Multiparametric MRI-based radiomics model integrating tumor lesion and periprostatic adipose tissue for predicting bone metastasis in prostate cancer
In brief
MRI radiomics model predicts bone metastasis in prostate cancer with 96% accuracy
In a study of 237 newly diagnosed prostate cancer patients, a combined model that merged tumor and periprostatic fat MRI features with clinical factors (T stage and Ki-67) achieved an area under the curve of 0.96 in the training set and 0.87 in validation. The tool non-invasively identifies patients at high risk for synchronous bone metastasis, offering a potential supplement to standard staging, though prospective testing is still needed.
- Journal
- Frontiers in oncology (Q2)
- Published
- 7 September 2026
- Study design
- Randomized controlled trial
- Evidence level
- Level 1, High (CEBM 1b)
- Authors
- Qian Zheng, Ruihong Chen, Yuying Xie, Sha Cui, Wenjin Bian, Jianting Li, et al.
- PMID
- 42769124
- DOI
- 10.3389/fonc.2026.1918323
Why clinicians should know about it
- Picked for Histology (top studies of the week, 27 September 2026): Ranked by evidence level and journal quartile
Abstract
PURPOSE: To develop and validate a multiparametric MRI (mpMRI)-based radiomics model incorporating features from both the tumor lesion and periprostatic adipose tissue (PPAT) for predicting synchronous bone metastasis (BM) in patients with newly diagnosed prostate cancer (PCa). METHODS: This retrospective study enrolled 237 patients with histologically confirmed PCa who underwent prostate mpMRI between January 2021 and December 2024. Patients were randomly allocated to a training cohort (n = 165) and a validation cohort (n = 72) in a 7:3 ratio. Univariate and multivariate logistic regression analyses identified independent clinical predictors of BM. Radiomics features were extracted from tumor lesions and PPAT on T2-weighted imaging (T2WI), fat-suppressed T2-weighted imaging (T2WI-FS), and apparent diffusion coefficient (ADC) maps. Separate radiomics models were constructed for intratumoral, PPAT, and combined (intratumoral + PPAT) features. A combined clinical-radiomics model was established by integrating the radiomics score (Rad-score) with independent clinical predictors. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: Multivariate analysis identified clinical T stage (OR = 5.00, 95% CI: 1.65-16.70; p = 0.006) and Ki-67 expression (OR = 4.61, 95% CI: 1.63-14.10; p = 0.005) as independent predictors of BM. The intratumoral radiomics model achieved AUCs of 0.928 (training) and 0.835 (validation). The PPAT radiomics model achieved AUCs of 0.859 (training) and 0.842 (validation). The combined radiomics model (intratumoral + PPAT) yielded AUCs of 0.955 (95% CI: 0.927-0.982) and 0.850 (95% CI: 0.745-0.954) in the training and validation cohorts, respectively. The combined clinical-radiomics model demonstrated AUCs of 0.960 (95% CI: 0.934-0.986) and 0.873 (95% CI: 0.785-0.960), respectively. DCA indicated favorable clinical net benefit across a wide range of threshold probabilities. CONCLUSION: The combined model integrating PPAT and tumor radiomics features with clinical predictors demonstrated robust discriminative ability for predicting BM in newly diagnosed PCa. This non-invasive model may provide complementary risk stratification beyond conventional clinical assessment by identifying patients with potentially aggressive disease characteristics and supporting individualized clinical decision-making.
Abstract as published, via PubMed.
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