ArticleLa Radiologia medica2025
Combining MRI radiomics, hypoxia gene signature score and clinical variables for prediction of biochemical recurrence-free survival after radiotherapy in prostate cancer.
Article in La Radiologia medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Artificial Intelligence in the Characterization and Outcome Prediction of Prostate Cancer: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting recurrence of prostate cancer after radical treatment using AI models based on PET/CT radiomics: a dual-center study.Frontiers in oncology · 2026Article
- Integrative machine learning models predict prostate cancer diagnosis and biochemical recurrence risk: Advancing precision oncology.NPJ digital medicine · 2025Article
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Authors and funding
16 authors.
Funding
Abstract
purposeTo investigate the value of combining MRI radiomic and hypoxia-associated gene signature information with clinical data for predicting biochemical recurrence-free survival (BCRFS) after radiotherapy for prostate cancer.
methodsPatients with biopsy-proven prostate cancer, hypoxia-associated gene signature scores and pre-treatment MRI who received radiotherapy between 01/12/2007 and 31/08/2013 at two cancer centres were included in this retrospective cohort analysis. Prostate segmentation was performed on axial T2-weighted sequences using RayStation (v9.1). Histogram standardisation was applied prior to radiomic feature (RF) extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. Four multivariable Cox proportional hazards BCRFS prediction models using clinical information alone and in combination with RFs and/or hypoxia scores were evaluated using concordance index (C-index) [confidence intervals (CI)]. Akaike Information Criterion (AIC) was used to assess model fit.
results178 patients were included. The clinical-only model performance C-index score was 0.69 [0.64-0.7]. The combined clinical-radiomics model (C-index 0.70[0.66-0.73]) and clinical-radiomics-hypoxia model (C-index 0.70[0.65-0.73]) both had higher model performance. The clinical-hypoxia model (C-index 0.68 [0.63-0.7) had lower model performance. Based on AIC, addition of RFs to clinical variables alone improved model performance (p = 0.027), whereas adding hypoxia gene signature scores did not (p = 0.625). The selected features of the combined clinical-radiomics model included age, ISUP grade, tumour stage, and wavelet-derived grey level co-occurrence matrix (GLCM) RFs.
conclusionAdding pre-treatment prostate MRI-derived radiomic features to a clinical model improves accuracy of predicting BCRFS after prostate radiotherapy, however addition of hypoxia gene signatures does not improve model accuracy.
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