen concentrations below 20 ng/mL, particularly targeting the diagnostic challenges presented by this intermediate PSA range. By systematically integrating imaging characteristics with laboratory parameters, the research sought to establish a practical decision-making framework for clinicians managing suspected prostate malignancies. A total of 218 patients with confirmed pathological diagnoses between January 2020 and December 2023 underwent a retrospective review. The cohort was divided into two distinct groups: a training cohort comprising 153 cases and a validation cohort containing 65 cases. For nomogram predictor selection, statistical modeling incorporated machine learning approaches including LASSO regression with ten-fold cross-validation, supplemented by both univariate and multivariate logistic regression analyses to identify independent prognostic factors.The nomogram's predictive performance was evaluated by determining the area under the receiver operating characteristic curve (AUC), developing calibration plots, and implementing decision curve analysis (DCA). The study findings revealed that among patients with prostate-specific antigen (PSA) concentrations ≤ 20 ng/mL, four parameters - PI-RADS v2.1 classification, free PSA ratio (%fPSA), diffusion-weighted imaging-derived ADC values, and serum hemoglobin concentrations - emerged as independent predictive factors for prostate carcinoma detection. The composite predictive model demonstrated superior diagnostic performance compared to individual parameters, achieving an elevated receiver operating characteristic curve area of 0.922. Notably, the PI-RADS v2.1 scoring system alone showed an AUC of 0.848 (P < 0.05) in this patient cohort. The area under the curve (AUC) for free PSA percentage reached 0.760 (P < 0.001), while apparent diffusion coefficient (ADC) values showed superior discriminative ability with an AUC of 0.825 (P < 0.001). Hemoglobin levels exhibited moderate predictive value (AUC = 0.622, P = 0.006). The developed predictive model exhibited outstanding diagnostic accuracy, achieving AUC scores of 0.922 in the training dataset and 0.898 in the validation cohort, complemented by precise calibration metrics. Integrating PI-RADS v2.1 scores with clinical parameters enhanced diagnostic performance, yielding 81.2% sensitivity and 89.3% specificity in lesion characterization.This marked improvement becomes evident when compared to the standalone application of PI-RADS v2.1, which yielded sensitivity and specificity values of 73.2% and 86.8% correspondingly. The PI-RADS v2.1 assessment derived from biparametric MRI demonstrates standalone prognostic value for detecting prostate malignancies in patients with serum PSA concentrations below 20 ng/mL. This imaging-based scoring system, when integrated with additional clinical parameters, significantly enhances the diagnostic reliability of clinical assessments. The methodology provides clinicians with a non-invasive evaluation tool featuring intuitive visualization capabilities, potentially reducing the necessity for invasive biopsy procedures while maintaining diagnostic precision. This integrated methodology demonstrates considerable promise as an effective framework for improving diagnostic accuracy in PCa identification and supporting therapeutic choices in clinical practice.

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