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Updated: Sep 19, 2025
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
A nomogram based on radiomic features from peri-prostatic adipose tissue for predicting bone metastasis in first-time
Bohao Liu1, Qian Cai1, Xiao Zhao1
1Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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.
