Stratified Analysis of Patients Within the PSA Gray Zone (4–10 ng/mL) and Its Clinical Application Value: Development of a Predictive Model for Clinically Significant Prostate Cancer Using Quantitative Indicators of PSA, ADC, and Relative T2 Value

Vollständiger Abstract

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Objective: Patients with prostate-specific antigen (PSA) levels in the 4–10 ng/mL gray zone present a diagnostic challenge, as PSA alone cannot reliably distinguish clinically significant prostate cancer (csPCa) from benign conditions or indolent disease, potentially leading to unnecessary biopsies or missed clinically significant disease. This study aimed to develop a risk prediction model for csPCa in this population using clinical indicators and magnetic resonance imaging (MRI) quantitative data, and to evaluate its potential value for risk stratification and as a supplementary tool for biopsy decision-making. Methods: We retrospectively included 210 patients with PSA levels in the 4–10 ng/mL range and confirmed pathological diagnoses, who were admitted to our hospital between January 2018 and June 2025. csPCa was defined as a Gleason score ≥ 7. Patients were stratified by pathological diagnosis (csPCa vs. non-csPCa) and randomly divided into training and internal validation sets in a 7:3 ratio. The following variables were collected for analysis: age, PSA, apparent diffusion coefficient (ADC), and relative T2 value. Univariate and multivariate logistic regression were used to identify independent predictors of csPCa. We constructed a prediction model and nomogram. The discriminating ability, calibration, and stability of the model were assessed using receiver operating characteristic (ROC) curves, calibration curves, and Bootstrap internal validation. Decision curve analysis (DCA) was performed to evaluate the model’s net benefit across clinically relevant threshold probabilities. Risk stratification was performed based on predicted probabilities. Results: Of the 210 patients, 50 had csPCa and 160 had non-csPCa lesions. Univariate regression showed associations between age, ADC value, and relative T2 value. Multivariate logistic regression identified age and relative T2 value as independent predictors in the final model. Internal validation using 1000 Bootstrap resampling with optimism correction yielded a corrected AUC of 0.698 (95% CI: 0.669–0.716), with a mean optimism of only 0.016, indicating minimal overfitting. The area under the curve (AUC) of the model was 0.720 in the training set and 0.711 in the validation set. The calibration curve for the training set demonstrated good agreement between predicted and observed probabilities. DCA showed net benefits across certain threshold probabilities. Risk stratification based on predicted probability showed csPCa detection rates of 14.7%, 28.3%, and 47.4% in low-, intermediate- and high-risk groups, respectively. Conclusions: The logistic binary classification model based on age and relative T2 value may assist in risk assessment for csPCa in PSA gray zone patients. However, its moderate discrimination suggests it should be used as a supplementary tool rather than a standalone diagnostic test. Risk stratification based on predicted probability may help identify different csPCa risk populations, but given the exploratory nature of this single-center retrospective study, multi-center, large-sample, and prospective studies are required for external validation and optimization before clinical implementation.