Radiomics of Periprostatic Fat and Tumor Lesion Based on MRI Predicts the Pathological Upgrading of Prostate Cancer from Biopsy to Radical Prostatectomy.

RATIONALE AND OBJECTIVES

To assess the predictive value of MRI-based radiomics of periprostatic fat (PPF) and tumor lesions for predicting Gleason score (GS) upgrading from biopsy to radical prostatectomy (RP) in prostate cancer (PCa).

METHODS

A total of 314 patients with pathologically confirmed prostate cancer (PCa) after radical prostatectomy (RP) were included in the study. The patients were randomly assigned to the training cohort (n = 157) and the validating cohort (n = 157) in a 1:1 ratio. All had pre-surgery MRI followed by transrectal ultrasound-guided prostate biopsy. Radiological features were extracted from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences for PPF and tumors. Univariate and multivariate logistic regression identified independent clinical risk factors, and a combined model was established by integrating radiomic features of PPF and PCa. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration, and decision curve analysis.

RESULTS

The combined model, incorporating radiomic features of PPF, PCa, and clinical data, predicted GS upgrading from biopsy to RP excellently (AUC=0.925, 95%CI0.872-0.979) in the training cohort. The Hosmer-Lemeshow test confirmed model fit (χ = 9.316, P = 0.316). The nomogram was validated in the validating cohort; it showed good accuracy (AUC= 0.937, 95% CI, 0.891-0.983) and was well calibrated (χ = 12.871, P = 0.116). Decision curve analysis indicated good clinical utility of the radiomic nomogram.

CONCLUSION

The combined model incorporating PPF, PCa, and clinical data showed excellent performance in predicting GS upgrading from biopsy to RP in PCa patients. This offers a novel and reliable noninvasive tool for GS upgrading risk stratification.