Identification of high-risk tumor characteristics in patients with localized prostate cancer using conventional combined with diffusion-weighted MRI imaging parameters.

Abstract

The objective of this study was to investigate the utility of conventional imaging combined with diffusion-weighted magnetic resonance imaging (MRI) in identifying high-risk tumor characteristics in patients with localized prostate cancer. A retrospective cohort study was conducted on 194 patients who underwent surgery for localized prostate cancer. Patients were categorized into low-risk and high-risk groups based on clinical criteria. Imaging data were obtained using a MRI system, and various imaging parameters were analyzed, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) signal intensities, diffusion-weighted MRI parameters, and their correlations with clinical characteristics. Statistical methods such as logistic regression, and receiver operating characteristic (ROC) analysis were employed to assess the diagnostic performance of the imaging parameters and to construct joint prediction models. A verification set prediction model was established and compared. The comparison of demographic and clinical characteristics between the low and high-risk groups revealed significant differences in the prostate-specific antigen (PSA) level, Gleason score, Tumor Size and prostate volume (PV). Standard imaging parameters, T1WI and T2WI signal intensities, exhibited significant differences between the low and high-risk groups. Additionally, diffusion-weighted MRI parameters, including signal intensities at different b values, apparent diffusion coefficient (ADC), K^trans, and K_ep, were notably associated with high-risk tumor characteristics in localized prostate cancer. Logistic regression analysis identified both standard imaging and diffusion-weighted MRI parameters as independent predictors of high-risk tumor characteristics. Furthermore, the ROC analysis demonstrated the diagnostic potential of T2WI signal intensity, signal intensity at 800 s/mm², and ADC in identifying high-risk tumors. Joint prediction models combining standard imaging and diffusion-weighted MRI parameters showed high predictive accuracy for high-risk tumor characteristics in localized prostate cancer, with Area Under the Curve (AUC) values of 0.777 for standard imaging, 0.826 for diffusion-weighted MRI, and 0.892 for the combined model. The AUC value for the prediction model in validation set was 0.860. In conclusion, this study underscores the diagnostic potential of conventional imaging combined with diffusion-weighted MRI in identifying high-risk tumor characteristics in patients with localized prostate cancer. Both standard imaging and diffusion-weighted MRI parameters were identified as non-invasive biomarkers for risk assessment and prognosis. These findings have implications for precision treatment of localized prostate cancer, highlighting the potential integration of imaging-based risk assessment tools into clinical practice for tailored treatment strategies and improved patient outcomes.