A nomogram based on radiomic features from peri-prostatic adipose tissue for predicting bone metastasis in first-time diagnosed prostate cancer patients.

Purpose: To evaluate a radiomics-based nomogram using peri-prostatic adipose tissue (PPAT) features for predicting bone metastasis (BM) in newly diagnosed prostate cancer (PCa) patients.

Methods: A retrospective study of 151 PCa patients (October 2010-November 2022) was conducted. Radiomic features were extracted from axial T2-weighted MRI of PPAT, and normalized PPAT was calculated as the ratio of PPAT volume to prostate volume. A radiomics score (Radscore) was developed using logistic regression with 16 features selected via LASSO regression. Independent predictors identified through univariate and multivariate logistic regression were used to construct a nomogram. Predictive performance was assessed using ROC curves, and internal validation involved 1000 bootstrapped iterations.

Results: The Radscore, based on 16 features, showed significant association with BM and outperformed normalized PPAT in predictive value. Independent predictors of BM included Radscore, alkaline phosphatase (ALP), and clinical N stage (cN). A nomogram integrating these factors demonstrated strong discrimination (C-index: 0.908; 95% CI: 0.851-0.966) and calibration, with consistent results in validation (C-index: 0.903; 95% CI: 0.897-0.916). Decision curve analysis confirmed its clinical utility.

Conclusions: Radscore, cN, and ALP were identified as independent BM predictors. The developed nomogram enables accurate risk stratification and personalized BM predictions for newly diagnosed PCa patients.