Diagnostic Value Analysis of PI-RADS v2.1 Combined with ADC Values in the Risk Stratification of Prostate Cancer Gleason Scores: A Retrospective Study.

BACKGROUND

Prostate cancer is a remarkable global health concern, necessitating accurate risk stratification for optimal treatment and outcome prediction. By highlighting the potential of imaging-based approaches to improve risk assessment in prostate cancer, this research aims to evaluate the diagnostic efficacy of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 combined with apparent diffusion coefficient (ADC) values to gain increased context within the broad landscape of clinical needs and advancements in prostate cancer management.

METHODS

The clinical data of 145 patients diagnosed with prostate cancer were retrospectively analysed. The patients were divided into low-moderate- and high-risk groups on the basis of Gleason scores. PI-RADS v2.1 scores were assessed by senior radiologists and ADC values were calculated by using diffusion-weighted imaging. Statistical, univariate logistic regression, and receiver operating characteristic curve analyses were employed to evaluate the diagnostic efficacy of each index and combined PI-RADS v2.1 scores and ADC values.

RESULTS

This study found significant differences in PI-RADS v2.1 scores and ADC values between the low-moderate- and high-risk groups ( < 0.001). Logistic regression analysis revealed associations of various clinical indicators, PI-RADS score and ADC values with Gleason risk classification. Amongst indices, mean ADC demonstrated the highest sensitivity (0.912) and area under curve (AUC) value (0.962) and the combination of PI-RADS v2.1 with mean ADC showed high predictive value for the Gleason risk grading of prostate cancer with a high AUC value (0.966).

CONCLUSIONS

This study provides valuable evidence for the potential utility of imaging-based approaches, specifically PI-RADS v2.1 combined with ADC values, in enhancing the accuracy of risk stratification in prostate cancer.